research paper on tsunami

REVIEW article

Probabilistic tsunami hazard and risk analysis: a review of research gaps.

• 1 Department of Mathematics/CEN, Universität Hamburg, Hamburg, Germany
• 2 NGI - Norwegian Geotechnical Institute, Oslo, Norway
• 3 University of Naples Federico II, Naples, Italy
• 4 Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
• 5 ERN Internacional, Mexico City, Mexico
• 6 Centre Internacional de Metodes Numerics en Enginyeria (CIMNE), Barcelona, Spain
• 7 University of Bergen, Bergen, Norway
• 8 Universite de Pau et des Pays de L’Adour, E2S UPPA, SIAME, France
• 9 IHCantabria - Instituto de Hidráulica Ambiental de La Universidad de Cantabria, Santander, Spain
• 10 Deutsches GeoForschungsZentrum GFZ, Potsdam, Germany
• 11 University College London, London, United Kingdom
• 12 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy
• 13 GEM Foundation, Pavia, Italy
• 14 Ifremer, Plouzané, France
• 15 Laboratoire Magmas and Volcans, Aubière, France
• 16 German Development Institute/Deutsches Institut für Entwicklungspolitik (DIE), Bonn, Germany
• 17 Indonesian Institute of Sciences (LIPI), Jakarta, Indonesia
• 18 University of Bristol, Bristol, United Kingdom
• 19 Alma Mater Studiorum - University of Bologna, Bologna, Italy
• 20 University of Hradec Kralove, Hradec Kralove, Czech Republic
• 21 Università Degli Studi Roma Tre, Rome, Italy
• 22 Cyprus University of Technology, Limassol, Cyprus
• 23 Geoscience Australia, Canberra, ACT, Australia
• 24 University College Dublin, Dublin, Ireland
• 25 Finnish Geospatial Research Institute (FGI), Masala, Finland
• 26 Department of Physics, University of Helsinki, Helsinki, Finland
• 27 Institute of Physics and Technology, Ural Federal University, Ekaterinburg, Russian Federation
• 28 Department of Engineering Sciences, Middle East Technical University, Ankara, Turkey
• 29 Facultad de Ciencias, Universidad de Málaga, Málaga, Spain
• 30 International Society for the Prevention and Mitigation of Natural Hazards, Athens, Greece
• 31 Cal Poly Pomona, Pomona, CA, United States
• 32 Geological Survey of Israel, Jerusalem, Israel
• 33 University of Zagreb, Zagreb, Croatia
• 34 British Geological Survey, Nottingham, United Kingdom
• 35 AECOM, Los Angeles, CA, United States
• 36 Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, Athens, Greece
• 37 Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität München, Munich, Germany
• 38 C.N.R. - Institute for Applied Mathematics and Information Technologies, Milano, Italy
• 39 Insight Research Centre, University College Cork, Cork, Ireland

Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach.

Introduction

Tsunamis are rare but potentially devastating natural hazards. With often limited available data, a coherent framework that incorporates data, physical assumptions (i.e., the general model of the system), and statistical methods for hazard and risk analysis is necessary to assess consequences affecting different layers of societies. To further develop, standardize and document such a framework is the underlying objective of COST Action AGITHAR (Accelerating Global Science in Tsunami Hazard and Risk Analysis; AGITHAR, 2020 ) and this article forms one outcome of the Action.

Probabilistic tsunami hazard and risk analyses (PTHA and PTRA, respectively) offer structured and rigorous procedures that allow for tracing and weighting the key elements in understanding the potential tsunami hazard and risk in globally distributed applications (e.g., Basili et al., 2021 ). Because of this, PTHA are becoming a standard basis for tsunami risk assessment around the world. Significant challenges in this analysis method are 1) the choice of hypothetical events and assigning “correct” probabilities, and ii) the impact of source regions distributed throughout an ocean basin and, conceivably, unifying distinct types of sources in a homogeneous probabilistic framework with a comprehensive treatment of uncertainty. The great importance of PTHA is due to its practical implications for society providing information for long-term planning and coastal management in areas where potential tsunamis may occur. Conversely, PTRA are still less abundant and standardized than PTHA, as elaborated in this review.

Few mega-tsunamis have been observed in the instrumental period, a timeframe spanning from approximately the 1960s to today. Thus, it is challenging to confidently assess the rate at which consequential tsunamis will occur. Predominantly seismically triggered tsunamis comprise about 80% of all tsunamis worldwide (e.g., Harbitz et al., 2014 ) with the remainder caused by landslides, volcanoes, or meteorological phenomena.

The sparsity of background data and requirements in engineering applications have driven the development of probabilistic methods for assessing tsunami hazard and risk aiming for unbiased comparisons of different hazards (natural and anthropogenic) as well as their uncertainty quantification. In recent years, the probabilistic framework has been increasingly applied. However, broadly accepted approaches are not yet defined, and potentially incompatible implementations of probabilistic methods are used in different regions across the world, and different tsunami source types are often treated separately and are rarely combined.

In this study, we have documented current gaps and open research questions related to PTHA and PTRA. We have organized this review into two main sections, one focused on tsunami hazard and the other on risk. We preface these topics with a brief introduction to the probabilistic framework underlying both PTHA and PTRA. Note that we grouped the gaps in numerical modeling in the hazard analysis related section, even though modeling may also be considered a cross-cutting topic. We believe, however, that the mentioned gaps are more related and addressed in a similar way as the other hazards related research gaps.

While PTHA and PTRA allow for including uncertainty in a consistent way, it is necessary to point out that it is not always simple to describe the knowledge gaps formally, for example through alternative models, and quantify their impact on hazard and risk models in terms of epistemic uncertainty (i.e., caused by lack of knowledge or data, Kiureghian and Ditlevsen, 2009 ). Overall, the research gaps identified in this study are “known unknowns” (e.g., Logan, 2009 ) and deserve more thorough research efforts, in order to determine their influence on the overall outcome of the PTHA or PTRA workflow.

This fact makes it hard to determine quantitatively the importance of each of the research gaps. Nevertheless, we tried to assess—in a qualitative way—the relative priority of research gaps and discuss this in the last section of this report.

Probabilistic Framework

In this section, we present a structure for probabilistic hazard and risk analyses. An overview is given in Figure 1 . More in-depth reviews of identified gaps related to the individual probabilistic framework components are discussed in sections “ Probabilistic Tsunami Hazard Analysis ” and “ Probabilistic Tsunami Risk Assessment ”.

FIGURE 1 . Roadmap of PTHA and PTRA frameworks: The entire process of risk evaluation needs to interact with the (risk-informed) decision-making process. Composite multi-dimensional risk and vulnerability indicators (“ Probabilistic Tsunami Hazard Analysis ” section) are shown as defining the context for the complex tsunami risk evaluation. The exposure modeling (“ Probabilistic Tsunami Risk Assessment ” section) defines groups of individuals and assets at risk. The horizontal flowchart at the bottom of the figure shows the PEER-like workflow for risk assessment. Probabilistic hazard analysis (“ Probabilistic Tsunami Hazard Analysis ” section) discusses estimation of the mean annual frequency (rate) of exceedance of a given value ( im ) of an intensity measure ( IM , Eq. 1 ) commonly visualized as a hazard curve. The IM can be a vector or a scalar that describes the intensity of a tsunami. Examples of IM’s are flow depth, maximum tsunami inundation height, etc. M refers in a generic manner to the size of various tsunami sources (e.g., earthquake magnitude, landslide volume). The tsunami sources, probability and modeling (earthquake, landslide, volcanic and meteotsunami) are discussed in “ Probabilistic Tsunami Hazard Analysis ” section. s denotes the vector of source parameters. N denotes the number of tsunamigenic sources. λ M min , i denotes the mean annual frequency of tsunamigenic events exceeding M min from source i . “ Gaps in Hydrodynamic Tsunami Modeling, Generation, Propagation, and Run-up ” section discusses hydrodynamic tsunami modeling, generation, propagation and run-up . The physical vulnerability (“ Gaps in Physical Vulnerability ” section) discusses the estimation of the probability distribution for a damage measure (DM, specific value dm ) given IM (specific value im ), known as the fragility function. The most common example of a DM is the physical damage state. The risk and resilience metrics section (“ Gaps in Risk and Resilience Metrics ” section) discusses the estimation of various decision variables (e.g., fatalities, repair costs, downtime) denoted as DV (specific value dv ). More specifically, it discusses the probability distribution for DV given DM also known as the consequence function. The vulnerability function ( Eq. 3 ) describes the (mean and standard deviation) of the probability distribution for DV given IM and is obtained by integrating over the entire domain of DM. One way to show the PTRA results is through visualizing the mean annual frequency of exceeding a specific value dv of DV (e.g., the loss exceedance curve (LEC) or the annual average loss (AAC)) shown in Eq. 2 , referred to generically as the risk curve.

The purpose of PTHA is to find the probability for a tsunami intensity measure ( IM ) to exceed a given threshold in a predefined time interval. Note that, in the PTHA framework, “Intensity Measure” is used with a meaning that differs from the “tsunami intensity scale” used, for instance, in tsunami catalogs to define the “size” of a tsunami or the effects it produces inland. In the PTHA context, an IM is a physical observable strictly connected to the physics of the process. Common IM s are wave amplitude, flow depth, current velocity, momentum flux, or maximum inundation height, depending on the problem setting ( Grezio et al., 2017 ).

Different probabilistic framework alternatives for computational PTRA exist. One option, rooted in seismic risk analysis, is performance-based risk assessment, presented by PEER (Pacific Earthquake Engineering Center) in 2000. The term performance-based is often used in contraposition to traditional prescriptive assessment procedures for seismic-resistant building design ( Fardis, 2009 ). The performance-based framework aims to provide a practical yet rigorous workflow and has also been used for risk assessment for hurricanes (e.g., van de Lindt and Dao, 2009 ; Barbato et al., 2013 ), floods ( De Risi et al., 2013 ; Jalayer et al., 2016 ), and tsunamis ( Chock et al., 2011 ; Chock, 2016 ; Attary et al., 2017 ). This framework can be organized in different modules; for example, hazard and vulnerability or hazard , fragility and consequence . Modules communicate with each other through intermediate variables and their conditional probabilities. Examples of intermediate variables are intensity measure ( IM ), damage measure ( DM ) and decision variable ( DV ). IM serves as an intermediate variable between hazard and vulnerability. DM connects vulnerability with fragility and describes physical damage. DV connects fragility with consequences and reaches out to decision-makers with numbers of casualties, repair costs, or downtimes. Interestingly, several risk-informed decision-making processes related to tsunamis are based on PTHA information only (e.g., hazard-based evacuation zones, hazard-based land-use planning). As an example, the criterion “flow depth ( IM ) larger than a threshold ( im )” can be used as a basis for decision-making (e.g., assigning evacuation zones). In other words, an IM can act as an intermediate variable (intensity measure) as well as a decision variable.

To illustrate the framework, suppose a finite set of N hypothetical tsunamigenic sources representing all possible tsunami events affecting the site of interest. Each event occurs randomly in time and independently of all others (i.e., as a Poisson process). The tsunami hazard curve–the main outcome of PTHA–describes the mean annual rate of a tsunami event affecting location x with an intensity measure IM(x) greater than some threshold im , denoted as λ( IM(x)≥im ). This can be expressed as:

where λ M min , i is the mean annual rate of occurrence of tsunamigenic events from source i (e.g., earthquakes, landslides, etc.) having magnitudes M exceeding M min , f M is the conditional probability density function for M ≥ M min ,i , and f S | M is the probability density function of the set of source parameters S given magnitude M . The aleatoric uncertainty associated with variable source characteristics can be represented by probabilistic prediction models of the source parameters. Finally, p ( IM ( x )≥ im| s ,m ) is the complementary cumulative distribution function of IM given S = s and M = m , and can be evaluated through tsunami simulations. Note that Eq. 1 can be used only if sources are independent; a counterexample being a landslide generated from the same earthquake that amplifies the ensuing tsunami’s destruction.

Epistemic uncertainty in PTHA is often accounted for using logic trees or, more recently, ensemble modeling, which allow alternative hypotheses for uncertain parameters, each of which is assigned a weight reflecting confidence in the respective parameter value (e.g., Geist and Parsons, 2006 ; Selva et al., 2016 ; Grezio et al., 2017 ). Equation 1 is computed for each logic tree ‘end branch’.

Building on tsunami hazard, the tsunami loss curve at any location is calculated by convolving vulnerability and hazard over the entire span of IM :

where λ ( D V ≥ d v ) is the mean annual rate of occurrence of DV larger than a threshold d v . Vulnerability is expressed through the complementary probability distribution function denoted as G D V | I M ( d v | i m ) , for DV given IM , and is itself calculated by integrating fragility and consequence functions (see also Figure 1 ):

• f D M | I M , the tsunami fragility function, predicts the probability of incurring a particular value ( dm ) of damage measure DM (e.g., damage states) for a given IM = im ;

• G D V | D M ( d v | d m ) , the tsunami consequence function (e.g., the damage-loss function), expressed as the complementary cumulative distribution function of DV given DM.

Strictly speaking, Eqs. 1 and 2 do not consider multi-hazard and multi-risk aspects such as cascading effects, combined damage due to tsunami loading and earthquake shaking. Assuming a Poisson process, the rate of exceedance λ is often transformed the first excursion of a specific value d v for a generic decision variable DV in the time Δ t (e.g., 1 year, 50 years):

Probabilistic Tsunami Hazard Analysis

This section discusses gaps in PTHA, focusing on those in tsunami sources and hydrodynamic modeling. Each subsection includes a summary of the present state-of-the-art, followed by an in-depth discussion of the gaps.

Gaps in Earthquake Source Representation

Existing methods.

Seminal Seismic PTHA (SPTHA) was performed using crude source and tsunami representations ( Lin and Tung, 1982 ; Rikitake and Aida, 1988 ; Tinti, 1991 ). Since then, the methodology has evolved dramatically ( Geist and Parsons., 2006 ; Annaka et al., 2007 ; Power et al., 2007 ; Thio et al., 2007 ; Burbidge et al., 2008 ; González et al., 2009 ; Sørensen et al., 2012 ; Hoechner et al., 2016 ; Miyashita et al., 2020 ), also in the framework of large programs (e.g., Horspool et al., 2014 ; Davies et al., 2018 ; Davies and Griffin, 2018 ; Basili et al., 2021 ).

SPTHA methodology for spatio-temporal and kinematic source treatment and the basic uncertainty framework were mostly transcribed from Probabilistic Seismic Hazard Analysis (PSHA, Esteva, 1967 ; Cornell, 1968 ; a historical perspective: McGuire, 2008 ). Due to tsunami data scarcity, it is challenging to derive hazard estimates directly from historical records ( Geist and Parsons, 2006 ; Grezio et al., 2017 ). Consequently, numerical modeling is a distinctive characteristic of SPTHA where seafloor displacement and tsunami evolution from generation to inundation are simulated for each scenario ( Geist and Parsons, 2006 ; Geist and Lynett, 2014 ). Source parameters can be inferred from past seismicity or from balancing the seismic moment across a fault zone, potentially constrained by geodetic strain rates ( Grezio et al., 2017 ). Often only major subduction zones are considered in SPTHA, assuming that they are the main hazard drivers (e.g., González et al., 2009 ; Davies et al., 2018 ). In this case, spatial characterization provides geometrical and kinematic constraints, such as the fault geometry, preferential slip direction, and other source zone properties. Crustal and general seismicity from unconstrained or unknown faults is treated with a larger uncertainty (e.g., Selva et al., 2016 ; Basili et al., 2021 ). Earthquakes are usually simplified to having either uniform (e.g., Horspool et al., 2014 ) or heterogeneous instantaneous slip (e.g., De Risi and Goda, 2017 ). Seafloor deformation is predominantly computed analytically assuming an elastic homogeneous half-space ( Mansinha and Smylie, 1971 ; Okada, 1992 ; Meade, 2007 ; Nikkhoo and Walter, 2015 ).

State-of-the-art seismic source representation for tsunami simulations is reviewed by Geist and Oglesby (2014) and Geist et al. (2019) . Additionally, we note some innovative efforts for complex, yet computationally affordable, approaches to source simulation ( Melgar et al., 2016 ; Murphy et al., 2016 ; Sepúlveda et al., 2017 ; Scala et al., 2020 ), and methods for handling source modeling uncertainties and sensitivity including temporal aspects and recurrence ( Grezio et al., 2010 ; Basili et al., 2013 ; Lorito et al., 2015 ; Selva et al., 2016 ; Lotto et al., 2017 ; Davies, 2019 ; Goda, 2019 ; Davies and Griffin, 2020 ).

Identified Gaps

Limited past events and data to inform hazard models (s1).

Completeness and quality of historical earthquake data, needed to constrain seismic source parameters, varies greatly depending on the history of the investigated geographical region ( Stucchi et al., 2004 ; Albini et al., 2014 ). Event catalogs are generally too short to account for the source frequency needed to model large average return periods in PTHA. The description of earthquake recurrence and of the tail of the frequency-magnitude distribution is highly uncertain ( Kagan, 2002 ; Geist and Parsons, 2014 ; Rong et al., 2014 ; Bommer and Stafford, 2016 ). In the attempt of constraining this uncertainty, seismic source parameters have been estimated globally using seismic or geodetic data or both (e.g., Bird and Kagan, 2004 ; Bird et al., 2015 ; Bird and Kreemer, 2015 ); however, these types of input data are not always considered by PTHAs. Moreover, a framework for constraining PTHA directly from tsunami observations exists ( Geist and Parsons, 2006 ; Grezio et al., 2017 ), while treatment of incomplete catalogs is described by Smit et al. (2017) . Where possible, other data types should also be considered. Paleo-seismic and paleo-tsunami catalogs may help constrain or validate at least large event recurrence (e.g., Priest et al., 2017 ; Paris et al., 2020 ), while GPS-constrained strain accumulation can indicate the total seismic moment rate (e.g., Hayes et al., 2018 ). Care should be taken of potential biases coming from overweighting evidence of large tsunamis in the past ( Geist and Parsons, 2006 ).

Fault Identification, Fault and Source Zone Parameterization and Tsunamigenic Potential Characterization (S2)

Tsunami sources are often constrained from infrequent offshore geologic studies investigating very large areas. Therefore, geologic fault data are often incomplete, causing a wide range of source knowledge levels ( Basili et al., 2013 ). Seismic source characterization for SPTHA generally refers to properties of pre-existing large faults, and often only to great subduction zone sources. All other–mostly crustal–faults are seldomly considered in PTHA, although non-subduction earthquakes may control tsunami hazard, especially when located near the target site ( Selva et al., 2016 ). Despite overall good constraint of subduction interface geometries (e.g., Hayes et al., 2018 ), along-strike trench segmentation and its impact on rupture propagation remains uncertain, limiting rupture forecasts and hindering estimates of subduction earthquake maximum magnitude (e.g., Bilek, 2010 ; Kopp, 2013 ; Grezio et al., 2017 ). Whenever fault knowledge is incomplete, more randomized “background” seismicity modeling is needed, with less predictable geometry and seismic behavior compared to subduction interfaces ( Sørensen et al., 2012 ; Selva et al., 2016 ). Fault slip rates can constrain seismicity recurrence parameters; these can vary both spatially ( Zechar and Frankel, 2009 ) and temporally (e.g., Ota and Yamaguchi, 2004 ; Ramírez-Herrera et al., 2011 ; Tiberti et al., 2014 ) but usually only averages are considered due to a lack of information. Kagan and Jackson (2014) pointed out that more research would be needed for focal mechanism forecasting; identifying the prevailing faulting mechanism is a critical task particularly in tectonically complex environments. This is expected, in turn, to exert a strong influence on tsunami hazard.

Variety, Complexity, and Dynamics of Fault Mechanics (S3)

Source simplification represents a dominant uncertainty in SPTHA ( Geist and Oglesby, 2014 ). Its effect on seafloor deformation needs to be investigated better, concerning deformation models that incorporate complex material properties, geometrical complexity, varying depth-dependent fault conditions, dynamic simulations including off-fault damage and near-surface amplification, which all may increase tsunami hazard ( Masterlark, 2003 ; Ma, 2012 ; Kozdon and Dunham, 2013 ; Ryan et al., 2015 ; Murphy et al., 2016 ; Lotto et al., 2017 ; Murphy et al., 2018 ; Scala et al., 2019 ; Scala et al., 2020 ; Tonini et al., 2020 ). Secondary ruptures including splay faulting may happen as an independent source or as part of a large earthquake on the subduction interface ( Wendt et al., 2009 ; Li et al., 2014 ; Hananto et al., 2020 ).

Tsunami earthquakes produce excessively large tsunami intensities compared to their moment magnitude ( Polet and Kanamori, 2016 ), and their global and local frequency is unconstrained. A simplified characterization of tsunami earthquakes , which is sometimes adopted, assumes larger slip associated with less rigid materials at shallow depths to preserve the seismic moment (e.g., Bilek and Lay, 1999 ; Geist and Bilek, 2001 ). These and other very complex ruptures, potentially containing fault branching, rupture jumping, and mixed-mode slip (e.g., Ulrich et al., 2019a ; Ulrich et al., 2019b ), are not well represented in PTHA. On a larger scale, rupture area may be shared by more than one subduction interface, like in the case of triple junctions (e.g., Solomon event 2007, Lorito et al., 2016 ). Due to a lack of observations the likelihood of such events is uncertain and quantification of their relative contribution to SPTHA therefore difficult.

Due to all these uncertainties and the extreme computational demand for dynamic computation, numerical simulations are de facto replaced with heterogeneous stochastic slip modeling (e.g., Herrero and Bernard, 1994 ; Mai and Beroza, 2002 ; Davies et al., 2015 ; Sepúlveda et al., 2017 ), and less frequently with stochastic stress modeling (e.g., Wendt et al., 2009 ). Because source observations are relatively scarce, more statistical tests ( Davies and Griffin, 2019 ) are needed for source model validation.

Empirical Scaling Relations (S4)

Several different empirical scaling relations are used to define earthquake rupture properties, such as length, width, average slip, and earthquake magnitude (e.g., Wells and Coppersmith, 1994 ; Murotani et al., 2008 ; Blaser et al., 2010 ; Strasser et al., 2010 ; Murotani et al., 2013 ; Goda et al., 2016 ; Skarlatoudis et al., 2016 ; Allen and Hayes, 2017 ; Thingbaijam et al., 2017 ). These relationships quantify appreciable uncertainties that are seldomly accounted for in SPTHA. These relations imply stress drop and time-dependent rupture characteristics and self-similarity of earthquakes across scales, but this is apparently violated in some cases. For example, the 2011 Tohoku earthquake released a huge amount of slip in a relatively small portion of the subduction interface compared to the Sumatra 2004 or Chile 1960 earthquakes ( Okal, 2015 ); scaling relations are not directly applicable to abnormally slow and unusually large shallow slip occurring in low-rigidity zones during tsunami earthquakes .

Complex, Non-stationary Seismic Cycle (S5)

Even in the simplest subduction environment, the seismic cycle over co-seismic, inter-seismic and post-seismic phases is complex and non-stationary, for example due to visco-elastic rheology and the role of fluids ( Wang et al., 2012 ; Moreno et al., 2014 ; Melnick et al., 2017 ). Time-dependent models could potentially be used to estimate the stress transfer from one earthquake to the neighboring faults ( King et al., 1994 ). Stress transfer from megathrust earthquakes triggering outer-rise ruptures or possibly even the opposite are such examples (e.g., Lorito et al., 2016 ). Based on seismic catalogs, it is possible to infer non-Poissonian earthquake recurrence, for example earthquake clustering ( Kagan and Jackson, 1991 ). A time-dependent model, which could better describe the probability of earthquake occurrence for some specific applications or timeframes, is taken into account by only a few PTHAs (e.g., Goda et al., 2017 ; Goda, 2020 ).

Other Constraints (S6)

It is reasonable to assume that high seismic coupling correlates with future slip location. Under simplifying assumptions, along-strike geodetic coupling variation can be inferred from geodetic strain ( Métois et al., 2012 ). Large uncertainty remains, particularly regarding the near-trench region ( Loveless and Meade, 2011 ). Recent developments in seafloor geodesy and modeling techniques are offering improved constraints (e.g., increasing offshore coupling resolution, Bürgmann and Chadwell, 2014 ; Foster et al., 2020 ), and slow slip events and consequently the stress evolution on the fault (e.g., Araki et al., 2017 ). High seismic coupling combined with stress accumulation in areas of seismic inactivity is described as a seismic gap. The possibility of using seismic gaps to identify zones of enhanced seismic hazard has long been debated (e.g., Bilek and Lay, 2018 ). Attempts to obtain physically motivated constraints on the maximum magnitude utilizing convergence rate, age of the oceanic crust and sediment thickness have been rather unsuccessful ( Okal, 2015 ). Ongoing research explores these and other controlling factors of subduction zone seismicity, including small- and large-scale roughness of the subduction interface, static friction coefficient, upper plate strain and rigidity, dip angle and curvature (e.g., Heuret et al., 2012 ; Bletery et al., 2016 ; Sallarès and Ranero, 2019 ; Rijsingen, et al., 2019 ; Muldashev and Sobolev, 2020 ). Additionally, rupture cycles and supercycles over multiple segments controlled by geological asperities have been proposed ( Philibosian and Meltzner, 2020 ). Similar to some of the previously discussed items in this section, no consensus has been reached on the statistical meaning of such information and on how to frame it within SPTHA.

Gaps in Landslide Source Representation

Landslide tsunami PTHA (LPTHA) was introduced less than a decade ago ( Geist and Lynett, 2014 ). Its application is often similar to SPTHA (e.g., ten Brink et al., 2006 ; Lane et al., 2016 ), but can also be based on geotechnical interpretations with a strong emphasis on expert judgment (e.g., Grilli et al., 2009 ; Hermanns et al., 2013 ; Løvholt et al., 2020 ). Salamon and Di Manna (2019) derive empirical scaling relations for landslides triggered by onshore earthquakes. In LPTHA, the landslide volume is used analogously to the seismic moment in SPTHA as a rate of occurrence. The slide volume is generally also the most influential factor on tsunami genesis ( Snelling et al., 2020 ). Landslide motion has a strong influence too ( Løvholt et al., 2015b ; Yavari-Ramshe and Ataie-Ashtiani, 2016 ). LPTHA source models are coupled to numerical tsunami models in Monte Carlo simulations. Methods for simulating both the landslide dynamics and tsunami generation range from block models ( Harbitz, 1992 ; Tinti et al., 1997 ; Watts, 2000 ; Grilli and Watts, 2005 ; Tinti et al., 2006 ; Løvholt et al., 2015b ), depth-averaged rheological models of viscoplastic or granular nature (e.g., Kelfoun and Druitt, 2005 ; Jop et al., 2006 ; Løvholt et al., 2017 ; Kim et al., 2019 ), to computational fluid dynamics (CFD) based approaches with different landslide complexity (e.g., Crosta et al., 2016 ; Abadie et al., 2020 ). Submarine landslide tsunamis are mainly characterized by the Froude number (landslide velocity over wave celerity) measuring the degree of critical landslide velocity, the landslide acceleration, and the rate of landslide mass mobilization (e.g., Ward, 2001 ; Løvholt et al., 2015b ). Subaerial landslides are characterized by the landslide frontal area, along with the Froude number, landslide density, and slope angle (e.g., Fritz et al., 2003 ; Heller and Hager, 2010 ; Bullard et al., 2019 ).

Lack of Understanding and Likelihoods for Tsunamigenic Landslide Volumes (L1)

For submarine landslides, we refer to the reviews of Huhn et al. (2019) and Harbitz et al. (2014) . The challenge can be attributed to several factors:

• Limited or insufficient mapping of past landslide occurrences. Their characteristics and lack of dating prevent constraining the age of the sediments without excessive uncertainty ranges (e.g., Geist et al., 2013 ). The new global landslide database initiative ( Clare et al., 2019 ) is a good starting point for standardizing, but not yet complete enough for feeding LPTHA. Good data coverage exists for certain regions such as the Mediterranean ( Urgeles and Camerlenghi, 2013 ), Gulf of Mexico ( Pampell-Manis et al., 2016 ) and the US East Coast ( Chaytor et al., 2009 , Geist et al., 2014 ).

• Limited understanding of how past landslide recurrence can be projected into the future hazard, including time and geological context dependency. For example, we cannot yet generally link climatically driven trends to past landslide frequency ( Urlaub et al., 2013 ). However, it is concluded that the last ice age affect present landslide probability offshore US ( Lee, 2009 ) and Norway ( Bryn et al., 2005 ).

• Limited available geological and geotechnical data inhibit identification of failure-prone sediments and discrimination from stable areas, including weak failure zones, pore pressure conditions or fractures, as well as obstacles or structures. When data exist, they may be proprietary, and a challenge is related to the need for covering very large geographical and heterogeneous regions. A methodological gap exists in bridging geotechnical data and slope stability models (e.g., Carlton et al., 2019 ) to volume-frequency relationships.

• Limited data and knowledge on triggers of landslides, such as meteorological or seismic events, impedes the quantitative assessment of potential landslide magnitude.

Difference of Onshore and Offshore Landslides (L2)

The specific character of subaerial and submarine landslides is often vastly different. Potential direct or indirect trigger mechanisms are sometimes not fully understood or difficult to embed into the probability of failure (e.g., precipitation-induced landslides, weak zones and fluid overpressure, range of failure propagation and cascading failure propagation spread). Understanding and estimating the annual probability of landslide failure in rock slopes with complex fracture patterns and stress conditions is associated with large uncertainty. Extensively monitored rock slopes in Norway (e.g., Blikra et al., 2005 ) show large motion over decades before failure takes place, rendering assessment of failure probability difficult. Matching expert judgment (e.g., Hermanns et al., 2013 ) to observed landslide magnitude frequency statistics (e.g., Nes, 2018 ) will help aggregate understanding of landslide frequencies and help link knowledge on failure-prone areas to probability. While epistemic uncertainties in the described situations are large, current LPTHA models do not incorporate them.

Limited Constraints on Landslide Dynamics and Material Behavior (L3)

The interplay of diverse tsunamigenic landslide parameters makes the generation complex, implying that much less voluminous landslides may be more effective tsunami generators than respectively larger ones. As an example, we note that the approximately 500 km 3 Trænadjupet Slide that occurred 4,500 years BP likely produced a moderate coastal impact possibly of just a few meters (e.g., Løvholt et al., 2017 ), while the 100 times less voluminous 1998 Papua New Guinea landslide induced more than 10 m run-up locally (e.g., Tappin et al., 2008 ). Because tsunami genesis is tightly linked to landslide acceleration as well as rate of mobilization of the landslide volume (e.g., Løvholt et al., 2005 ), quantifying the rate and nature of the slope failure is important. Just a few studies discuss the effect of initial failure rate on tsunami generation (e.g., Trapper et al., 2015 ; Germanovich et al., 2016 ; Puzrin et al., 2016 ) and related aspects such as remoulding and cascading failures on the landslide tsunami generation (e.g., Løvholt et al., 2017 ; Kim et al., 2019 ; Zengaffinen et al., 2020 ). How to include these factors and their associated probabilities in PTHA is not resolved. While advanced numerical models for landslide dynamics exist (e.g., Tinti et al., 1997 ; Jop et al., 2006 ; Savage et al., 2014 ; Si et al., 2018a ; Si et al., 2018b ; Kim et al., 2019 ; Wang et al., 2019 ; Gallotti and Tinti, 2020 ), their complexity and variety obfuscate understanding on which models are most suitable to be used. Furthermore, some models (e.g., Savage et al., 2014 ; Si et al., 2018a ; Si et al., 2018b ) are presently too comprehensive to be used in PTHA. Procedures for linking them to measured material properties and geological settings are not in place. Finally, fluid resistance forces (pressure drag, skin friction, and added mass) may be as important as the landslide properties, in particular for submarine landslides and further investigating physical understanding is necessary to constrain epistemic uncertainty.

Limited Availability of Benchmarks (L4)

Suitable benchmarks have recently been made available (e.g., Huang and Garcia, 1998 ; NTHMP, 2018 ; Kim et al., 2019 ), but are arguably less mature and fewer than their hydrodynamic modeling counterparts (e.g., Pedersen, 2008 ; Synolakis et al., 2008 ). A challenge is a transition from simplified laboratory tests to real-world landslide representation. Moreover, while numerous empirical lab experiments exist, they are significantly influenced by scale effects ( Heller, 2011 ). Neither complex rheological behavior nor real-world complexity is covered in the benchmarks. Complex laboratory experiments (e.g., Rondon et al., 2011 ) can be used for validating CFD models, but CFD models are presently too computationally expensive for tsunami hazard analysis modeling.

Limited Past Events to Inform Hazard Models (L5)

Information about past landslides and tsunamis can be used to infer landslide dynamics uncertainty. This can be done using landslide run-out information alone (e.g., Salmanidou et al., 2017 ), which consequently yields broad epistemic uncertainties in LPTHA. By using tsunami information, such uncertainties can be drastically reduced (e.g., Gylfadóttir et al., 2017 ; Kim et al., 2019 ; Løvholt et al., 2020 ). In practice, however, very few landslide tsunami data are available.

Gaps in Volcano Source Representation

Volcanic PTHA, coined VPTHA here, is even less developed than LPTHA ( Grezio et al., 2017 ). Among the few examples are the VPTHA framework developed in Ulvrova et al. (2016) and Paris et al. (2019) for underwater explosions at Campi Flegrei, and Grezio et al. (2020) for pyroclastic flows of Vesuvius. Given that risk reduction measures at volcanoes are often related to the identification of precursory patterns preceding eruptions or to recognizing unrest episodes with increased volcanic activity, the volcanic hazard is often computed conditional to eruptions or unrest, and without an explicit quantification of long-term probability. For example, in Paris et al. (2019) , the hazard analysis (Campi Flegrei, Naples, Italy) is confined to conditional tsunami intensity probabilities, due to probabilistic realizations of eruptions with different vent size and location.

Variety of Potential Volcanic Sources (V1)

Tsunamigenic volcanic events are diverse and they include both eruptive and non-eruptive triggering phenomena, such as underwater explosions, pyroclastic flows, lahars, slope failures, volcanic earthquakes, shock waves from large explosions, and caldera subsidence ( Latter, 1981 ; Kienle et al., 1987 ; Begét et al., 2005 ; Day, 2015 ; Paris, 2015 ; Grezio et al., 2017 ). A large range of wave characteristics is typical for volcano tsunamis, even if most such sources are localized and generate mainly short-period waves with greater dispersion and limited far-field effects compared to earthquake-generated tsunamis (e.g., Yokoyama, 1987 ; Nomanbhoy and Satake, 1995 ; Le Méhauté and Wang, 1996 ; Choi et al., 2003 ; Watts and Waythomas, 2003 ; Bellotti et al., 2009 ; Maeno and Imamura, 2011 ; Ulvrova et al., 2016 ; Selva et al., 2019 , 2020 ). However, tsunamis are among the farthest propagating volcanic perils, often generating regional impact (e.g., Krakatau, Stromboli, Ischia, etc., see for example Paris et al., 2014 ; Rosi et al., 2018 ; Selva et al., 2019 ; Gallotti et al., 2020 ). Notably, some of the tsunamigenic volcanic events overlap with those recorded for seismic and landslide tsunami: flank collapse, slope failure and even pyroclastic flows are related to landslides ( Løvholt et al., 2015b ; Paris, 2015 ); volcano-tectonic earthquakes occur with high frequency in volcanic areas ( Paris, 2015 ). Such frequency information as well as understanding material properties and transformation during flow should draw upon volcanological expertise. It is often difficult to define a single generation phenomenon since different potentially tsunamigenic processes can occur during the same volcanic episode, especially during large caldera-forming eruptions ( Paris, 2015 ).

Difficulties in Constraining Recurrence Rates (V2)

Since volcanic tsunami generation is so diverse, constraining recurrence rates for the different source types as eruptive phenomena ( Walter et al., 2019 ), unrest episodes ( Tinti et al., 1999 ; Selva et al., 2020 ), and triggered subaerial landslides ( Selva et al., 2019 ) is difficult. The integration into a multi-source VPTHA is further complicated by the need for accounting for the complex interdependencies that may exist among the different source mechanisms. The hazard is often nonstationary through time (e.g., Bebbinghton, 2008 ; Bebbinghton, 2010 ), which represents another challenge.

Gaps in Modeling Tsunami Generation and Propagation (V3)

Extensive reviews on existing strategies to model volcanic sources are found in Paris, (2015) , Grezio et al. (2017) and Paris et al. (2019) . Given the complexity, an important part of the hazard analysis is oriented toward understanding the physical mechanism of generation, and how to represent this probabilistically. Similar to landslide generated tsunamis, volcano tsunami modeling suffers from the difficulty of coupling the complex dynamics of the generating event and its interaction with wave propagation. For example, pyroclastic flows are complex, multi-phase phenomena involving the interaction of high-temperature gases and volcanic clasts covering a very large range of granulometric dimensions ( Freundt, 2003 ; Bougouin et al., 2020 ). This difficulty leads to simplified modeling schemes (e.g., Bevilacqua et al., 2017 ; Sandri et al., 2018 ). These simplified strategies may be too reduced for an effective constraint of their tsunami potential ( Grezio et al., 2020 ). Some phenomena may be represented by empirical models (for submarine explosions, see Paris et al., 2019 , and for caldera collapse, see Ulvrova et al., 2016 ). Experimental and numerical simulations coupled with field data increased understanding of the physics and main parameters of volcanic tsunamis ( Grezio et al., 2017 ).

Lack of Data From the Geological Record (V4)

Tsunami is often not dealt with in the volcanological community, although it may be more fatal than other volcanic hazards such as lava flows or ash falls ( Auker et al., 2013 ; Brown et al., 2017 ). Consequently, a systematic investigation of tsunami-related data in geological surveys at volcanoes is often missing. Because different volcanic phenomena may trigger tsunamis, even when tsunami data exist, attributing the observation to a specific mechanism is difficult (e.g., Krakatau 1883 eruption: Paris et al., 2014 ). Therefore, a systematic collection of available volcano-generated tsunami data and linking to potential volcanic generating processes is required. This will imply defining a strategy of tsunami-oriented monitoring around coastal volcanoes. It would be useful to combine such efforts with existing data collections such as the Global Volcanism Program ( Global Volcanism Program, 2013 ).

Limited Availability of Well Recorded Past Events or Benchmark Studies (V5)

Only a few past events are well constrained in terms of both the source and of the subsequent tsunami (e.g., Unzen 1792, Karymskoye Lake 1996; Montserrat 1997 and 2003, Anak Krakatau 2018; Stromboli 2002 and 2019). The lack of consensus in modeling procedures for each type of tsunamigenic volcanic event, along with the tendency to consider all sources as “unique”, complicates the task of defining benchmarks for volcano tsunamis.

Gaps in Meteorological Source Representation

Meteotsunami PTHA, coined MPTHA here, was developed only recently (see Grezio et al., 2017 ). A framework for MPTHA development is proposed by Geist et al. (2014) . The dynamics of meteotsunamis are fairly well-known (e.g., Monserrat et al., 2006 ; Sibley et al., 2020 ), related to unusually strong and rapid atmospheric pressure fluctuations and resonance effects causing strong waves closely associated with the behavior of tsunamis. The source mechanisms of meteotsunamis are also well understood ( Monserrat et al., 2006 ; Pattiaratchi and Wijeratne, 2015 ) with a major driver a Proudman resonance ( Proudman, 1929 ). Because meteotsunamis are strongly linked to (un)favorable combinations of pressure fluctuations, shallow (shelf) bathymetry, and directivity of the weather system, they take place more frequently in specific geographical areas, such as in the Adriatic Sea ( Vilibić and Šepić, 2009 ), the Baltic Sea ( Pellikka et al., 2020 ), and the East Coast of the United States ( Pasquet et al., 2013 ). The main input data for meteotsunamis include meteorological pressure data, preferably with full spatial and temporal characteristics of the pressure field for given meteorological events. Such data can be used to provide synthetic probabilistic source scenarios as input to an MPTHA, where an example for the Northeast US coastline is given by Geist et al. (2014) . While this field does not share the data sparsity issues that are associated with volcanoes and landslides, large uncertainties persist, as briefly discussed below.

Lack of Understanding the Potential and Likelihood for Tsunamigenic Meteorological Patterns (M1)

A systematic assessment of potential source areas and exposed coastal regions is not available. Some regional studies can serve as a preliminary indication (e.g., Dusek et al., 2019 ; Šepić et al., 2012 ; Šepić et al., 2016 ), but a rigorous catalog is missing. Climatological information is likely available, but a systematic extraction of data concerning meteotsunami potential has not been performed. It is not clear whether the resolution of available climatological data (e.g., from reanalysis) is sufficiently fine to allow for the extraction of corresponding relevant meteotsunami source patterns.

High Sensitivity to Several Parameters and Lack of Understanding of Local Amplification Factors (M2)

Whitmore and Knight (2014) demonstrate the high sensitivity of typical tsunami impact to source parameters and hence a large gap in knowledge on relevant localized parameters. The size, speed, amplitude, directivity, and duration of an atmospheric disturbance resonating with the water column in a specific topographic setting need to be known to assess the hazard. Therefore, such parameters need to be derived for all tsunamigenic regions, then applied to available climatological data sets, and finally fed into corresponding models for assessment of hazard. An assessment of amplifying tidal conditions in each of such regions is also missing.

Limited Availability of Benchmark Studies (M3)

While there are many individual meteotsunami events described in the literature (e.g., Churchill et al., 1995 ; González et al., 2001 ; Pasquet et al., 2013 ; Vilibić et al., 2014 ), no truly validated benchmark data are available for meteotsunami benchmarking. In principle, a similar methodology as described in Synolakis et al. (2008) could be followed. However, only very little unification of source modeling has been achieved and except for preliminary simplified tests (as in Vilibić, 2008 ), there exists no widely accepted test suite. This applies in particular to verification and validation of the probabilistic workflow of MPTHA.

Limited Past Events and Data to Inform Hazard Models (M4)

There is no consistent catalog of occurrences, although regional studies have been performed (e.g., Haslett et al., 2009 ; Woodruff et al., 2018 ). As stated before, there are no unified parameterizations of meteotsunami sources, which could be entered into such a catalog. Even though many individual events are described in the literature (see subsection above), these are by no means representative or complete to be used in hazard models. More rigorous collection of data with the special focus on meteotsunamis–background climatology, meteorological situation, ocean state, topo-bathymetry–for the diverse areas of interest would be desirable.

Gaps in Hydrodynamic Tsunami Modeling, Generation, Propagation, and Run-up

Hydrodynamic tsunami modeling includes numerical simulation of tsunami generation, propagation as well as coastal and onshore impact. It is an essential part of any PTHA or PTRA analysis. Reviews of commonly applied methods are available (e.g., Pedersen, 2008 ; Synolakis et al., 2008 ; Behrens and Dias, 2015 ). The pre-eminent challenge is the need to bridge a broad range of scales. First, in the probabilistic regime, a comprehensive PTRA must consider a very large number of scenarios to cover all relevant tsunamigenic sources, explore wave physics, and quantify uncertainties. Second, for each individual scenario source, large-scale propagation and coastal inundation modeling (optimally at scales of 1–10 m) need to be represented to quantify tsunami-related on-shore damages and losses. However, the fastest HPC simulation workflows (e.g., de la Asunción et al., 2013 ; Oishi et al., 2015 ; Macías et al., 2017 ; Musa et al., 2018 ) still require typically 10–60 min to simulate tsunami inundation at a scale of tens of meters, rendering them unsuitable for extensive PTRA studies with up to millions of scenarios ( Basili et al., 2021 ). To overcome this “challenge of scales”, modeling approximations are presently necessary for PTHA feasibility and can either involve 1) largely reducing the number of inundation scenarios (e.g., González et al., 2009 ; Lorito et al., 2015 ; Volpe et al., 2019 ; Williamson et al., 2020 ), 2) use of approximate models or statistics such as amplification factors (e.g., Løvholt et al., 2012 ; Kriebel et al., 2017 ; Gailler et al., 2018 ; Glimsdal et al., 2019 ), or 3) machine learning-based tsunami emulators (e.g., Sarri et al., 2012 ; Salmanidou et al., 2017 ; Giles et al., 2020 ).

PTHA Uncertainty Treatment for Tsunami Inundation Processes (H1)

At present, we lack well tested local PTHA benchmarks where the sources of uncertainties are effectively characterized, in a way that allows their formal propagation along the PTHA/PTRA assessment chain. Moreover, the effect of coseismic coastal displacement due to near field sources (e.g., Volpe et al., 2019 ), which affects tsunami inundation, should be investigated more deeply, especially when using techniques for reducing the number of scenarios. For this purpose, a large number of inundation scenarios are needed to quantify the epistemic uncertainty and bias caused by simplifications introduced through approximate methods. A local PTHA application using more than 40,000 earthquake sources ( Gibbons et al., 2020 ) is only a start.

Tsunami Generation (H2)

Unit source models ( Kajiura, 1963 ; Nosov and Kolesov, 2007 ; Molinari et al., 2016 ) of varying computational cost and complexity approximate the volumetric deep-water source displacements. While Lotto et al. (2019) clarified that the horizontal momentum does not effectively contribute to tsunami generation in deep-water sources, an extensive sensitivity analysis of how such simplifications affect PTHA has not been carried out. Incorporating time-dependent and moving sources, be it earthquakes (e.g., Ulrich et al., 2019a ), landslides (e.g., Løvholt et al., 2015b ) or volcanoes, will involve much higher computational burden. How to limit the number of source time steps for time-dependent source modeling is sparsely studied (e.g., Zengaffinen et al., 2020 ). For landslide tsunamis, closed-form models (e.g., Watts et al., 2003 ; Cecioni and Bellotti, 2010 ) represent a simple alternative but can introduce biases when conveyed to real geographical settings, due to oversimplification or inadequacy for the real situation. Subaerial landslides and volcanoes are often simplified because the required consideration of full 3D hydrodynamics (e.g., Abadie et al., 2020 ) into PTHA poses too high computational demand. Hence, more research is needed for developing simplified time-dependent sources compatible with PTHA demands, while quantifying the epistemic uncertainty and bias caused by the simplification. New methods may facilitate more detailed characterization of past inundation scenarios and their sources (e.g., Chagué-Goff et al., 2012 ; Sugawara et al., 2014 ; Paris et al., 2020 ).

Uncertainty and Variability due to Numerical Model for Tsunami Propagation (H3)

Most non-linear shallow water (NLSW) simulation codes produce similar results in the propagation phase, in particular in controlled benchmark cases (e.g., Synolakis et al., 2008 ). However, clear model differences can appear due to varying components (applied numerical method, workflow, sources, setup etc.) in practical applications. Comparing different numerical forecast models in the Indian Ocean, Greenslade et al. (2014) found large variations, attributed to differences in the workflow and source representation rather than to the tsunami model itself. Testing how such kinds of uncertainty quantification relate to “heterogeneous modeling practices” has not been carried out systematically. Moreover, a rigorous investigation of the performance of far-field propagation is sparse ( Dao and Tkalich, 2007 ; Davies and Griffin, 2020 ). Differences in numerical dissipation and discretization can also contribute to modeling deviations. As there is no standardized test case for far-field propagation that could reveal the differences in performance of different approaches, it is pressing to address these issues more systematically. Due to the computational burden, most PTHA applications today employ shallow water type models, neglecting frequency dispersion, which can lead to bias. Dispersion can be incorporated through conventional dispersive wave solvers (e.g., Bellotti et al., 2008 ; Løvholt et al., 2008 ; Kim et al., 2009 ; Shi et al., 2012 ), or through manipulating numerical schemes in NLSW codes (like MOST, e.g., Wei et al., 2008 ), although the general applicability of the latter is presently not clear. A systematic investigation quantifying the effect of dispersion (as in Glimsdal et al., 2013 ) on PTHA for practical source configurations would be desirable.

Nonlinearity and Resonances (H4)

Most tsunami simulations to date start from an ocean at rest and assume that interaction of currents with gravity waves is negligible. Androsov et al. (2011) demonstrated that significant alterations of the wave height can be attributed to tidal activity. A quantitative sensitivity analysis of this effect, its dependence on bathymetry, and its correlation to the choice of model (NLSW) is necessary. Huthnance (1975) described the phenomenon of trapped waves on continental shelves that may trigger edge waves and other amplified phenomena. Tsunami resonance effects in Chile and the Balearic Islands are studied in Aranguiz et al. (2019) and Vela et al. (2014) . Pattiaratchi and Wijeratne (2015) describe the effect of such phenomena as amplifying factors for meteotsunamis. It is currently unclear how such amplifying phenomena can be represented in the numerical model, nor if the strength is captured adequately.

Quantifying the Influence of Modeling Assumptions and Scaling (H5)

A hierarchy of modeling approaches, from shallow water assumption, over dispersive long wave solvers, to Navier Stokes type models, can be used to numerically treat tsunami hazard analysis in varying complexity. Due to ever-increasing computational resources, a trend toward more involved model equations can be observed. However, a clear quantitative assessment of the difference has only partly been performed. Lynett et al. (2017) use extensive benchmarking to study and compare modeling approaches to currents induced by tsunami waves. While this study is enlightening and provides very good benchmarking tools, further assessment is necessary to quantify the influence of higher fidelity modeling techniques. Generally, we note that current benchmarking (e.g., Synolakis et al., 2008 ) stays behind current high-fidelity modeling capabilities. Additionally, some benchmarks based on laboratory experiments have issues with scaling (see Heller, 2011 ; Pedersen et al., 2013 ), and related bias and accuracy have not been investigated systematically.

Modeling Situations With Complex Tsunami Inundation (H6)

NLSW models are predominantly used to simulate tsunami inundation. However, real inundation situations involve features too complex for NLSW approximate modeling, such as urban structures, or damage and erosion due to debris transport. At present, these topics are only partly represented, often using heuristic model formulations. Examples include spatially variable friction mapping (e.g., Gayer et al., 2010 ; Kaiser et al., 2011 ), or porous body equivalent friction models representing buildings (e.g., Yamashita et al., 2018 ). Bottom friction parameterization is almost insensitive for offshore modeling (see Arcos and LeVeque, 2015 ). However, variable bottom friction parameterizations may pose a viable tool for simulating detailed inundation, but large uncertainties still prevail (e.g., Griffin et al., 2015 ; Macías et al., 2020 ). While small scale laboratory tests exist ( Park et al., 2013 ), the heuristic nature of named models and the difficulty to perform controlled tests, implies potentially large epistemic uncertainties. Debris impact and transport are predominantly addressed through post-disaster surveys and experimental analysis of data so far (e.g., Nistor et al., 2017a ; Nistor et al., 2017b ; Stolle et al., 2019 ), and is mostly embedded in only vulnerability analysis (see below), and not in hydrodynamic modeling or PTHA to our knowledge. Extending the modeling dimensions and physical complexity is desirable (e.g., Marras and Mandli, 2021 ). Open and related to this issue is the influence and potential bias of the accuracy of topo-bathymetric grids, including filtering of structures and vegetation, on the accuracy of inundation simulations (see Griffin et al., 2015 ; Goda and Song, 2019 ). Unphysical bias can also be introduced when coupling high resolution (nested) models to large-scale propagation models as shown in Harig et al. (2008) .

Probabilistic Tsunami Risk Assessment

This section discusses identified gaps in PTRA. We go through current state for exposure modeling, physical vulnerability, and risk and resilience metrics, as they naturally follow each other in a consequence-based risk workflow ( Figure 1 ). Methods characterizing the complex social, organizational, and economic context in a tsunami risk assessment are discussed subsequently.

Gaps in Exposure Modeling

Exposure data provide information about the characteristics and location of people and assets at risk. There are several techniques for the acquisition of exposure data, with different degrees of resolution and precision ( Pittore et al., 2017 ). Data from governmental agencies are most commonly used, as they are open and available in most developed countries. These data often provide coverage for the entire building inventory (e.g., physical assets) and are regularly updated for asset management (e.g., national technical maps) and fiscal reasons (e.g., cadastral data). Different exposure databases exist. The Global Exposure Database—GED ( De Bono and Mora, 2014 ; De Bono and Chatenoux, 2015 ) developed for GAR13 and updated later for GAR15 ( UNISDR, 2013 ; 2015 ) provides a global dataset at 5 km grid resolution at inland and 1 km at coastal locations, including data for buildings, their use, and exposed value. The 2013 and 2015 versions of the GED served as the exposure databases for the global risk model by the United Nations Office for Disaster Risk Reduction, which considered earthquakes, hurricanes, tsunamis and riverine floods as hazards. The DRMKC Risk Data Hub WebGIS tool ( Antofie et al., 2019 ) has been developed to provide access and sharing of EU-wide information relevant for disaster risk management. Initiatives such as the Open Exposure Data (OED) with roots in proprietary catastrophe modeling and reinsurance industry, provide the opportunity to generate exposure data, including those relevant to tsunami risk, with interoperability between different modeling tools. These databases mainly contain data from census or remote sensing. A recent interview-based approach, relying on local practicing engineers with knowledge of building features, has been adopted for the compilation of building inventories at regional scales ( Polese et al., 2020 ). Careful validation needs to address possible heterogeneity in data. At present, the only guidelines and tools that exist for capturing and classifying exposure data for a tsunami are the multi-hazard exposure taxonomy, and associated tools, provided by GED4ALL ( Silva et al., 2018b ).

Lack of Detail (E1)

Most available exposure data have not been collected for the purpose of tsunami risk assessment and may be missing important information for modeling tsunami fragility or vulnerability. For instance, population cadastral data are often collected at the municipal, district or residential unit level, requiring extra assumptions to determine the geographical distribution. Tsunami hazard intensities can vary considerably between two nearby locations. Accurate geo-localization of the exposed assets and people is needed to obtain robust results, necessitating a minimum resolution level for the exposure databases. While main building construction characteristics are often known, tsunami relevant features (e.g., building lateral load resistance, foundation) are missing ( Rivera et al., 2020 ). Exposure data for critical structures and infrastructure should include functionality information for the exposed asset. This would allow for proper modeling and hence assessment of community resilience, considering different services such as healthcare and education. In other cases, data gaps and uncertainties are associated with regulatory and privacy limitations or outdated sources.

Lack of Exposure Data (E2)

In many developing countries, where cities have rapid urbanization processes and long-term planning is not consistently enforced, exposure data are not always available or updated. Such data may be inferred from satellite and aerial imagery, from freeware data made available from international projects (e.g., NASA’s EOSDIS), from volunteered geographic information systems (e.g., Huyck et al., 2011 ; Huyck and Eguchi, 2017 ; OpenStreetMap, 2020 ), or through intergovernmental organizations (e.g., JRC Risk Data Hub, 2020 ).

Lack of Tsunami Exposure Model and Taxonomy (E3)

Significant efforts have been made in the earthquake risk community to define a common exposure taxonomy (e.g., GED4GEM, Silva et al., 2018a ; METEOR, Huyck et al., 2019 ). However, these taxonomies do not contain all the required structural attributes for estimating tsunami risk such as geomorphological, land use, and land cover datasets, or number and size of openings in buildings. A recent development is GED4ALL, a multi-hazard taxonomy ( Silva et al., 2018b ), which considers tsunami as a hazard. GED4ALL also discusses multiple asset types like buildings, people, infrastructure systems and crops. Common taxonomy and attributes are fundamental to avoid heterogeneity, especially when considering multiple asset types.

Spatio-Temporal Variability (E4)

Most exposure models are static in time and do not consider the spatio-temporal variability of exposure components. This aspect is critical when modeling human exposure since there can be daily and seasonal variations. For example, coastal regions often attract tourists, visitors and seasonal workers, leading to significant seasonal fluctuations in the population ( Fraser et al., 2014 ). Spatio-temporal variation in exposure heavily influences the tsunami risk.

Gaps in Physical Vulnerability

As tsunami losses are closely connected to damages to buildings and infrastructure, the vulnerability component is often cut into two parts: a tsunami-to-damage fragility function, and a damage-to-loss consequence function ( Figure 1 ). Advancements in tsunami vulnerability models have significantly lagged behind those of tsunami hazard, with almost no studies found to precede the 2004 Indian Ocean Tsunami ( Charvet et al., 2017 ). However, with the recent devastating tsunamis providing a large quantity of observed damage and loss data to develop and validate fragility and vulnerability models, this field of study has rapidly grown. Several empirical fragility functions for the assessment of buildings ( Koshimura et al., 2009 ; Mas et al., 2012 ; Suppasri et al., 2014 ; Charvet et al., 2015 ; Chock et al., 2016 ) and infrastructure ( Eguchi et al., 2014 ; Hatayama, 2014 ) have been derived from observed damage in the 2004 Indian Ocean, 2009 Samoa, 2010 Chile, and 2011 Tohoku tsunamis. Recently, analytical fragility functions were derived from numerical simulations of building response under tsunami inundation ( Petrone et al., 2017 ; Alam et al., 2018 ; Karafagka et al., 2018 ; Páez-Ramírez et al., 2020 ), and under sequential earthquake and tsunami impact ( Park et al., 2012 ; Attary et al., 2019 ; Petrone et al., 2020 ). Only a few studies exist that move from fragility to vulnerability modeling ( De Risi et al., 2017 ). There is a lack of consensus on many aspects of physical fragility and vulnerability modeling.

Limitation in Asset Types and Geographical Scope (P1)

The vast majority of existing tsunami fragility and vulnerability models relate to buildings, few exist for bridges, fuel tanks, or other types of infrastructure. The main reason is that most fragility functions are empirical, and few observational damage or loss data are available for infrastructure components. Even for buildings, the geographical scope of existing vulnerability and fragility models is limited. Most empirical fragility functions are based on data from the 2004 Indian Ocean and 2011 Tohoku events, and hence represent non-engineered buildings in countries surrounding the Indian Ocean and engineered buildings typical of Japan. With analytical fragility functions only covering a small number of building types, large portions of the world’s exposure remain unrepresented by current studies.

Effect of Multiple Hazard on Empirical Tsunami Fragility Mode (P2)

Tsunamis are commonly triggered by large earthquakes. Near-source, observational data on asset damage and loss collected after the tsunami often include the combined effects of earthquake ground shaking and tsunami inundation. Hence, empirical fragility and vulnerability models derived from such data inherently comprise the effects of both hazards. Therefore, corresponding empirical fragility models may be regarded as inappropriate for use in a tsunami-only risk assessment. Pure tsunami damage data is rare and currently limited to non-engineered structures ( Charvet et al., 2017 ).

Lack of Consensus Regarding Best Tsunami Intensity Measure (P3)

The intensity measure IM ( Figure 1 ) links the hazard and vulnerability components within risk models. Traditionally, tsunami inundation maps are presented in terms of inundation depth. While the majority of fragility and vulnerability models adopt inundation depth as IM , other tsunami IM have also been used such as the flow velocity or momentum flux. The absence of inundation velocity measurements in field data requires running tsunami inundation simulations to use such IM ( Koshimura et al., 2009 ; Song et al., 2017 ). More recently, force-based IM (e.g., flow velocity, momentum flux) were used in fragility functions for engineered buildings yielding better correlation to observed damage than inundation depth ( Macabuag et al., 2016 ). However, no consensus on the most appropriate IM could be reached. As a consequence, mismatches between representations of hazard and vulnerability in risk modeling may exist.

Gaps in Building Analysis and Assessment for Use in Analytical Tsunami Fragility (P4)

Buildings are often used as vertical evacuation shelters and an assessment of their structural fragility is therefore an important information in the risk assessment workflow. Tsunami engineering being a younger discipline than earthquake engineering has adopted approaches from the latter community. This was supported by the physical similarity of both hazards applying predominantly horizontal loads to structures. However, there are fundamental differences in how earthquake and tsunami loads are applied to buildings. For example, tsunami loads affect the lower floors of a high-rise building, whereas seismic loads are inertial forces usually causing increasing magnitude for higher floors ( Baiguera et al., 2019 ). Thus, earthquakes induce large bending moments in structural elements, whereas tsunamis typically induce large shear. Since typical structural modeling approaches tend to prioritize flexural effects, the bias in tsunami fragility assessment may be large. Furthermore, seismic loads are dynamic, whereas loads from tsunami inundation can be considered quasi-static, and Rossetto et al. (2018) have shown that building ductility is often not crucial in the tsunami response of structures. Although no consensus has been reached in this regard, more fragility functions based on static rather than time-dependent non-linear approaches are derived now ( Petrone et al., 2017 ; Rossetto et al., 2019 ). As a tsunami applies direct pressures to a structure, non-structural components like infill walls (and their openings) are seen to play an important role in determining tsunami forces ( Del Zoppo et al., 2021 ). Furthermore, buoyancy, foundation scour and debris impact, which significantly affect building damage from tsunami inundation are rarely modeled ( Del Zoppo et al., 2019 ). These effects are still to be investigated; therefore, published analytical tsunami fragility functions are subjected to large modeling uncertainties. Progress towards more comprehensive and reliable analytical fragility and vulnerability models is needed.

Gaps in Risk and Resilience Metrics

Tsunami risk assessments typically reflect the impact on the exposed population and infrastructure. The most commonly used decision variables (or metrics) are the number of fatalities, injuries, affected people, besides the direct and indirect economic losses. Direct economic losses represent the repairing/replacement costs of damaged assets, whereas indirect losses reflect costs as down-time, partial loss of functionality of buildings and infrastructure, loss or reduction in network connectivity, flow and/or capacity. These metrics can be used in alternative approaches such as worst-case scenarios, scenario-based for a prescribed return-period, and fully probabilistic. A review of early methods for tsunami risk assessment can be found in Jelínek and Krausmann (2008) .

Fully probabilistic risk assessments require the integration of hazard estimates (PTHA) with vulnerability functions (see Figure 1 , Løvholt et al., 2015a ; 2019 ). Since the results of PTHA are not always available, tsunami risk assessments are often performed considering selected (worst-case) scenarios as hazard input (e.g., Triantafyllou et al., 2019 ), which sometimes represent past disasters (e.g., Daniell et al., 2017 ). Having the results of PTHA available, tsunami risk assessment can also be performed for a limited number of scenarios (e.g., Nadim and Glade, 2006 ; Okumura et al., 2017 ). When the PTHA results are available in the form of stochastic event sets, a fully probabilistic tsunami risk assessment (PTRA) can be performed ( Ordaz, 2000 ; Strunz et al., 2011 ; Salgado-Gálvez et al., 2014 ), although these types of analyses usually demand an extensive computational effort (e.g., Løvholt et al., 2015a ; Jaimes et al., 2016 ; Goda and Song, 2019 ; Ordaz et al., 2019 ).

In a fully probabilistic tsunami risk assessment workflow, risk results are obtained in terms of exceedance frequencies for the above-mentioned metrics ( Figure 1 ). For instance, loss exceedance curves (LEC) provide the relationship between loss values and their exceedance frequencies ( Løvholt et al., 2015a ; Jaimes et al., 2016 ; Attary et al., 2017 ; Ordaz et al., 2019 ). The area under the LEC corresponds to the average annual loss (AAL), a metric that provides a long-term overview of risk and accounts for the contribution of large and infrequent events as well as small and more frequent ones. From the LEC, loss values associated with a given return period can be obtained, such as loss values estimated by Løvholt et al. (2015a) at a global level representing direct losses. The Hazus tsunami loss estimation methodology provides state-of-the-art decision-support software for estimating potential losses from tsunami events ( FEMA, 2017a ; FEMA, 2017b ).

Risk assessment is not necessarily limited to quantifying the direct and indirect impact on exposed populations and infrastructures. The evaluation of safety and reliability of physical systems is of interest too and for this, fragility functions (“ Gaps in Physical Vulnerability ” section) can be integrated with hazard to obtain the frequency of exceeding a given damage level (see Figure 1 , e.g., Park et al., 2019 ; Fukutani et al., 2019 ). The risk metrics provide valuable data also for the assessment of quantitative resilience (also denoted as engineering resilience), which aims to estimate the resilience of a network, an infrastructure, or even an urban ecosystem to a specific natural hazard (see Mebarki et al., 2016 for industrial plants, Akiyama et al., 2020 for bridges). Quantitative resilience should not be confused with coastal community resilience which is discussed in detail in the following section.

Gaps Related to Characterization and Propagation of Uncertainties (R1)

Most existing PTRA models rely on a homogeneous Poisson process as the probabilistic backbone for the occurrence process ( Eq. 4 ). The Poisson model, strictly speaking, should be used for propagating only those uncertainty sources that renew with the occurrence of each new event ( Kiureghian, 2005 ). This means that propagation of other sources of uncertainties in a PTRA framework (i.e., those that lack renewal properties), such as the uncertainties in modeling, analysis method, and in general epistemic uncertainties, need more research ( Goda and De Risi, 2018 ; Goda, 2020 ). One possible direction could point to Bayesian methods ( Jalayer and Ebrahimian, 2020 ).

Challenges in Characterizing Vulnerability Functions (R2)

PTRA lacks a clear distinction and definition of the different loss components that are quantified through the vulnerability functions. On the one hand, direct economic losses can be estimated with a good degree of confidence using existing methodologies ( Pagnoni et al., 2019 ). Long-term direct (e.g., cost of maintenance) and indirect losses (e.g., down-time and reduced functionality including business interruption) typically represent a significant component of the total economic loss (direct + indirect) yet require better quantitative approaches.

Lack of a Tsunami Consequences Database (R3)

There is a lack of tsunami-specific consequence databases accounting for casualties and losses ( Yamao et al., 2015 ). These types of databases exist for disasters in general (e.g., EM-DAT) and more specifically for earthquakes ( So et al., 2012 ; Cardona et al., 2018 ). They are useful not only to keep a consistent record of past events and the affected regions but to disaggregate the impacts of large events in terms of losses (direct and indirect) and casualties (fatalities and injured), besides assessing the consequences in particular sectors (e.g., road networks, heritage sites, etc.) at different resolution levels. The information included in the consequences databases provides valuable data to validate and calibrate different components of the models (e.g., fragility curves, vulnerability functions). Some data can be partially acquired from collections of documented eyewitness accounts ( Santos and Koshimura, 2015 ), or other sources (e.g., ITIC, 2020 ).

General Lack of Risk Studies for Networks and Lifelines (R4)

Current implementations of PTRA are mainly focused on residential buildings and emergency planning activities such as the definition of evacuation routes. However, the resilience of coastal areas relies on conventional and strategic infrastructures ( Akiyama et al., 2013 ; Pitilakis et al., 2019 ). Conventional infrastructure such as roads, bridges, power, water, sanitation and communication networks, underpin economic and social activities in most urban areas ( Salgado-Gálvez et al., 2018 ). Schools and hospitals support the provision of education and health services, which are essential to recovery. Critical infrastructures in coastal areas include harbors (nuclear) power plants, gas and oil storage, and early warning infrastructure, such as tidal buoys and offshore bottom pressure gauges ( De Risi et al., 2018 ). Such infrastructures are complex, often interconnected and geographically distributed systems involving multiple sectors ( Duenas-Osorio and Vemuru, 2009 ; Argyroudis et al., 2019 ), where further research is needed to quantify their resilience to tsunamis.

Assessing Tsunami Risk in a Multi-Hazard and Multi-Risk Framework (R5)

As triggered events, tsunamis fit naturally within a multi-hazard framework. Moreover, there can be several cascading consequences associated with the occurrence of tsunamis, such as technological disasters induced by natural hazards known as NATECH risks (e.g., the Fukushima Disaster), disruption to supply chains, and societal impacts. Therefore, management and decision-making for tsunami risk should be framed in a multi-risk context. To be able to make risk-informed decisions considering tsunamis, it is important to model the interaction of tsunamis with other phenomena at the level of hazards, vulnerabilities, and socio-economic consequences. An important gap related to risk assessment for tsunamis (and in general) is the lack of a streamlined and standard workflow for modeling the multi-hazard and multi-risk aspects. Currently, most studies consider the different hazards to be independent or “simultaneous” (e.g., earthquake and tsunami as independent events); whereas, few works consider interacting hazards such as coupled simulation of tsunami and earthquake ( De Risi and Goda, 2016 ; Goda et al., 2017 ; Goda and De Risi, 2018 ; Ordaz et al., 2019 ; Park et al., 2019 ), the cumulation of tsunami and earthquake damages and losses ( Ordaz, 2015 ; Attary et al., 2019 ; Park et al., 2019 ; Petrone et al., 2020 ), and interaction of tsunami and aging infrastructure ( Akiyama et al., 2020 ).

Lack of Understanding and Quantification of Mortality (R6)

Strikingly, the 2004 tsunami with more than 226,000 dead and missing people ( EM-DAT, 2020 ) caused an order of magnitude higher fatalities than the 2011 Tohoku tsunami with 19,846 ( EM-DAT, 2020 ). Hence, past major disasters indicate that the vulnerability to tsunami mortality of a population is much more sensitive to demographic factors ( Løvholt et al., 2014 ) than to physical vulnerabilities (“ Gaps in Physical Vulnerability ” section). Correlations of tsunami flow depth and number of fatalities following the 2004 Indian Ocean, 2006 Java and 2011 Tohoku tsunamis reveal much larger scatter than those observed in physical vulnerability functions, even when derived from the same events ( Reese et al., 2007 ; Koshimura et al., 2009 ; Suppasri et al., 2016 ). As human behavior influences mortality strongly ( Johnston et al., 2016 ; Blake et al., 2018 ), deriving simplified vulnerability charts based on single tsunami intensity measures may not be appropriate. Tsunami awareness and availability of tsunami early warning systems and infrastructure are important ( Gregg et al., 2006 ; Fraser et al., 2014 ), as well as proximity to source areas. Our understanding and ability to quantify and assess the effect of all these factors on tsunami mortality is still very limited.

The Weakness of Capturing Multi-Faceted Aspects of Vulnerability (R7)

Quantitative risk assessments typically address several socio-economic parameters (e.g., safety, downtime, direct and indirect economic losses, and even human behavior and response) as dimensions of consequences to disruptive tsunami events. However, PTRA falls short in modeling some dimensions of vulnerability that are part of a given context and not directly caused by a disruptive event (e.g., governance-related issues, adaptation and coping capacities, societal inequalities). There are no established methodologies, within the context of the PTRA framework ( Equations 1 – 4 ), for characterizing context-based impacts of tsunami on the social, political and economic dimensions, leaving it unclear how to address these dimensions. Integrated and heuristic approaches such as "MOVE" ( Birkmann et al., 2013 ) or holistic approaches as those proposed by Carreño et al. (2007) or Aguirre-Ayerbe et al. (2018) , have strived to address the context-based and multi-dimensional nature of vulnerability and risk and could be adapted to be used as physical risk indicators in the outcomes of PTRA.

Gaps in Social Vulnerability, Multi-Dimensional Vulnerability and Risk Indicators

Although not directly addressing tsunami risk, Jasanoff (1993) pointed out the urge to bridge the two cultures of quantitative and qualitative risk assessment, stressing the importance to view risk in a larger context of social justice ( who should we protect, from which harm, at what cost, and by foregoing what other opportunities). The societal factors impacting vulnerability and risk are mainly rooted in a complex and diverse aggregate, which varies over time and space. Qualitative vulnerability investigations use models and frameworks considering several dimensions (e.g., economic, demographic, psychological, political or physical), summarized by composite vulnerability and risk indices. These indicators can be distinguished from the risk and resilience metrics discussed in the previous section (“Gaps in Risk and Resilience Metrics” section) since some of them cannot be directly integrated into a computational PTRA procedure. Examples of existing multi-dimensional vulnerability and risk indicators are: The community resilience (e.g., Lam et al., 2016 ; Saja et al., 2019 ), the urban disaster risk index ( Carreño et al., 2007 ; Salgado-Gálvez et al., 2016 ), the social vulnerability index ( Cutter et al., 2003 ; Flanagan et al., 2011 ), the Coastal vulnerability index ( McLaughlin and Cooper, 2010 ), Metropolitan Tsunami Human Vulnerability Assessment ( Tufekci et al., 2018 ).

The Difficulty of “Quantifying” Social Vulnerability (I1)

Social vulnerability describes combinations of social, cultural, economic, political, and institutional processes that determine differentials in the experience of hazards and recovery from dangerous events ( Spielman et al., 2020 ). Experts may construct meaningful indicators to include a social component in hazard planning, preparation, and response. Integrating social vulnerability research into emergency and disaster risk management is essential, but caution is required to assign quantitative elements. Integration of social factors may allow planners and decision-makers to better identify problems in case of destructive events and provide insights into addressing recovery solutions ( Cardona, 2001 ; Chakraborty et al., 2005 ; Schmidtlein et al., 2008 ). Social Vulnerability Index (SoVI) is a single quantitative indicator which was developed through a review of hazard case studies by Cutter et al. (2003) examining the spatial patterns of social vulnerability to natural hazards at the county level in the United States. Because of the complex and multidimensional nature of factors contributing to vulnerability, no variable has yet been identified to fully validate SoVI. An alternative approach to assess its reliability is to identify how the changes in the SoVI algorithm construction may lead to the changes in the outcome. Schmidtlein et al. (2008) investigated the sensitivity of quantitative features of the SoVI such as the scale of application, the set of used variables, and various geographic contexts.

Ambiguities in Definition of Community Resilience (I2)

Resilience is a frequently used term to discuss the capacity of a society or ecosystem to recover quickly from a disaster. The United Nations Office for Disaster Risk Reduction has defined resilience as “ the capacity of a system, community or society potentially exposed to hazards to adapt, by resisting or changing in order to reach and maintain an acceptable level of functioning and structure. This is determined by the degree to which the social system is capable of organizing itself to increase this capacity for learning from past disasters for better future protection and to improve risk reduction measures ” ( UNISDR, 2007 ). A comprehensive review of various definitions of resilience can be found in Davoudi et al. (2012) and Ayyub (2014) . The definition of coastal resilience is hindered by varying definitions and non-unified terminology, difficulties in selecting and combining different resilience indicators, and lack of data for validation ( Lam et al., 2016 ). In fact, resilience is still lacking rigorous measurement methods ( Bozza et al., 2015 ), especially in the context of tsunami hazard ( Genadt, 2019 ).

Lack of Tsunami Vulnerability Index (I3)

A specific tsunami Disaster Risk Index (TDRI), similarly to the Disaster Risk Index (DRI) developed by the UN Development Program to compare disaster risk between countries exposed to hazards (UNDP, 2004) or the Urban Seismic Risk Index by Carreño et al. (2007) should be developed.

Integrated Approaches to Consider the Multi-Dimensional Aspects of Tsunami Risk (I4)

Vulnerability and risk are multi-faceted concepts and encompass various assets, physical, organizational, and institutional dimensions (e.g., Herslund et al., 2016 ). Vulnerability and risk assessment considering these different facets often requires different scientific backgrounds and approaches ( Hufschmidt et al., 2005 ). A consequence-based approach to risk assessment (e.g., the PEER framework, or computational PTRA in general) has its roots in engineering. The approach follows a logical flow from causes associated with a disruptive event toward quantifying its direct and indirect socio-economic consequences. This approach focuses on the physical dimension of vulnerability, acting as a “container” of functions and services and thereby invokes–directly or indirectly–other dimensions of vulnerability such as social, economic and organizational vulnerability. On the other hand, the context-based approach (e.g., approaches based on integrated indicators) has its roots in the humanities and social disciplines. This approach deals with the context and the interactions between different actors, the respective territory, the different drivers (climate, societal changes) and how decisions can affect the overall context and the complex interplay between actors and drivers. Needless to say, the two approaches complement each other and have to be taken into account in policymaking for DRR in an integrated manner ( O’Brien et al., 2007 ).

Considering Community Response and Organizational Capacities (I5)

Recent tsunami events worldwide have highlighted the need to critically revisit how human behavior in tsunami evacuation, and more generally, the human dimension of preparedness for tsunamis is addressed within the risk assessments. The lessons from Japan 2011, Chile 2010 and Indonesia in 2010 and 2018 events highlight such needs. Questions arise on how and if the different and seemingly inconsistent human behavior can be addressed in tsunami risk assessments. Moreover, atypical events such as the Krakatoa, Indonesia 2018, do not allow for conventional prevention, warning and mitigation strategies. In most cases, aid and help arrive late due to limited organizational capacities, leaving the affected communities in even more vulnerable conditions, especially during the first critical hours and days after the event. Events with growing levels of complexity are likely to continue to occur in the future and this calls for a more in-depth consideration of how different communities respond and how those variations can be integrated within the risk assessment framework.

Incorporating Risk Perception in the Formulation and Analysis of Complex Risks (I6)

Perceptions are dynamic and socially constructed. Perceptions can change abruptly or gradually, depending on the context. Understanding evacuation behavior requires an understanding of risk perceptions. This can help explain why the response to tsunami drills may be different than when responding to a real event. It is quite challenging for risk methodologies to consider the dynamic, complex and subjective aspects of risk perception. Only by understanding the subjective meanings of perceived risks allows risk communication to be designed and applied more effectively.

Conclusions and Directions

In this review, we discuss a large number of research gaps in PTHA and PTRA. It becomes obvious that methods have substantially improved over the past decades, but also that open questions remain in the physical description, conceptualization, modeling, as well as the social and psychological dimensions of the topic.

The physics and geological complexity of tsunamigenic sources are still not captured nor understood adequately, leading to large uncertainties. For SPTHA, neither all earthquake faults nor their exact location, geometry, boundary and initial conditions (e.g., stress, friction) are fully constrained. Statistical models of recurrence constitute the largest uncertainties in large and rare events, including tsunami earthquakes. Uncertainty may become excessive for landslide tsunamis, where statistics on past events often are absent, and our understanding of slope failure probability is limited. The need for covering vast geographical scales, source diversity and related uncertainties, render LPTHA extremely challenging. For VPTHA additional difficulties arise due to the complexity of tsunamigenic volcano sources and triggers, but they are constrained spatially. MPTHA may benefit from a large meteorological data network allowing for (prototypical) forecasting as well as PTHA applications, but sensitivity to source parameters is still unconstrained.

While modeling and parameterization of individual phenomena are possible, they are often excessively computationally expensive or highly uncertain due to missing constraints on input parameters. The multiple scales involved in PTHA from far-field propagation over oceanic distances to the need to resolve small scale inundation features while capturing physics and resolving uncertainties still represent an open challenge. Yet, this solution is needed to convey PTHA information properly into risk analysis.

Even more challenging is the situation in PTRA, where gaps exist in the transformation of physical hazard to risk and quantifying the uncertainties in the assessment of risk and resilience. Key concepts, such as physical vulnerability and mortality and their related uncertainties, are less developed than the main PTHA elements. There are gaps regarding selection of IM, limited observed damage asset- and location-wise, and limited experimental validation.

Furthermore, tsunami science is immature concerning embedding issues with intrinsically multi-hazard and multi-risk aspects, such as the cascading events that are entangled with tsunami hazards. A weakly developed link between quantitative PTRA and the social sciences is a clear gap. At this point, it is worth noting that terms “vulnerability” and “resilience” are multi-dimensional concepts that are used both in the consequence-based–natural sciences inspired–as well as context-based approaches–motivated by social sciences. Therefore, they may have quite different interpretations depending on the analysis context.

The overarching issue is integrating all the above components and developing an overall consistent sensitivity and uncertainty quantification framework, to understand tsunami risk and identify risk drivers, from the probability of the sources causing hazards to the probability of their physical consequences and societal impact. This understanding must be developed and prioritized in future research.

To guide such efforts, we have performed an expert judgment exercise that we discuss in the following subsection. It may help to identify most pressing research needs as well as prioritize research efforts.

Prioritizing Research Gaps

A scientific sensitivity analysis of the impact of each research gap, as conducted for individual sources in Sepúlveda et al. (2017) or Davies and Griffin (2020) , on the overall result of a PTHA or PTRA is certainly out of the scope of a single review paper. However, some guidance on prioritization of efforts is certainly desirable. Since we focused our description on research gaps, we suggest two important metrics for the prioritization: The susceptibility of PTHA and PTRA results on uncertainty due to the research gap (sensitivity) and the difficulty or amount of research effort needed to fill that respective gap (tractability).

In order to assess these two metrics, we conducted a first-pass expert judgment among the more than 50 co-authors of this article–all experts in one or more of the aspects of our review. A questionnaire was designed that asked three questions for each of the 47 research gap subsections that we have described before. The first two questions addressed the two metrics just mentioned. The third question asked if experts were of the opinion if the research gap existed because of a missing theoretical understanding, a lack of data, or both. While this somewhat ad hoc prioritization is not as solid as a rigorous expert elicitation (e.g., Cooke, 1991 ; Budnitz et al., 1997 ; Morgan, 2014 ; for tsunami hazard see an application in Basili et al., 2021 , or the discussion in Grezio et al., 2017 ) and hence could be somehow biased, we believe it still provides a valuable starting point for future efforts. It is a qualitative broad-brush answer to the question, which research gap may be of highest importance. More details on this exercise are given in the Supplementary Material .

The result of this exercise is visualized in a priority matrix ( Figure 2 ). It may appear natural to respond first to those research gaps that are located in the left upper quadrant of the matrix, since these gaps are considered less difficult to solve, while they are expected to influence the risk considerably. It can be noted that most of the research gaps are judged hard to solve but with a highly sensitive impact on the overall result. This seems natural, since high impact but simple problems would have been solved already.

FIGURE 2 . Priority Matrix for all the 47 research gaps identified. Letters indicate seismic source gaps (S), landslide source gaps (L), volcanic source gaps (V), meteorological source gaps (M), hydrodynamical modeling gaps (H), exposure related gaps (E), physical vulnerability related gaps (P), resilience related gaps (R), social vulnerability and risk indicators related gaps (I). The size of each marker relates to the agreement of experts, larger marker size means less spread in the answers. Colors are used to indicate if the gap is caused by missing theoretical understanding (blue), a lack of data (red), or both (cyan).

Based on our qualitative assessment, we can therefore identify some overall trends. First, we see some common challenges related to establishing annualized source probability of occurrence, which tend to cluster in the upper right corner of Figure 2 . This means that they are considered relatively most important, yet hardest to solve. Of these, obtaining landslide related annual source probabilities (L1) is considered both the largest yet most important obstacle, while a just slightly lower similar prioritization is evident for earthquake and volcano sources (S1 and V2). Another aspect that is considered important (and challenging) is the multi-hazard and cascading hazard aspect (R5). On the other hand, the research gaps that appear to be least sensitive and also easy to be filled are related to the numerical modeling of wave propagation (H3), as well as lack of joint intensity measures (I3) and gaps related to earthquake scaling relations (S4). Finally, we also note Figure 2 allows us to analyze several instances of components with similar sensitivity but with clearly different tractability. For instance, the lack of tsunami exposure data (E2) is considered as important as modeling complicated aspects of inundation (H6), but the former is assumed by the authors of this paper to be more easily achieved. Several other similar examples can be analyzed from Figure 2 .

It is noteworthy that most of the research gaps that most experts find consensus on are highly sensitive in their impact (all located at the upper margin of the point cloud). It is also worth noting that most research gaps are considered to relate to data and theory gaps and that those that relate to only a missing theoretical understanding are considered of relatively low sensitivity. This may be related to the fact that when we don’t understand a phenomenon, we cannot really judge whether it affects our results or not. In other words, this may be an “unknown”. Whereas a data related research gap may already have proved to be sensitively influential by a specific example, but due to a lack of data cannot be involved concisely into the workflow.

This priority matrix is just a very first approach. Since tsunami research eventually aims at protecting life from natural hazard, one could also prioritize those research gaps with direct impact on this goal. These would be in particular those topics mentioned in sections “ Gaps in Physical Vulnerability ,” “ Gaps in Risk and Resilience Metrics ,” and “ Gaps in Social Vulnerability, Multi-Dimensional Vulnerability and Risk Indicators ” (marked with P, R, and I; respectively).

Final Considerations

We have described and prioritized a comprehensive list of research gaps in PTHA and PTRA. While our approach to prioritization and the metric used to do so are to some extent subjective, it remains for the scientific community and further investigation as well as future incentives to decide, which directions to choose from. Nevertheless, our priority matrix will serve as a first impression on the weight of each of the identified research gaps.

An important part of the future puzzle will be exploring how uncertainties propagate to risk across disciplines. While uncertainties are more extensively explored in earthquake-related hazard analysis, non-seismic hazard, vulnerability, exposure and risk are lagging behind. On the other hand, different levels of maturity of methods and understanding will always exist. Hence, it is imperative to develop PTRA standards and guidelines to appropriately merge all risk analysis components considering their different uncertainty exploration and maturity level.

While validation of individual components has been addressed in several of the sections in our text, validating the PTHA and PTRA workflow as a whole is still ongoing research. Marzocchi and Jordan (2014) propose a methodology for a meaningful test of general probabilistic hazard models and an example of a successful application can be found in Meletti et al. (2021) .

Certainly, research gaps exist also outside of the scope of PTHA and PTRA. New computational methods, like fuzzy methods, machine learning techniques and even advances in classical computational methods have to be considered. Rigorous, information theory inspired approaches to validation may also be explored.

Considering the goals of the Sendai Framework for Disaster Risk Reduction and acknowledging the vast number of challenges outlined in the sections before, a concerted interdisciplinary effort to close the most pressing gaps is required. Attempts to gather expertize, facilitate exchange and development, and coordinate community efforts are represented by the Global Tsunami Model ( GTM, 2020 ) and the COST Action AGITHAR. A thorough consolidation of available sources of information in openly accessible databases, documentation of standard workflows, unification of terminology and metrics, as well as information hubs need to be established.

Author Contributions

All Authors contributed in early stages of the manuscript by individual contributions from their respective research field. Major contributions are listed as follows: Early compilation of text: MS, FL, SL, JB, IA-Q, TR; Abstract and Conclusions: JB, FL, SL, JS; Introduction: FL, FJ, SL, JB, JS, MS-G; Probabilistic Framework: FJ, MS-G, IA-A; Earthquake Sources: SL, JS, FL, KJ, MV, SM; Landslide Sources: FL, RP, SA, AS; Volcano Sources: JS, FL, SA; Meteotsunami Sources: JB; Hydrodynamic Modeling: JB, FL, JM, IA-Q, AB; Exposure: FJ, TR, MS-G; Physical Vulnerability: TR, FJ, JS, MDZ; Risk/Resilience: IA-A, FJ; Social Vulnerability: FJ, IA-A, IR; substantial revisions: AG, KJ, SL, SM, RDR, MS, JS, RP, AB, RB, SA, MV, MDZ, AS, IA-Q, CC; internal review: all authors.

This article is based upon work from COST Action CA18109 AGITHAR, supported by COST (European Cooperation in Science and Technology). VB and PC obtained support through the VES20 Inter-Cost LTC 20020 project. MS-G obtained support through the Severo Ochoa Centers of Excellence Program (Ref. CEX 2018–000797-S). TU acknowledges funding from the European Union’s Horizon 2020 research and innovation program (ChEESE project, Grant Agreement No. 823844).

Conflict of Interest

HT was employed by AECOM.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work would not have been possible without the help of many researchers, who are too many to be named individually, but we thank all participants of COST Action AGITHAR for their valuable input in official or informal conversations, workshops and interactions. We would like to thank the reviewers and editor for very considerate and challenging suggestions that improved this manuscript substantially.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2021.628772/full#supplementary-material

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Keywords: tsunami, probabilistic method, hazard, risk, research gap

Citation: Behrens J, Løvholt F, Jalayer F, Lorito S, Salgado-Gálvez MA, Sørensen M, Abadie S, Aguirre-Ayerbe I, Aniel-Quiroga I, Babeyko A, Baiguera M, Basili R, Belliazzi S, Grezio A, Johnson K, Murphy S, Paris R, Rafliana I, De Risi R, Rossetto T, Selva J, Taroni M, Del Zoppo M, Armigliato A, Bureš V, Cech P, Cecioni C, Christodoulides P, Davies G, Dias F, Bayraktar HB, González M, Gritsevich M, Guillas S, Harbitz CB, Kânoǧlu U, Macías J, Papadopoulos GA, Polet J, Romano F, Salamon A, Scala A, Stepinac M, Tappin DR, Thio HK, Tonini R, Triantafyllou I, Ulrich T, Varini E, Volpe M and Vyhmeister E (2021) Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps. Front. Earth Sci. 9:628772. doi: 10.3389/feart.2021.628772

Received: 12 November 2020; Accepted: 10 February 2021; Published: 29 April 2021.

Reviewed by:

Copyright © 2021 Behrens, Løvholt, Jalayer, Lorito, Salgado-Gálvez, Sørensen, Abadie, Aguirre-Ayerbe, Aniel-Quiroga, Babeyko, Baiguera, Basili, Belliazzi, Grezio, Johnson, Murphy, Paris, Rafliana, De Risi, Rossetto, Selva, Taroni, Del Zoppo, Armigliato, Bureš, Cech, Cecioni, Christodoulides, Davies, Dias, Bayraktar, González, Gritsevich, Guillas, Harbitz, Kânoǧlu, Macías, Papadopoulos, Polet, Romano, Salamon, Scala, Stepinac, Tappin, Thio, Tonini, Triantafyllou, Ulrich, Varini, Volpe and Vyhmeister. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jörn Behrens, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Tsunami and Earthquake Research

• Publications

Here you will find general information on the science behind tsunami generation, computer animations of tsunamis, and summaries of past field studies.

Field Studies

Our researchers collect data from sites of recent tsunamis to gain a better understanding of the potential impact on other regions with high probability of tsunamis. Their work helps inform coastal planning, protection, and resiliency.

Learn about the earthquakes that triggered recent tsunami events, and watch computer simulations of each tsunami from different angles.

Background information and links to our other tsunami research projects.

Could It Happen Here?

Life of a Tsunami

Local Tsunamis in the Pacific Northwest

Cascadia subduction zone marine geohazards.

• Probabilistic Forecasting of Earthquakes and Tsunamis

Tsunami Hazards, Modeling, and the Sedimentary Record

• Unusual Sources of Tsunamis - Presentation by Eric Geist

The scope of tsunami research within the USGS, however, is broader than the topics covered here. USGS researchers have also provided critical research toward understanding how sediments are transported during tsunami runup and deciphering the geologic record of prehistoric tsunamis. The USGS collaborates closely with the NOAA Center for Tsunami Research .

As part of the National Tsunami Hazard Mitigation Program , the USGS has also upgraded the seismograph network and communication functions of the U.S. Tsunami Warning Center .

Soon after the devastating tsunami in the Indian Ocean on December 26, 2004 many people have asked, “Could such a tsunami happen in the United States?” As a starting point, read “ Could It Happen Here? ”

Starting points:

Unusual Sources of Tsunamis

• Not all tsunamis are generated by earthquakes
• Tsunamis can be caused by volcanoes, landslides, and even atmospheric disturbances
• Data from tide gauges can help unravel the complex physics of these sources

Tsunami events:

September 8, 2017, Mexico

March 11, 2011, Japan

• Preliminary simulations of the tsunami
• Notes from the field : International Tsunami Team visits Japan before (2010) and after (May 2011); plus eyewitness accounts from California on March 11

October 25, 2010, Indonesia

February 27, 2010 Chile

September 29, 2009, Samoa

• Preliminary analysis of the tsunami
• USGS scientists in Samoa and American Samoa studying impacts of tsunami

April 1, 2007, Solomon Islands

March 28, 2005, Sumatra

• Analysis and comparison of the December 2004 and March 2005 tsunamis
• Field study of the effects of the December 2004 and March 2005 earthquakes and tsunamis  - April 2005

December 26, 2004, Sumatra-Andaman Islands

• Tsunami generation from the 2004 M=9.1 Sumatra-Andaman earthquake
• Initial findings on tsunami sand deposits, damage, and inundation in Sumatra  - January 2005
• Initial findings on tsunami sand deposits, damage, and inundation in Sri Lanka  - January 2005

June 23, 2001, Peru

• Preliminary analysis of the tsunami generated by the earthquake
• Preliminary analysis of sedimentary deposits from the tsunami

July 17, 1998, Papua New Guinea

• Descriptive model of the tsunami

April 18, 1906, San Francisco

Below are current tsunami studies and tsunami education materials.

The Question: Soon after the devastating tsunamis in the Indian Ocean on December 26, 2004 and in Japan on March 11, 2011, many people have asked, "Could such a tsunami happen in the United States?"

In the past century, several damaging tsunamis have struck the Pacific Northwest coast (Northern California, Oregon, and Washington). All of these tsunamis were distant tsunamis generated from earthquakes located far across the Pacific basin and are distinguished from tsunamis generated by earthquakes near the coast—termed local tsunamis.

Probabilistic Forecasting of Earthquakes, Tsunamis, and Earthquake Effects in the Coastal Zone

Coastal and Marine Geohazards of the U.S. West Coast and Alaska

PubTalk 1/2017 — Unusual sources of tsunamis

A presentation on "Unusual Sources of Tsunamis From Krakatoa to Monterey Bay" by Eric Geist, USGS Research Geophysicist

- Not all tsunamis are generated by earthquakes. - Tsunamis can be caused by volcanoes, landslides, and even atmospheric disturbances - Data from tide gauges can help unravel the complex physics of these sources

Below are USGS publications on a wide variety of topics related to tsunamis.

Earthquake magnitude distributions on northern Caribbean faults from combinatorial optimization models

On-fault earthquake magnitude distributions are calculated for northern Caribbean faults using estimates of fault slip and regional seismicity parameters. Integer programming, a combinatorial optimization method, is used to determine the optimal spatial arrangement of earthquakes sampled from a truncated Gutenberg-Richter distribution that minimizes the global misfit in slip rates on a complex fau

The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)

Book review of "tsunami propagation in tidal rivers", by elena tolkova, catastrophic landscape modification from a massive landslide tsunami in taan fiord, alaska.

The October 17th, 2015 Taan Fiord landslide and tsunami generated a runup of 193 m, nearly an order of magnitude greater than most previously surveyed tsunamis. To date, most post-tsunami surveys are from earthquake-generated tsunamis and the geomorphic signatures of landslide tsunamis or their potential for preservation are largely uncharacterized. Additionally, clear modifications described duri

Recent sandy deposits at five northern California coastal wetlands — Stratigraphy, diatoms, and implications for storm and tsunami hazards

A recent geological record of inundation by tsunamis or storm surges is evidenced by deposits found within the first few meters of the modern surface at five wetlands on the northern California coast. The study sites include three locations in the Crescent City area (Marhoffer Creek marsh, Elk Creek wetland, and Sand Mine marsh), O’rekw marsh in the lower Redwood Creek alluvial valley, and Pillar

A combinatorial approach to determine earthquake magnitude distributions on a variable slip-rate fault

Introduction to “global tsunami science: past and future, volume iii”, effect of dynamical phase on the resonant interaction among tsunami edge wave modes, probabilistic tsunami hazard analysis: multiple sources and global applications, introduction to “global tsunami science: past and future, volume ii”, reducing risk where tectonic plates collide, reducing risk where tectonic plates collide—u.s. geological survey subduction zone science plan.

Below are news stories about tsunamis.

National Preparedness Month 2020: Earthquakes and Tsunamis

Natural hazards have the potential to impact a majority of Americans every year.  USGS science provides part of the foundation for emergency ...

A Tale of Two Tsunamis—Why Weren’t They Bigger? Mexico 2017 and Alaska 2018

Why do some earthquakes trigger large tsunamis, and others don’t? Learn how earthquakes produce tsunamis, how scientists predict tsunami size and...

Below are FAQs associated with tsunamis.

Could a large tsunami happen in the United States?

Large tsunamis have occurred in the United States and will undoubtedly occur again. Significant earthquakes around the Pacific rim have generated tsunamis that struck Hawaii, Alaska, and the U.S. west coast. One of the largest and most devastating tsunamis that Hawaii has experienced was in 1946 from an earthquake along the Aleutian subduction zone. Runup heights reached a maximum of 33 to 55 feet...

Is there a system to warn populations of an imminent occurrence of a tsunami?

NOAA (National Oceanic and Atmospheric Administration) maintains the U.S. Tsunami Warning Centers , and work in conjunction with USGS seismic networks to help determine when and where to issue tsunami warnings. Also, if an earthquake meets certain criteria for potentially generating a tsunami, the pop-up window and the event page for that earthquake on the USGS Latest Earthquakes Map will include...

What are tsunamis?

Tsunamis are ocean waves triggered by: Large earthquakes that occur near or under the ocean Volcanic eruptions Submarine landslides Onshore landslides in which large volumes of debris fall into the water Scientists do not use the term "tidal wave" because these waves are not caused by tides. Tsunami waves are unlike typical ocean waves generated by wind and storms, and most tsunamis do not "break"...

What is it about an earthquake that causes a tsunami?

Although earthquake magnitude is one factor that affects tsunami generation, there are other important factors to consider. The earthquake must be a shallow marine event that displaces the seafloor. Thrust earthquakes (as opposed to strike slip) are far more likely to generate tsunamis, but small tsunamis have occurred in a few cases from large (i.e., > M8) strike-slip earthquakes. Note the...

What is the difference between a tsunami and a tidal wave?

Although both are sea waves, a tsunami and a tidal wave are two different and unrelated phenomena. A tidal wave is a shallow water wave caused by the gravitational interactions between the Sun, Moon, and Earth ("tidal wave" was used in earlier times to describe what we now call a tsunami.) A tsunami is an ocean wave triggered by large earthquakes that occur near or under the ocean, volcanic...

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Interdisciplinary geosciences perspectives of tsunami volume 2.

Acknowledgments

Conflicts of interest.

• Khakimzyanov, G.; Dutykh, D.; Mitsotakis, D.; Shokina, N.Y. Numerical Simulation of Conservation Laws with Moving Grid Nodes: Application to Tsunami Wave Modelling. Geosciences 2019 , 9 , 197. [ Google Scholar ] [ CrossRef ]
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• Rasyif, T.M.; Kato, S.; Syamsidik; Okabe, T. Numerical Simulation of Morphological Changes due to the 2004 Tsunami Wave around Banda Aceh, Indonesia. Geosciences 2019 , 9 , 125. [ Google Scholar ] [ CrossRef ]
• Ali Hasan Muhammad, R.; Tanaka, N. Energy Reduction of a Tsunami Current through a Hybrid Defense System Comprising a Sea Embankment Followed by a Coastal Forest. Geosciences 2019 , 9 , 247. [ Google Scholar ] [ CrossRef ]
• Imai, K.; Inazumi, T.; Emoto, K.; Horie, T.; Suzuki, A.; Kudo, K.; Ogawa, M.; Noji, M.; Mizuto, K.; Sasaki, T. Tsunami Vulnerability Criteria for Fishery Port Facilities in Japan. Geosciences 2019 , 9 , 410. [ Google Scholar ] [ CrossRef ]
• Paulik, R.; Lane, E.; Williams, S.; Power, W. Changes in Tsunami Risk to Residential Buildings at Omaha Beach, New Zealand. Geosciences 2019 , 9 , 113. [ Google Scholar ] [ CrossRef ]
• Pakoksung, K.; Suppasri, A.; Matsubae, K.; Imamura, F. Estimating Tsunami Economic Losses of Okinawa Island with Multi-Regional-Input-Output Modeling. Geosciences 2019 , 9 , 349. [ Google Scholar ] [ CrossRef ]
• Enomoto, Y.; Yamabe, T.; Sugiura, S.; Kondo, H. Possible Mechanism for the Tsunami-Related Fires That Occurred at Aonae Harbor on Okushiri Island in the 1993 Hokkaido Nansei-Oki Earthquake. Geosciences 2019 , 9 , 253. [ Google Scholar ] [ CrossRef ]
• Sugiura, M.; Sato, S.; Nouchi, R.; Honda, A.; Ishibashi, R.; Abe, T.; Muramoto, T.; Imamura, F. Psychological Processes and Personality Factors for an Appropriate Tsunami Evacuation. Geosciences 2019 , 9 , 326. [ Google Scholar ] [ CrossRef ]
• Ishibashi, R.; Nouchi, R.; Honda, A.; Abe, T.; Sugiura, M. A Concise Psychometric Tool to Measure Personal Characteristics for Surviving Natural Disasters: Development of a 16-Item Power to Live Questionnaire. Geosciences 2019 , 9 , 366. [ Google Scholar ] [ CrossRef ]
• Kitagawa, K. Co-Constructing a Narrative of ‘Never Give Up’ in Preparing for a Mega-Tsunami: An Exemplar of ‘All-Of-Society Engagement’? Geosciences 2019 , 9 , 486. [ Google Scholar ] [ CrossRef ] [ Green Version ]

Share and Cite

Suppasri, A. Interdisciplinary Geosciences Perspectives of Tsunami Volume 2. Geosciences 2019 , 9 , 503. https://doi.org/10.3390/geosciences9120503

Suppasri A. Interdisciplinary Geosciences Perspectives of Tsunami Volume 2. Geosciences . 2019; 9(12):503. https://doi.org/10.3390/geosciences9120503

Suppasri, Anawat. 2019. "Interdisciplinary Geosciences Perspectives of Tsunami Volume 2" Geosciences 9, no. 12: 503. https://doi.org/10.3390/geosciences9120503

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• Published: 15 April 2021

Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks

• Fumiyasu Makinoshima   ORCID: orcid.org/0000-0001-9247-4104 1 ,
• Yusuke Oishi   ORCID: orcid.org/0000-0003-4264-8932 1 ,
• Takashi Yamazaki 1 ,
• Takashi Furumura   ORCID: orcid.org/0000-0002-2091-0533 2 &
• Fumihiko Imamura   ORCID: orcid.org/0000-0001-7628-575X 3  

Nature Communications volume  12 , Article number:  2253 ( 2021 ) Cite this article

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Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.

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Introduction.

The rapid forecasting of hazards and dissemination of warnings can increase evacuation lead times and thus are key to saving lives during natural disasters. For tsunami disasters, quick evacuations supported by such warnings can drastically reduce the number of casualties, but inaccurate hazard forecasts and warnings can have the opposite effect. During the 2011 Tohoku tsunami event, the earthquake magnitude and tsunami height were initially underestimated based on a precomputed database; as a result, some residents felt safe based on the initial warning and were unaware of the need for evacuation 1 . Although the warning was updated several times based on additional observations, the updated information could not always reach the residents due to communication disruptions; consequently, many of the coastal residents did not realise the tsunami risk at their locations. The underestimation of tsunami risk increased the number of tsunami-induced casualties, and as a result, Japan experienced the loss of over 18,000 citizens, even with the in-place warning system. The need for accurate and reliable early warnings has been common during past mega-tsunamis; notably, the 2004 Indian Ocean tsunami had a catastrophic regional impact 2 , and the immense loss of nearly 230,000 lives stressed the importance of tsunami early warning systems, resulting in efforts to establish tsunami early warning frameworks in broader regions 3 . Therefore, fast and accurate tsunami forecasting methods based on real-time tsunami observation data are urgently needed, and the early warnings provided can contribute to mitigating casualties in future tsunami events.

To date, tsunami early warning systems have been developed based on past tsunami catastrophes and available technologies 4 . Especially in the decade since the 2011 Tohoku tsunami, dense tsunami observation networks have been implemented 5 , 6 , and various tsunami forecasting methods using real-time observation data, such as real-time tsunami inundation simulations using supercomputers 7 with rapid source estimations 8 , 9 and data assimilation approaches 10 , 11 , have been proposed based on the lessons learned from the 2011 event. However, real-time tsunami inundation forecasting immediately after an earthquake has remained challenging due to the difficulty of rapid estimation of the tsunami source, in which various uncertainties exist 12 , and due to the high computational costs associated with simulating nonlinear tsunami propagation in shallow water.

To overcome the above challenges, we present a tsunami forecasting method using a convolutional neural network (CNN) developed as a deep learning approach in AI research. The present method is capable of directly forecasting tsunami inundation based solely on up-to-date observation data and does not require extensive computational resources, such as those provided by supercomputers. During the past decade, deep learning has achieved great success in image and pattern recognition 13 as well as in broader areas, including physics-based simulations such as structural analysis 14 and computational fluid dynamics 15 ; moreover, tsunami observation networks have been improved.

In this work, we utilise a CNN to process valuable data from dense tsunami and geodetic observation networks and achieve remarkable tsunami forecasting performance. A notable advantage of a CNN is its low computational cost; i.e., the computational cost of CNN inference is much lower than that of nonlinear tsunami propagation simulations. Additionally, the present approach does not require a tsunami source estimation process since our CNN is designed for end-to-end forecasting from observation data to tsunami inundation forecasting. Therefore, the present CNN can immediately and accurately predict the tsunami inundation time series at a single location. To the best of our knowledge, this study is the first attempt at end-to-end tsunami inundation forecasting with a CNN, and the results verify the feasibility of AI-enabled tsunami forecasting for the establishment of early warnings.

CNN tsunami forecasting

Figure  1 shows a schematic view of the proposed tsunami forecasting method based on a CNN. First, 10,000 cases of numerical tsunami propagation and inundation simulations solving a set of partial differential equations were conducted to prepare the data sets for training the CNN. In the simulations, sets of synthetic observation data at observation points and the resulting tsunami inundation waveforms within 2 h were calculated based on randomly generated tsunami scenarios. For the tsunami observations obtained with ocean bottom pressure gauges, simulated tsunami waveforms converted into pressure waveforms at the ocean bottom were used as inputs. Then, we trained a CNN with synthetic tsunami data to directly predict the tsunami inundation waveform at a single onshore location (the green star in Fig.  1 ) solely from the observations. We built a 1-D CNN comprising a total of 15 layers for tsunami forecasting since compact 1-D CNNs are suited for real-time and low-cost applications because of their low computational complexity 16 . The stacked waveforms were fed into the network as inputs, and the corresponding features were extracted via convolutional and pooling-like layers. To consider additional observations as inputs for the network, we simply stacked same-size arrays so that the size of the input channels equalled the considered number of observation points. Since recent studies on tsunami source inversion suggest that inland geodetic observational data are useful for determining the tsunami source 8 , 9 , 17 , we also considered inland geodetic observations of initial ground heights as additional inputs to the CNN. The geodetic observations were fed as vectors with the same length as the offshore waveforms. The onshore tsunami inundation waveform was then forecasted by the fully connected layers using the features extracted from the convolutional layers. In contrast to general time series forecasting using deep learning, in which the subsequent transition of values at future time t  +  h is predicted based on the available observations at time t in the same series 18 , our network predicts a future onshore tsunami inundation waveform based on the available observations at different offshore and onshore points. After the training process, the trained CNN can output a tsunami inundation waveform at a single location on land (the green star in Fig.  1 ) when observation data for an unknown tsunami are given. Preparing multiple networks enables tsunami forecasting for multiple points.

The CNN learns the relation between observation data and the resulting tsunami waveform from thousands of numerical simulation results. The tsunami and geodetic observation points are uniformly selected to cover the forecasting areas and are illustrated as red points in the map. After the training process is completed, the trained CNN can predict a time series of inundation tsunami waveform at the onshore forecasting site, which is represented with a green star, solely from observation data for unknown tsunami scenarios.

Data preparation for the CNN

The source fault geometry of the 2011 Tohoku-Oki earthquake was considered based on the parameters presented in Fujii et al. 19 , and 44 sub-faults were used to generate synthetic tsunami data sets for the training of CNN (Fig.  2a ). Here, the slip parameters of each sub-fault were randomly assigned within their defined range for the sub-faults to generate various tsunami scenarios. We conducted tsunami simulations for a total of 12,000 scenarios and obtained observations for inputs (red points in Fig.  2a ) and an onshore tsunami inundation waveform at a single location (the green star in Fig.  2b ) to train the CNN. A total of 49 offshore tsunami observation points and five onshore geodetic observation points were considered as inputs for the CNN based on an actual tsunami observation network 20 and the Global Navigation Satellite System observation network 21 in Japan. These tsunami and geodetic observation points were selected to cover the fault region and the forecasting point. The offshore tsunami observation waveforms were sampled at 1 Hz, and a constant deformed ground height with the same number of samples was considered as the onshore geodetic observations. The forecasting waveform was sampled at 0.5 Hz with a data size of 3600. For the observation data, various time windows of tsunami observations (e.g., 5, 10, 15, 20, 25, and 30 min) were considered as inputs for the CNN to investigate the effect of the observation length on the forecasting accuracy. From the 12,000 simulation results, we used 10,000 cases for training, 1000 cases for validation monitoring, and 1000 for testing. The generated earthquake scenarios have a seismic moment M 0 ranging between 4.03 × 10 22 and 8.21 × 10 22  Nm, which corresponds to a moment magnitude M w ranging from 9.0 to 9.2, assuming a rigidity of 30 GPa (Fig.  2c ). The initial sea-bottom deformation caused by an earthquake was calculated with Okada’s formula 22 , and the tsunami propagation and inundation were simulated using TUNAMI-N2 code (see “Methods” for details). A single tsunami simulation for 2 h to generate data for the CNN required ~3 h using a CPU node with two Intel Xeon Gold 6148 processors with 384 GiB memory.

a Fault geometry and observation point as input for convolutional neural network (CNN). Red circles represent observation points for CNN. A total of 49 offshore tsunami observation points and five onshore geodetic observation points were considered to cover the fault region and the forecasting point (the green star). Fault geometry is considered based on Fujii et al. 19 , but its slip amount is randomly assigned within a given range to generate various tsunami scenarios. b A close view of the forecasting site, the Sendai plain where experienced the large extent of tsunami inundation. The green star is the forecasting site for which the CNN forecast a tsunami inundation waveform. c Distribution of the seismic moment of generated earthquake scenarios. A total of 12,000 scenarios were generated and divided into 10,000 training sets, 1000 validation sets and 1000 test sets.

Network configuration and training

The constructed CNN consisted of nine convolutional layers and three pooling-like convolutional layers followed by three fully connected layers (Supplementary Table  1 ). Each convolutional layer was followed by a Leaky ReLU activation function 23 with a negative slope parameter of a  = 0.01. Dropout with a dropout ratio of p  = 0.5 was applied to the outputs of the fully connected layers to prevent overfitting 24 . In the convolutional layers, we set the kernel size, stride and padding to minimise changes in the array size, and dimensionality reduction was performed mainly by the pooling-like layers. For the 5, 15, 25 and 35 min observation cases, the kernel size of the last convolutional layer was set as 3 to prevent the remainder from being generated, but such changes were minimised to evaluate the effect of the observation lead time. Thus, a longer observation period leads to a greater number of learnable parameters in the network.

We considered the mean squared error (MSE) between the simulated and forecasting waveforms over the forecasting time as the loss function. The network parameters were optimised to minimise the loss function by the Adam optimisation algorithm 25 . We used the default parameters suggested in the original paper that proposed the Adam algorithm ( β 1  = 0.9, β 2  = 0.999 and ε  = 10 −8 ), except for a step size of α  = 10 −4 . The training of the network was performed in the AI Bridging Cloud Infrastructure, a GPU-accelerated supercomputer in which each node has two Intel Xeon Gold 6148 CPUs and four NVIDIA Tesla V100 SXM2 GPUs with 384 GiB memory. The network training and validation in this study were implemented using Pytorch 26 and Horovod 27 . The batch size for each GPU during the training phase was 25, and five computer nodes were used for training. We considered 3000 epochs in the training process and retained the model that yielded the minimum validation loss for the validation data sets during training. The training process was completed within 2 h even for the largest network in this study.

Forecasting performance on synthetic tsunamis

We evaluated the performance of the trained CNN by analysing the forecasting results for 1000 test tsunami scenarios that were not considered during the training process, and we confirmed that the CNN successfully predicted the tsunami inundation waveform at the forecasting site (Fig.  3b ). For the evaluation of the forecasting accuracy, we considered two metrics: the maximum tsunami amplitude, defined as the maximum value of the tsunami amplitude over the forecasting time, and the tsunami arrival time, defined as the time when the tsunami flow depth first exceeds 10% of the maximum flow depth. Even with only 5 min offshore tsunami and inland geodetic observations, the mean absolute errors of the maximum tsunami amplitude and the tsunami arrival time were 0.4 m and 47.7 s, respectively. The average relative errors were 8.1% and 1.2% for the maximum tsunami amplitude and the tsunami arrival time, respectively. For these forecasts, the trained CNN required only 0.004 s on average using a single CPU node with 40 cores; this approach is much faster than conventional simulation-based forecasting approaches and requires fewer computational resources. The trained CNN yielded accurate and rapid forecasts of both the tsunami size and the arrival time for various tsunami scenarios directly from the observations.

a Observation points and a snapshot of tsunami propagation at 300 s for a simulated test scenario. The location of the observation points as inputs for the CNN are illustrated as circles. The forecasting site is represented with the green star. b Result of tsunami inundation forecasting with the CNN at the forecasting site with the ground-truth simulation result. c Error distribution of the maximum tsunami amplitude at the forecasting site for 1000 test scenarios.

Effects of the offshore observation length and geodetic data

We investigated the effect of the length of the observation inputs (5, 10, 15, 20, 25 and 30 min) and the importance of geodetic observation inputs by training different CNN models with different inputs (Fig.  4 ). The results show that longer observations led to higher forecasting accuracy. Using the geodetic observation data, the CNN achieved good forecasting performance equivalent to that of a CNN with a long observation period. We confirmed that this performance improvement achieved with comparatively long-term observations and geodetic data corresponds mainly to increased accuracy in the initial ground height estimation in which the CNN with a short observation length and without geodetic data exhibited poor accuracy (Supplementary Fig.  1 ). This result can be explained by the characteristics of tsunami long wavelength. Since the propagation speed of a tsunami is slow in shallow water, it is difficult to obtain the waveforms generated from nearshore faults that cause coastal subsidence with a short observation length; thus, short-term observations alone are not sufficient for estimating the magnitude of subsidence. Inverting the offshore fault slip distribution from onshore geodetic information can lead to non-unique solutions; however, geodetic data offer direct information about subsidence much faster than offshore observations. The proposed CNN integrated different information types from different observations and achieved the presented forecasting performance, even with very short observation periods.

The height of the bar represents the mean value for 1000 test scenarios, and the error bar in the figure represents the standard deviation.

Sensitivity of offshore and onshore observations

To understand information processing in CNN tsunami forecasting models, we conducted a sensitivity analysis (e.g., occlusion test 28 , 29 ) of the trained CNN models. In this analysis, we systematically removed inputs from certain observation points and evaluated the resulting amounts of change in the forecasting results to represent the impact on the CNN. The sensitivity analysis was conducted for models with different observation lengths, and the sensitivity of the observation points for forecasting was visualised (Fig.  5 ). The observation points with high sensitivities were located in the specific region of the observation network along the major path of tsunamis towards the forecasting site. High sensitivities were observed mainly over large slip areas since the amount of slip on an offshore fault has a predominant influence on tsunami inundation. In contrast, the information from distant observation points had almost no effect on the forecast, and thus, this information was not important for the CNN. This result indicates that for an accurate forecast, the CNN requires only certain observation points along the path of a tsunami propagating to a forecasting site. As the observation time increases, high sensitivities can also be confirmed at nearshore tsunami observation points. The CNNs using both offshore tsunami and onshore geodetic observations showed high sensitivities at both onshore and offshore points. The increased sensitivities for nearshore tsunami observations with longer observation periods and the higher sensitivities for additional onshore geodetic observations suggest that the CNN effectively integrates available information within limited observation periods to achieve high forecasting accuracy.

a CNN trained only with offshore tsunami observation data. b CNN trained with both offshore tsunami and onshore geodetic observation data. The observation points with high sensitivity indicate that the forecasting result changes considerably when the inputs from these observation points are lacking.

Forecasting speed

The computational time for tsunami forecasting with the CNN was measured to investigate the forecasting speed and assess the ability of the method to be employed for the issuance of tsunami warnings. Here, we measured the time required to forecast 1000 test scenarios using a single CPU node with 40 cores. Table  1 reports the average computational time for tsunami forecasting. The computational time increases as the observation time increases since a consistent CNN architecture is adopted for all observation lengths; a large neural network structure and a corresponding increase in the number of parameters are needed for longer observation times. The addition of 5 min observations increased the number of parameters by ~13 million, mainly due to the larger size of the fully connected layer after the convolutional layers. Nevertheless, the computational time for tsunami forecasting with the CNN was only 0.011 s, even for the largest CNN settings in the test (30 min offshore tsunami observations with geodetic observations). The addition of five geodetic input channels slightly increased the number of parameters by 1920 but had a negligible effect on the computational time. Thus, for CNN tsunami forecasting, the use of geodetic data was effective from the perspectives of not only the forecasting accuracy but also the computational time required to consider additional inputs. The tsunami forecasting speed achieved by the CNN is sufficiently fast to provide tsunami warnings, even with limited computational resources.

Application to the 2011 Tohoku tsunami event

We trained the CNNs using the 10,000 synthetic tsunami scenarios with the observation settings at the time the 2011 Tohoku tsunami event occurred and investigated the forecasting performance of the CNN for real events using real-world data (Fig.  6a ). This application used the same network configuration and the same 10,000 tsunami scenarios employed in the previous tests using synthetic data. We used publicly available observation data 30 , 31 during the event as inputs for the CNN (Fig.  6b, c ), i.e., three offshore tsunami observations recorded by GPS buoys (803, 801 and 806) and three onshore GNSS observations (Rifu, Watari and Souma1). For the missing parts in the tsunami waveforms observed by GPS buoys, 1 Hz data were prepared by cubic interpolation. For the geodetic observations, displacements at 5 min after the occurrence of the earthquake were used as inputs. Since complete data were not available at Souma1, we used the latest observation as the input. The forecasting site is shown as the green star in Fig.  6a . Most of the buildings around the forecasting site were totally destroyed by the tsunami; however, Arahama Elementary School (Arahama ES, illustrated as the black cross in Fig.  6a ) located close to the forecasting site provided survey results to verify the inundation forecast. During the 2011 event, the tsunami reached the second floor of the Arahama ES 32 , and a survey immediately after the event reported that tsunami debris were found at a height of 4.62 m above the school basement 33 . The arrival time of the devastating tsunami at this site was also estimated as ~15:55 (JST) based on a stopped clock 34 .

The elapsed time is from the earthquake occurrence. a Observation points for the CNN and the forecasting site. The red triangles represent the offshore tsunami observation points (803, 801, 806), and the red circles represent onshore geodetic observation points (Rifu, Watari, Souma1). The red cross mark in the small-scale map represents the epicentre of the 2011 earthquake. The forecasting site is shown in the large-scale map as the green star. Arahama ES (the black cross mark) where tsunami inundation traces for validation are available is located close to the forecasting site. b Tsunami waveforms at 803, 801 and 806 observed during the 2011 event 31 . c Geodetic observations at Rifu, Watari and Souma1 observed during the 2011 event. d Results of tsunami inundation forecasting with different observation periods. Grey triangles represent the observation interval used for forecast. Red waveforms are the CNN forecasting results. The height and position of the black cross mark represents the surveyed tsunami inundation trace at Arahama ES. e Results of offshore tsunami waveform forecasting at the Sendai New Port with different observation periods. Grey triangles represent the observation interval used for forecast. Red waveforms are CNN forecasting results. Grey lines are observed waveforms at the Sendai New Port during the 2011 event. Full observation was not available because the gauge was destroyed by the tsunami.

We trained the CNNs using only synthetic tsunami data with different observation periods and forecasted the tsunami inundation waveform using the actual observation data as inputs for the CNNs (Fig.  6d ). Initially, the CNNs forecasted unphysical waveforms with 20 min or less observations, when almost no tsunami signals were available. After obtaining the first positive peak of the tsunami from 30 min observations, the CNN forecasted an inundation waveform, but the forecasted amplitude was small compared to the actual trace at Arahama ES. With the 35 min observations, in which the entire first positive peak of the tsunami is available, the CNN forecasted a maximum flow depth of 3.88 m at 3974 s after the earthquake. Further 40 min observation revealed the negative peak of the tsunami, enabling the forecast of a larger inundation depth (5.64 m at 3952 s). Although the reported tsunami traces do not directly reflect the actual maximum flow depth, the CNN forecasting can be considered reasonably accurate. However, the forecasted arrival time of the maximum flow depth was ~3 min earlier than the estimated arrival time indicated by the stopped clock at the Arahama ES.

To further verify the CNN forecasting result, we trained the CNN for forecasting a tsunami waveform at the Sendai New Port where a time series of the tsunami until the first peak was recorded by a wave gauge (subsequent observations were not available because the gauge was destroyed by the tsunami). The forecasting results for the Sendai New Port are summarised in Fig. 6e . Similar to the previous inundation forecasting results, the CNN could not provide a reasonable forecast with 20 min or less observation time when sufficient tsunami signals were not available. After obtaining the positive peak of the tsunami using 30 min observation, the CNN could forecast the tsunami having the first peak value of 6.78 m, which is compatible with the observed first peak (6.62 m). The CNNs trained with 35 and 40 min observations forecasted similar peaks (5.77 and 6.42 m, respectively), and the rise of the first wave was more consistent with the observation data. Nevertheless, even with sufficient offshore tsunami observations, an ~3 min difference between the observation and the forecast appeared in the tsunami arrival time, suggesting that the CNN forecast tends to be slightly earlier than the actual tsunami arrival. A possible cause of this earlier arrival tendency is the effect of rupture propagations, i.e., when generating tsunami data sets for the CNN, the effect of rupture delays on each sub-fault was not considered, and instantaneous slip was assumed; however, the tsunami source inversion assuming sub-faults with multiple time windows suggested that the duration of the tsunamigenic slip of the 2011 Mw 9.0 earthquake lasted ~2.5–3 min 35 , and this duration was consistent with the difference in the arrival time. The current CNN forecast provides a slightly earlier arrival tendency and might serve as a cautious warning; however, to forecast the tsunami arrival time more accurately for large earthquakes, it may be necessary to consider the effect of rupture propagation on faults in large earthquakes.

Recent high-sampling-rate tsunami observations, especially by ocean bottom pressure gauges, can capture a wide range of geophysical phenomena in the ocean including ocean currents and seismic waves with much shorter periods (seconds to minutes) than that of tsunamis (minutes to hours) 36 . Consequently, such non-tsunami components can affect tsunami forecasting as noise 37 . To investigate the performance of the CNN under actual observation conditions, we further evaluated the effect of the short-period observation noise on the CNN tsunami forecasting. Noise waveforms were obtained using actual sea-level observation data at the GPS buoys 803, 801 and 806 a day before the 2011 tsunami event (Fig.  7a, b ), and the effect of noisy tsunami input on the forecasting results was evaluated using 40 min observation data (Fig.  7c–e ). For this evaluation, the similarity between the forecasting results with and without noise were evaluated using the formula for calculating the variance reduction 38 ; therefore, the similarity becomes 100% for a perfect match and lower for misfits. The result demonstrated that even with disturbances, the CNN successfully forecasted both the inundation and offshore waveforms with a small difference. The similarity between forecasting result with and without noise was 99.999% for inundation forecasting and 99.997% for offshore waveform forecasting. Additionally, we also examined the effect of much larger noise on the inundation forecasts. Larger noise waveforms were generated by adding white noise that ranged from −1.0 to 1.0 and was amplified by a certain ratio of the maximum observation amplitude. The additional noise test demonstrated that the CNN tsunami forecast maintained a high similarity of 99.7% on average for 1000 noisy inputs, even with a large noise level of 20% (see Supplementary Fig.  2 ).

a Sea-level observations from GPS buoys before the 2011 tsunami event. The grey and red lines represent the raw observations and the 15 min running average of the raw data, respectively. The elapsed time is from a day before the earthquake occurrence. b Noise waveforms extracted from the observations. The deviation from the averaged data (the red lines in a ) is extracted as noise. The figure shows the noise waveforms during the same observation period for the actual tsunami observations, but a day before the tsunami. c Noise waveforms for CNN input. The extracted noise waveforms before the event are added to the actual tsunami observations to prepare the noise inputs. d Inundation forecasting results with and without noise. The grey and red lines represent the forecasting results without and with noise, respectively. Because the difference between the two waveforms is small, the result without noise is illustrated with a thicker line. e Offshore tsunami forecasting results with and without noise. The grey and red lines represent the forecasting results without and with noise, respectively. Because the difference between the two waveforms is small, the result without noise is illustrated with a thicker line.

In this study, we presented a tsunami inundation forecasting approach based on a CNN trained with a large quantity of synthetic tsunami scenarios and verified the approach against both synthetic tsunamis and the actual tsunami observations from the 2011 Tohoku earthquake. The CNN tsunami forecast in this study is expected to overcome the bottlenecks of previous simulation-based real-time tsunami forecasting approaches for real applications, such as the difficulty of rapid tsunami source estimations immediately after the earthquake and high computational costs for simulating nonlinear tsunami propagations.

Large earthquakes generating tsunamis are infrequent, resulting in a lack of sufficient data to train the CNN; however, the test using actual observations during the 2011 tsunami event demonstrated that the CNN trained on only synthetic tsunamis can provide an accurate tsunami inundation forecast even for a real tsunami if the target tsunami scenario exists within the distribution of the training data sets. Therefore, it is important to prepare as many tsunami scenarios as possible for training the CNN to address the various mechanisms generating tsunamis.

Large tsunamis can be caused by large slip at plate boundaries, which is a common tsunami generation mechanism, as well as different types of mechanisms, such as a steep angle slip of splay faults 39 , outer-rise normal faults 40 and slow slip at a shallow plate boundary (tsunami earthquake) 41 . Additionally, non-seismic sources, such as volcanic eruptions 42 and landslides 43 , can also cause large tsunamis. To address these various tsunami sources by the CNN tsunami forecasting, a wide variety of tsunami scenarios should be included in the training set. A promising solution to address the various types of tsunami is to generate synthetic tsunami scenarios directly assuming a sea surface fluctuation (e.g., using Gaussian distributions having a range of several kilometres or tens of kilometres) rather than considering a sea surface deformation based on fault movements. Local tsunamis caused by volcanic eruptions or landslides can be represented with a few sea surface displacement units 44 , 45 , and the initial tsunami profiles generated by large earthquakes can also be represented by a superposition of a series of sea surface displacement units 46 , 47 , 48 . Simulating a wide variety of tsunami scenarios and training CNNs on large data sets are computationally expensive; however, it is feasible given the recent advances in high-performance computing of tsunami simulations and an efficient training approach as demonstrated in this study. CNN tsunami forecasting trained on various sea surface displacements should have the potential to be applied to a wide variety of tsunamis, including non-seismic tsunamis, for which the issuance of early warnings has been difficult by employing conventional earthquake-triggered approaches.

Tsunami simulation

We used the TUNAMI-N2 code 49 , 50 , which is distributed by the Tsunami Inundation Modelling Exchange project of the International Union of Geodesy and Geophysics and Intergovernmental Oceanographic Commission of United Nations Educational, Scientific and Cultural Organisation 50 , to create the tsunami simulation data for training the CNNs. The TUNAMI-N2 solves the following nonlinear shallow water Eqs. ( 1 )–( 3 ) with a staggered-grid finite-difference method:

where η is the tsunami height, D is the water depth, and M and N are the velocity fluxes in the x and y directions, respectively. g is the gravitational acceleration (=9.81 m/s 2 ), and n is Manning’s roughness coefficient, which we set to 0.025 s/m 1/3 in the simulations in this paper. As employed in general tsunami simulations, nested grid configurations were prepared in which the grid size was decreased by a factor of 3 (1215, 405, 135, 45 and 15 m) to reduce the computational cost; accordingly, tsunamis in coastal areas are evaluated at higher resolutions than those in offshore areas. Bathymetry data projected onto the Japan Plane Rectangular CS X were used for the simulation. The entire calculation domain with nested grids is illustrated in Supplementary Fig.  3 . The finest domain (∆ x  = ∆ y  = 15 m) covers the Sendai Plain, which experienced a large inundation extent and devastating damage during the 2011 tsunami. The time step of the simulation was set to ∆ t  = 0.2 s.

Sensitivity analysis

To evaluate the sensitivity at each station, we set the signal from a station to zero and evaluated the change in the MSE over 1000 test scenarios. An occlusion test was conducted for every observation point, and the sensitivity of an observation point was evaluated based on the relative change in the MSE from the baseline MSE (no occlusion). The sensitivity is calculated by Eq. ( 4 ):

where n is the number of test scenarios, \({\mathrm{MSE}}^\prime\) is the MSE with occlusion, and MSE is the MSE without occlusion (baseline).

Data availability

The tsunami observation data used in this study are available from The Nationwide Ocean Wave information network for Ports and HArbourS, NOWPHAS ( https://www.mlit.go.jp/kowan/nowphas/index_eng.html ). The GNSS observation data processed by Shu and Xu are used and available from ( https://doi.pangaea.de/10.1594/PANGAEA.914110 ). Other relevant data in this study are available from the corresponding author upon reasonable request.

Code availability

The code that supports the findings in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The computational resources of the AI Bridging Cloud Infrastructure (ABCI) provided by the National Institute of Advanced Industrial Science and Technology (AIST) were used. The topography and bathymetry data used in the tsunami numerical simulation were obtained by integrating the data from the Central Disaster Prevention Council; Tohoku Regional Development Bureau of Ministry of Land, Infrastructure, Transport and Tourism (MLIT); and Geospatial Information Authority of Japan. We used the observation data of the 2011 Tohoku tsunami obtained from the Nationwide Ocean Wave information network for Ports and HArbourS, NOWPHAS. The NOWPHAS tsunami and tidal observation data are observed by Ports and Harbours Breau, MLIT and processed by Port and Airport Research Institute.

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F.M. and Y.O. designed the research. F.M. and T.Y. prepared the simulation codes. F.I. prepared the data during the 2011 tsunami event. F.M. and T.F. wrote the manuscript. T.F. and F.I. contributed to data interpretation and provided discussions to improve the quality of the paper.

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Makinoshima, F., Oishi, Y., Yamazaki, T. et al. Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks. Nat Commun 12 , 2253 (2021). https://doi.org/10.1038/s41467-021-22348-0

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Savastano, G., A. Komjathy, O. Verkhoglyadova, O. Yang, A. Mazzoni, M. Crespi, Y. Wei, and A.J. Mannucci (2017): Real-time detection of tsunami ionospheric disturbances with a stand-alone GNSS receiver: a preliminary feasibility demonstration , Scientific Reports (Nature), 7, 46607, doi: 10.1038/srep46607.

Lynett, P., Y. Wei, and D. Arcas (2016): Tsunami Hazard Assessment: Best Modeling Practices and State-of-the-Art Technology . NUREG/CR-7223, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, Washington, DC, Published online .

Naito, C., H.R. Riggs, Y. Wei, and C. Cercone (2016): Shipping container impact assessment for tsunamis. J. Waterw. Port Coast. Ocean Eng., 05016003, doi: 10.1061/(ASCE)WW.1943-5460.0000348 .

Tang, L., V.V. Titov, C. Moore, and Y. Wei (2016): Real-time Assessment of the 16 September 2015 Chile Tsunami and Implications for Near-Field Forecast, Pure and Applied Geophysics, DOI: 10.1007/s00024-015-1226-3.

Titov, V.V., U. Kânoğlu, and C. Synolakis (2016): Development of MOST for real-time tsunami forecasting . J. Waterw. Port Coast. Ocean Eng., 142(6), 03116004, doi: 10.1061/(ASCE)WW.1943-5460.0000357, published online .

Titov, V., C. Moore, M. Spillane, Y. Wei, E. Gica, and H. Zhou (2016): Tsunami Hazard Assessment on Wave Generation, Propagation, and Inundation Modeling for the US East Coast . In U.S. Nuclear Regulatory Commission, NUREG.

Titov, V.V., Y. Song, L. Tang, E. Bernard, Y. Bar-Sever, and Y. Wei (2016): Consistent estimates of tsunami energy show promise for improved early warning , Pure and Applied Geophysics, doi: 10.1007/s00024-016-1312-1, published online .

Wei Y., Tsunami probabilistic reference maps for benchmarking Hawaii tsunami design zone maps per the ASCE 7-16 standard, NOAA Tech Memo., in review.

Wei, Y. and H. Zhou, Sensitivity study of unit sources for tsunami propagation and source inversion, NOAA Tech Memo., in review.

Arcas, D. (2015): A Tsunami Forecast Model for Santa Monica, California . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 9, Available online , 140 pp, doi: 10.7289/V5D50JX8.

Barberopoulou, A., M.R. Legg, and E. Gica (2015): Time evolution of man-made harbour modifications in San Diego: Effects on tsunamis . J. Mar. Sci. Eng., 3(4), 1382-1403, doi: 10.3390/jmse3041382.

Bernard, E., and V.V. Titov (2015): Evolution of tsunami warning systems and products . Philos. Trans. R. Soc. Lond. A, 373(2053), 20140371, doi: 10.1098/rsta.2014.0371.

Chamberlin, C., and D. Arcas (2015): Modeling tsunami inundation at Everett, Washington, from the Seattle Fault . NOAA Tech. Memo. OAR PMEL-147, Published online , 24 pp, doi: 10.7289/V59Z92V0.

Dilmen, D.I., V.V. Titov, and G.H. Roe (2015): Evaluation of the relationship between coral damage and tsunami dynamics; case study: 2009 Samoa tsunami . Published online , Pure Appl. Geophys., doi: 10.1007/s00024-015-1158-y.

Eblé, M.C., G.T. Mungov, and A.B. Rabinovich (2015): On the leading negative phase of major 2010-2014 tsunamis . Pure Appl. Geophys., 172(12), 3493-3508, doi: 10.1007/s00024-015-1127-5.

Gica, E. (2015): A Tsunami Forecast Model for Kihei, Hawaii . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 11, 112 pp.

Gica, E. (2015): A Tsunami Forecast Model for Midway Atoll . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 7, 132 pp, doi:10.7289/V5RJ4GCP. [ PDF Version ]

Gica, E. (2015): A Tsunami Forecast Model for Santa Barbara, California . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 12, 128 pp, doi: 10.7289/V508639D

Gica, E., V.V. Titov, C. Moore, and Y. Wei (2015): Tsunami simulation using sources inferred from various measurement data: Implications for the model forecast . Pure Appl. Geophys., Mar 2015, Vol. 172, Issue 3-4, pp 773-789, doi: 10.1007/s00024-014-0979-4.

Kânoglu, U., V.V. Titov, E. Bernard, and C. Synolakis (2015): Tsunamis: Bridging science, engineering and society . Philos. Trans. R. Soc. Lond. A, 373(2053), 20140369, doi: 10.1098/rsta.2014.0369.

Percival, D.B., D.W. Denbo, M.C. Eblé, E. Gica, P.Y. Huang, H.O. Mofjeld, M.C. Spillane, V.V. Titov, and E.I. Tolkova (2015): Detiding DART® buoy data for real-time extraction of source coefficients for operational tsunami forecasting . Published online , Pure Appl. Geophys., June 2015, Vol. 172, Issue 6, pp 1653-1678, doi: 10.1007/s00024-014-0962-0.

Rabinovich, A., and M. Eblé (2015): Deep-ocean measurements of tsunami waves . Pure Appl. Geophys., 172(12), 3281-3312, doi: 10.1007/s00024-015-1058-1.

Rasmussen, L., P.D. Bromirski, A.J. Miller, D. Arcas, R.E. Flick, and M.C. Hendershott (2015): Source location impact on relative tsunami strength along the U.S. West Coast . Published online , J. Geophys. Res., 120, doi: 10.1002/2015JC010718.

Reynolds, M.H., Berkowitz, P., Courtot, K.N., and Gica, E. (2015): Tohoku tsunami impacts on Central Pacific island avifauna. [In preparation]

Spillane, M.C. (2015): A Tsunami Forecast Model for Arena Cove, California . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 10, 140 pp, doi: 10.7289/V5000020.

Spillane, M. (2015): A Tsunami Forecast Model for Elfin Cove, Alaska . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 13, 176 pp, doi 10.7289/V5VH5KTQ

Spillane, M.C. (2015): A Tsunami Forecast Model for Nantucket, Massachusetts . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 8, 118 pp, doi:10.7289/V5MS3QPD. [ PDF Version ]

Wei, Y., H. Fritz, V. Titov, B. Uslu, C. Chamberlin, and N. Kalligeris (2015): Source models and near-field impact of the April 1, 2007 Solomon Islands tsunami . Pure Appl. Geophys., 172(3), doi: 10.1007/s00024-014-1013-6, 657-682.

Wei, Y., H.K. Thio, G. Chock, V. Titov, and C. Moore (2015): Development of probabilistic tsunami design maps along the U.S. West Coast for ASCE7. In 11th Canadian Conference on Earthquake Engineering, Victoria, BC, 21-24 July 2015. [Accepted]

Admire, A.R., L.A. Dengler, G.B. Crawford, B.U. Uslu, J.C. Borrero, S.D. Greer, and R.I. Wilson (2014): Observed and modeled currents from the Tohoku-oki, Japan and other recent tsunamis in Northern California . Published online , Pure Appl. Geophys., doi: 10.1007/s00024-014-0797-8.

Barberopoulou, A., M.R. Legg, E. Gica, and G. Legg (2014): Multiple wave arrivals contribute to damage and tsunami duration on the US West Coast . In Tsunami Events and Lessons Learned, Environmental and Societal Significance, Y.A. Kontar, V. Santiago-Fandiño, T. Takahashi (ed.), Advances in Natural and Technological Hazards Research, Vol. 35, Springer Netherlands, ISBN: 978-94-007-7268-7 (Print) 978-94-007-7269-4 (Online), 359-376.

Bernard, E., Y. Wei, L. Tang, and V.V. Titov (2014): Impact of Near-Field, Deep-Ocean Tsunami Observations on Forecasting the 7 December 2012 Japanese Tsunami . Pure and Appl. Geophys., 171(12), doi: 10.1007/s00024-013-0720-8, 3483-3491.

Chamberlin, C., and D. Arcas (2014): Modeling tsunami inundation at Everett, Washington from the Seattle Fault and the South Whidbey Island Fault. NOAA Tech. Memo. OAR PMEL-146. [For review]

Dall’Osso, F., D. Dominey-Howes, C. Moore, S. Summerhayes, and G. Withycombe (2014): The exposure of Sydney (Australia) to earthquake-generated tsunamis, storms and sea level rise: a probabilistic multi-hazard approach . Sci. Rep., 4, 7401, doi: 10.1038/srep07401. [ HTML Article ]

Dunbar, P., M. Eblé, G. Mungov, H. McCullough, and E. Harris (2014): NOAA’s historical tsunami event database, raw and processed water level data, and model output relevant to the 11 March 2011 Tohoku, Japan earthquake and tsunami . In Tsunami Events and Lessons Learned, Environmental and Societal Significance, Y.A. Kontar, V. Santiago-Fandiño, T. Takahashi (ed.), Advances in Natural and Technological Hazards Research, Vol. 35, Springer Netherlands, ISBN: 978-94-007-7268-7 (Print) 978-94-007-7269-4 (Online), 113-127.

Dong, S., N. Wang,  H. Lu and  L. Tang (2014): Bivariate Distribution of Group Height and Group Length for Ocean Waves Using Copula Method, Coastal Engineering (Accepted).

Eblé, M., and NCTR Staff (2014): A Tsunami Forecast Model for Newport, Oregon . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 5, 2nd Ed., 146 pp, doi:10.7289/V5125QK9. [ PDF Version ]

Gica, E., M.H. Teng, and P.L.-F. Liu: Probabilistic risk analysis of run-up and inundation due to distant tsunamis (2014). J. Waterw. Port Coast. Ocean Eng. [In preparation]

Greenslade, D.J.M., A. Annunziato, A. Babeyko, D. Burbidge, E. Ellguth, N. Horspool, T. Srinivasa Kumar, Ch. Patanjali Kumar, C. Moore, N. Rakowsky, T. Riedlinger, A. Ruangrassamee, P. Srivihok, and V.V. Titov (2014): An assessment of the diversity in scenario-based tsunami forecasts for the Indian Ocean . Cont. Shelf Res., 79, doi: 10.1016/j.csr.2013.06.001, 36-45.

Percival, D.M., D.B. Percival, D.W. Denbo, E. Gica, P.Y. Huang, H.O. Mofjeld, and M.C. Spillane (2014): Automated tsunami source modeling using the sweeping window positive elastic net . J. Am. Stat. Assoc., 109(506), doi: 10.1080/01621459.2013.879062, 491-499.

Spillane, M.C. (2014): A Tsunami Forecast Model for Point Reyes, California . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 6, 176 pp, doi:10.7289/V5W9573D . [ PDF Version ]

Tang. L., V. Titov, L. Wright,H. Mofjeld,C. Paternostro, P. Burke, K. Earwaker, Y. Wei, N. Wang, H. Zhou and E. Bernard (2014): Currents in Harbors, Bays and Channels in Hawaii of the 2011 Japan Tsunami and Implications for Forecasting. Geophys. Res. Lett. [In preparation]

Titov, V.V., Y. Song, L.Tang , E.N. Bernard, Y. Bar-Sever, Y. Wei, C. Moore and J. Newman (2014): Independent energy estimates of the 2011 Japan tsunami show promise for improving tsunami warnings. Geophys. Res. Lett. [In preparation]

Wei, Y., A.V. Newman, G.P. Hayes, V.V. Titov, and L. Tang (2014): Tsunami forecast by joint inversion of real-time tsunami waveforms and seismic or GPS data: application to the Tohoku 2011 tsunami . Pure and Appl. Geophys., 171(12), doi: 10.1007/s00024-014-0777-z, 3281-3305.

Wei, Y., U.S. ten Brink, and B.F. Atwater (2014): Tsunami sources that might explain the catastrophic overwash of Anegada, British Virgin Islands, between 1650 and 1800. J. Geophys. Res. [In preparation]

Yim, S.C., Y. Wei, M. Azabakht, S. Nimmala, and T. Potisuk (2014): Case study for tsunami design of coastal infrastructure: Spencer Creek Bridge, Oregon . Journal of Bridge Engineering, 10.1061/(ASCE)BE.1943-5592.0000631, 05014008.

Zhou, H., Y. Wei, L. Wright, and V. Titov (2014): Waves and currents in Hawaiian waters induced by the dispersive 2011 Tohoku tsunami. Published online, Pure Appl. Geophys., 171(12), doi: 10.1007/s00024-014-0781-3, 3365-3384.

Fritz, H.M., J.V. Hillaire, E. Molière, Y. Wei, and F. Mohammed (2013): Twin tsunamis triggered by the 12 January 2010 Haiti earthquake . Pure Appl. Geophys., 170(9-10), doi: 10.1007/s00024-012-0479-3, 1463-1474.

Gica, E., D. Arcas, and V. Titov (2013): Tsunami inundation modeling of San Juan Islands due to a Cascadia subduction zone earthquake. In Washington Emergency Management Division Hazard Report, State of Washington. [For review]

Mungov, G., M. Eblé, and R. Bouchard (2013): DART® tsunameter retrospective and real-time data: A reflection on 10 years of processing in support of tsunami research and operations . Pure Appl. Geophys., 170(9-10), doi: 10.1007/s00024-012-0477-5, 1369-1384. [PDF Version]

Percival, D.B., D.W. Denbo, M.C. Eblé, E. Gica, P.Y. Huang, H.O. Mofjeld, M.C. Spillane, V.V. Titov, and E.I. Tolkova (2013): Detiding DART® buoy data for real-time extraction of source coefficients for operational tsunami forecasting. Pure Appl. Geophys. [Accepted, In Revision]

Tolkova, E. (2013): Tide-tsunami interaction in Columbia River, as implied by historical data and numerical simulations . Pure Appl. Geophys., 170(6-8), Pageoph Topical Volumes: Tsunamis in the World Ocean, K. Satake, A. Rabinovich, D.D. Howes, and J.C. Borrero (Eds.), doi: 10.1007/s00024-012-0518-0, 1115-1126.

Uslu, B., M. Eble, D. Arcas, and V. Titov (2013): Tsunami Hazard Assessment for the Commonwealth of the Northern Mariana Islands . Tsunami Hazard Assessment Special Series, Vol. 3, 188 pp. [ PDF Version ]

Wei, Y., C. Chamberlin, V. Titov, L. Tang, and E.N. Bernard (2013): Modeling of the 2011 Japan tsunami - Lessons for near-field forecast . Pure Appl. Geophys., 170(6-8), doi: 10.1007/s00024-012-0519-z, 1309-1331. [ PDF Version ]

Arcas, D., and H. Segur (2012): Seismically generated tsunamis . Philos. Trans. R. Soc. Lond. A , 370 (1964), doi: 10.1098/rsta.2011.0457, 1505-1542.

Atwater, B.F., U.S. ten Brink, M. Buckley, R.S. Halley, B. Jaffe, A.M. López-Venegas, E.G. Reinhardt, M.P. Tuttle, S. Watt, and Y. Wei (2012): Geomorphic and stratigraphic evidence for an unusual tsunami or storm a few centuries ago at Anegada, British Virgin Islands . Nat. Hazards, 63(1), doi: 10.1007/s11069-010-9622-6, 51-84. [PDF Version]

Bernard, E. (2012): Thirteenth Annual Roger Revelle Commemorative Lecture—Tsunamis: Are we underestimating the risk? Oceanography, 25(2), doi: 10.5670/oceanog.2012.60, 208-218. [PDF Version]

Buckley, M., Y. Wei, B. Jaffe, and S. Watt (2012): Inverse modeling of velocities and inferred cause of overwash that emplaced inland fields of boulders at Anegada, British Virgin Islands . Nat. Hazards, 63(1), doi: 10.1007/s11069-011-9725-8, 133-149. [PDF Version]

Gica, E., D. Arcas, and V. Titov (2012): Tsunami inundation modeling of Long Beach and Ocean Shores, Washington due to a Cascadia subduction zone earthquake. InWashington Emergency Management Division Hazard Report, State of Washington. [For review]

Hamlington, B.D., R.R. Leben, O.A. Godin, E. Gica, V.V. Titov, B.J. Haines, and S.D. Desai (2012): Could satellite altimetry have improved early detection and warning of the 2011 Tohoku tsunami? Geophys. Res. Lett., 39, L15605, doi: 10.1029/2012GL052386. [PDF Version]

Mungov, G., M. Eblé, and R. Bouchard (2012): DART® tsunameter retrospective and real-time data: A reflection on 10 years of processing in support of tsunami research and operations . Pure Appl. Geophys., doi: 10.1007/s00024-012-0477-5.

Paros, J., C. Meinig, M. Spillane, P. Migliacio, L. Tang, W. Chadwick, T. Schaad, and S. Stalin (2012): Nano-resolution technology demonstrates promise for improved local tsunami warnings on the MARS project . In Oceans 2012 MTS/IEEE, Yeosu, Korea, 21-24 May 2012.

Tang, L., V.V. Titov, E. Bernard, Y. Wei, C. Chamberlin, J.C. Newman, H. Mofjeld, D. Arcas, M. Eble, C. Moore, B. Uslu, C. Pells, M.C. Spillane, L.M. Wright, and E. Gica (2012): Direct energy estimation of the 2011 Japan tsunami using deep-ocean pressure measurements . J. Geophys. Res., 117, C08008, doi: 10.1029/2011JC007635.

Yeh, H., E. Tolkova, D. Jay, S. Talke, and H. Fritz (2012): Tsunami hydrodynamics in the Columbia River . Journal of Disaster Research, 7(5), 604-608. [PDF Version]

Zhou, H., Y. Wei, and V.V. Titov (2012): Dispersive modeling of the 2009 Samoa tsunami . Geophys. Res. Lett., 39(16), L16603, doi: 1029/2012GL053068. [PDF Version]

Arcas, D, and Y. Wei (2011): Evaluation of velocity-related approximations in the non-linear shallow water equations for the Kuril Islands, 2006 tsunami event at Honolulu, Hawaii . Geophys. Res. Lett., 38 , L12608, doi: 10.1029/2011GL047083, 6 pp.

Barberopoulou, A., M.R. Legg, B. Uslu, and C.E. Synolakis (2011): Reassessing the tsunami risk in major ports and harbors of California I: San Diego . Nat. Hazards , 58 (1), doi: 10.1007/s11069-010-9681-8, 479-496.

Barberopoulou, A., J.C. Borrero, B. Uslu, M. Legg, and C. Synolakis (2011): A second generation of tsunami inundation maps for the state of California . Pure Appl. Geophys., 168(11), doi: 10.1007/s00024-011-0293-3, 2133-2146. [PDF Version]

Bernard, E., and C. Meinig (2011): History and future of deep-ocean tsunami measurements . In Proceedings of Oceans' 11 MTS/IEEE, Kona , IEEE, Piscataway, NJ, 19-22 September 2011, No. 6106894, 7 pp.

Borrero, J.C., B. McAdoo, B. Jaffe, L. Dengler, G. Gelfenbaum, B. Higman, R. Hidayat, A. Moore, W. Kongko, Lukijanto, R. Peters, G. Prasetya, V. Titov, and E. Yulianto (2011): Field survey of the March 28, 2005 Nias-Simeulue earthquake and tsunami. Pure Appl. Geophys. , 168(6-7), doi: 10.1007/s00024-010-0218-6, 1075-1088.

Cheung, K.F., Y. Wei, Y. Yamazaki, and S.C. Yim (2011): Modeling of 500-year tsunamis for probabilistic design of coastal infrastructure in the Pacific Northwest . Coastal Engineering , 58 (10), doi: 10.1016/j.coastaleng.2011.05.003, 970-985

Dengler, L., and B. Uslu (2011): Effects of harbor modification on Crescent City, California's tsunami vulnerability . Pure Appl. Geophys. , 168 (6-7), doi: 10.1007/s00024-010-0224-8, 1175-1185

Eble, M., V. Titov, G. Mungov, C. Moore, D. Denbo, and R. Bouchard (2011): Signal-to-noise ratio and the isolation of the 11 March 2011 Tohoku tsunami in deep-ocean tsunameter records . In Proceedings of Oceans' 11 MTS/IEEE, Kona , IEEE, Piscataway, NJ, 19-22 September 2011, No. 6107288, 4 pp.

Fritz, H.M., J.C. Borrero, C.E. Synolakis, E.A. Okal, R. Weiss, V. Titov, B. Jaffee, S. Foteinis, P. Lynett, I. Chan, and P.L.-F. Liu (2011): Insights on the 2009 South Pacific Tsunami in Samoa and Tonga from Field Surveys and Numerical Simulations, Earth Sci. Revs., 107, 66-75.

Fritz, H.M., J.V. Hillaire, E. Molière, F. Mohammed, and Y. Wei (2011): Coastal impacts by the 12 January 2010 earthquake and tsunamis in Haiti . In Proceedings of the Solutions to Coastal Disasters Conference 2011 , ASCE Conf. Proc., Anchorage, AK, 26-29 June, 2011.

Hamlington, B.D., R.R. Leben, O.A. Godin, J.F. Legeais, E. Gica, and V.V. Titov (2011): Detection of the 2010 Chilean tsunami using satellite altimetry . Nat. Hazards Earth Syst. Sci. , 11 , doi: 10.5194/nhess-11-2391-2011, 2391-2406.

Lawson, R.A., D. Graham, S. Stalin, C. Meinig, D. Tagawa, N. Lawrence-Slavas, R. Hibbins, and B. Ingham (2011): From Research to Commercial Operations: The Next Generation Easy-to-Deploy (ETD) Tsunami Assessment Buoy . In Proceedings of Oceans' 11 MTS/IEEE, Kona , IEEE, Piscataway, NJ, 19-22 September 2011, No. 6107114, 8 pp.

Newman, A.V., L. Feng, H.M. Fritz, Z.M. Lifton, N. Kalligeris, and Y. Wei (2011): The energetic 2010 Mw 7.1 Solomon Islands tsunami earthquake . Geophys. J. Int. , 186 (2), doi: 10.1111/j.1365-246X.2011.05057.x, 775-781.

Newman, A.V., G. Hayes, Y. Wei, and J. Convers (2011): The 25 October 2010 Mentawai tsunami earthquake, from real-time discriminants, finite-fault rupture, and tsunami excitation . Geophys. Res. Lett. , 38 , L05302, doi: 10.1029/2010GL046498.

Paros, J., E. Bernard, J. Delaney, C. Meinig, M. Spillane, P. Migliacio, L. Tang, W. Chadwick, T. Schaad, and S. Stalin (2011): Breakthrough underwater technology holds promise for improved local tsunami warnings . In Symposium for Underwater Technology (UT), 2011 IEEE - 2011 Workshop on Scientific Use of Submarine Cables and Related Technologies (SSC), 5-8 April 2011.

Percival, D.B., D.W. Denbo, M.C. Eble, E. Gica, H.O. Mofjeld, M.C. Spillane, L. Tang, and V.V. Titov (2011): Extraction of tsunami source coefficients via inversion of DART® buoy data . Nat. Hazards , 58 (1), doi: 10.1007/s11069-010-9688-1, 567-590.

Rabinovich, A.B., P.L. Woodworth, and V.V. Titov (2011): Deep-sea observations and modeling of the 2004 Sumatra tsunami in Drake Passage . Geophys. Res. Lett. , 38 , L16604, doi: 10.1029/2011GL048305.

Titov, V.V., C. Moore, D.J.M. Greenslade, C. Pattiaratchi, R. Badal, C.E. Synolakis, and U. Kânoğlu (2011): A new tool for inundation modeling: Community Modeling Interface for Tsunamis (ComMIT) . Pure Appl. Geophys. , 168 (11), doi: 10.1007/s00024-011-0292-4, 2121-2131. [PDF Version]

Titov, V., E. Bernard, D. Arcas, Y. Wei, C. Chamberlin, C. Moore, and L. Tang (2011): March 11, 2011 Tohoku-Japan tsunami: Lessons from forecast assessment . In Proceedings of Oceans' 11 MTS/IEEE, Kona , IEEE, Piscataway, NJ, 19-22 September 2011, No. 6107282, 2 pp.

Tolkova, E., and W. Power (2011): Obtaining natural oscillatory modes of bays and harbors via Empirical Orthogonal Function analysis of tsunami wave fields . Ocean Dynam. , 61 (6), doi: 10.1007/s10236-011-0388-5, 731-751.

Uslu, B., W. Power, D. Greenslade, V. Titov, and M. Eble (2011): The July 15, 2009 Fiordland, New Zealand tsunami: Real-time assessment . Pure Appl. Geophys. , 168 (11), doi: 10.1007/s00024-011-0281-7, 1963-1972. [PDF Version]

Wei, Y., V. Titov, A. Newman, G. Hayes, L. Tang, and C. Chamberlin (2011): Near-field hazard assessment of March 11, 2011 Japan tsunami sources inferred from different methods . In Proceedings of Oceans' 11 MTS/IEEE, Kona , IEEE, Piscataway, NJ, 19-22 September 2011, No. 6107294, 9 pp.

Zhou, H., C. Moore, Y. Wei, and V. V. Titov (2011): A nested-grid Boussinesq-type approach to modeling dispersive propagation and runup of landslide-generated tsunami . Nat. Hazards Earth Sys. Sci. , 11 (10), doi: 10.5194/nhess-11-2677-2011, 2677-2697. [PDF Version]

Arcas, D., and B. Uslu (2010): A Tsunami Forecast Model for Crescent City, California . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 2, 112 pp. [Full Text]

Arcas, D., and C. Chamberlin (2010): Modeling tsunami inundation in Lake Washington from a Seattle Fault earthquake. NOAA Tech. Memo. OAR PMEL-147.

Atwater, B., U.S. ten Brink, M. Buckley, R. Halley, B. Jaffe, A. Lopez-Venegas, E. Reinhardt, M. Tuttle, S. Watt, and Y. Wei, (2010). Geomorphic and stratigraphic evidence for an unusual tsunami or store a few centuries ago at Anegada, British Virgin Islands. Natural Hazards, doi:10.1007/s11069-010-9622-6.

Bernard, E.N., C. Meinig, V.V. Titov, K. O'Neil, R. Lawson, K. Jarrott, R. Bailey, F. Nelson, S. Tinti, C. von Hillebrandt, and P. Koltermann (2010): Tsunami resilient communities . In Proceedings of the "OceanObs'09: Sustained Ocean Observations and Information for Society" Conference (Vol. 1) , Venice, Italy, 21-25 September 2009, Hall, J., D.E. Harrison, and D. Stammer, Eds., ESA Publication WPP-306, doi: 10.5270/OceanObs09.pp.04. [PDF Version]

González, F.I., E.L. Geist, B. Jaffe, U. Kânoğlu, H. Mofjeld, C.E. Synolakis, V.V. Titov, D. Arcas, D. Bellomo, D. Carlton, T. Horning, J. Johnson, J. Newman, T. Parsons, R. Peters, C. Peterson, G. Priest, A. Venturato, J. Weber, F. Wong, and A. Yalciner (2009): Probabilistic tsunami hazard assessment at Seaside, Oregon, for near- and far-field seismic sources . J. Geophys. Res. , 114 , C11023, doi: 10.1029/2008JC005132.

Righi, D., and D. Arcas (2010): A Tsunami Forecast Model for Newport, Oregon . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 5, 80 pp. [Full Text]

Tang, L., V.V. Titov, and C.D. Chamberlin (2010): A Tsunami Forecast Model for Hilo, Hawaii . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 1, 94 pp. [Full Text]

Tolkova, E. (2010): EOF analysis of a time series with application to tsunami detection . Dynam. Atmos. Ocean , 50 (1), doi: 10.1016/j.dynatmoce.2009.09.001, 35-54. [PDF Version]

Uslu, B., D. Arcas, V.V. Titov, and A.J. Venturato (2010): A Tsunami Forecast Model for San Francisco, California . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 3, 88 pp. [Full Text]

Uslu, B., M. Eble, V.V. Titov, and E.N. Bernard (2010): Distant tsunami threats to the ports of Los Angeles and Long Beach, California . NOAA OAR Special Report, Tsunami Hazard Assessment Special Series, Vol. 2, 100 pp. [Full Text]

Uslu, B., V.V. Titov, M. Eble, and C. Chamberlin (2010): Tsunami hazard assessment for Guam . NOAA OAR Special Report, Tsunami Hazard Assessment Special Series, Vol. 1, 186 pp. [Full Text]

Wei, Y., and D. Arcas (2010): A Tsunami Forecast Model for Kodiak, Alaska . NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 4, 96 pp. [Full Text]

Zhou, H., and M.H. Teng (2010): Extended fourth-order depth-integrated model for water waves and currents generated by submarine landslides :. J. Eng. Mech. , 136 (4), 506-516.

Arcas, D. (2009): Tsunamis . In Ocean: An illustrated atlas , S.A. Earle and L.K. Glover (eds.), National Geographic Society, 351 pp

Bernard, E.N., and A.R. Robinson (eds.) (2009): Tsunamis. The Sea, Volume 15 . Harvard University Press, Cambridge, MA and London, England, 450 pp.

Barberopoulou, A., J.C. Borrero, B. Uslu, N. Kalligeris, J.D. Goltz, R.I. Wilson, and C.E. Synolakis (2009): New maps of California to improve tsunami preparedness . Eos Trans. AGU , 90 (16), 137-144.

Borrero, J.C., R. Weiss, E.A. Okal, R. Hidayat, Suranto, D. Arcas, and V.V. Titov (2009): The tsunami of 2007 September 12, Bengkulu province, Sumatra, Indonesia: post-tsunami field survey and numerical modelling . Geophys. J. Int. , 178 (1), 180-194.

Dengler, L., B. Uslu, A. Barberopoulou, S.C. Yim, and A. Kelly (2009): The November 15, 2006 Kuril Islands-generated tsunami in Crescent City, California . Pure Appl. Geophys. , 166 (1-2), 37-53.

Mofjeld, H.O. (2009): Tsunami measurements . Chapter 7 in The Sea, Volume 15: Tsunamis , Harvard University Press, Cambridge,MA and London, England, 201-235.

Okal, E.A., C.E. Synolakis, B. Uslu, N. Kalligeris, and E. Voukouvalas (2009): The 1956 earthquake and tsunami in Amorgos, Greece . Geophys. J. Int. , 178 (3), 1533-1554.

Okumura, Y., K. Harada, E. Gica, T. Takahashi, S. Koshimura, S. Suzuki, and Y. Kawata (2009): Long-term effects of social responses in the 1994 Mindoro tsunami disaster in Oriental Mindoro, Philippines . JSCE Journal of Earthquake Engineering , 30 , 637-644.

Percival, D.B., D. Arcas, D.W. Denbo, M.C. Eble, E. Gica, H.O. Mofjeld, M.C. Spillane, L. Tang, and V.V. Titov (2009): Extracting tsunami source parameters via inversion of DART® buoy data. NOAA Tech. Memo. OAR PMEL-144, 22 pp. [ PDF ]

Tang, L., V. V. Titov, and C. D. Chamberlin (2009), Development, testing, and applications of site-specific tsunami inundation models for real-time forecasting, J. Geophys. Res., 114, C12025, doi:10.1029/2009JC005476. [ PDF ]

Titov, V.V. (2009): Tsunami forecasting . Chapter 12 in The Sea, Volume 15: Tsunamis , Harvard University Press, Cambridge, MA and London, England, 371-400.

Tolkova, E. (2009): Principal component analysis of tsunami buoy record: Tide prediction and removal . Dyn. Atmos. Oceans , 46 (1-4), doi: 10.1016/j.dynatmoce.2008.03.001, 62-82.

Zhou, H.-Q. and Teng, M.H. (2009) Higher-order modeling of water waves generated by submerged moving disturbances, in: Proceedings of the 28th International Conference on Ocean, Offshore and Arctic Engineering (CD-ROM), Honolulu, Hawaii, May 31-June 5, 2009.

Zhou, H.-Q., Teng, M.H., Lin, P.-Z., Gica, E. and Feng, K.-L. (2009), Predicting run-up of breaking and nonbreaking long waves by applying the Cornell COMCOT model, in: Nonlinear Wave Dynamics: Selected Papers of the Symposium Held in Honor of Philip L-F Liu's 60th Birthday, Lynett P. (ed.), World Scientific Publishing Co., Hackensack, New Jersey, U.S.A.

Bernard, E., C. Maier, C. McCreery, S.J. McLean, J.M. Rhoades, and P.M. Whitmore (2008): NOAA's Tsunami Program 2008-2017 Strategic Plan . NOAA Technical Memorandum NWS-95, 29 pp. [PDF]

Bernard, E.N. (2008): The tsunami-resilient coastal community . J. Earthq. Tsunami , 2 (4), 279-285.

Burwell, D., and E. Tolkova (2008): Curvilinear of the MOST model with application to the coast-wide tsunami forecast . NOAA Tech. Memo. OAR PMEL-142, 28 pp. [PDF]

Chawla, A., J. Borrero and V. Titov, (2008), Evaluating wave propagation and inundation characteristics of the MOST tsunami model over a complex 3D beach, in: Advances in Coastal and Ocean Engineering, v. 10, 261-267.

Gica, E., M. Spillane, V.V. Titov, C. Chamberlin, and J.C. Newman (2008): Development of the forecast propagation database for NOAA's Short-term Inundation Forecast for Tsunamis (SIFT) . NOAA Tech. Memo. OAR PMEL-139, 89 pp. [PDF]

Greenslade, D.J.M. and V.V. Titov (2008): A comparison study of two numerical tsunami forecasting systems, Pure appl. geophys. 165, 1991-2001, DOI 10.1007/s00024-008-0413-x.

Martin, M.E., Weiss, R., Bourgois, J., Pinegina, T.K., Houston, H. & Titov, V.V., 2008. Combining constraints form tsunami modeling and sedimentology to untangle the 1969 Ozernoi and 1971 Kamchatskii tsunamis. Geophysical Research Letters, 35, L01610.

Merati, N., C. Chamberlin, C. Moore, T.C. Vance, and V.V. Titov (2008): Integration of tsunami analysis tools into a GIS workspace—Research, modeling and hazard mitigation efforts within NOAA’s Center for Tsunami Research. In Geotechnical Contributions to Urban Hazard and Disaster Analysis, P. Showalter and L. Wu (eds.), Springer Publishing.

Rabinovich, A.B., R.E. Thomson, V.V. Titov, F.E. Stephenson, and G.C. Rogers, 2008: Locally generated tsunamis recorded on the coast of British Columbia.  Atmos.-Ocean, 46 (3), 343-360. [ PDF ]

Spillane, M.C., E. Gica, V.V. Titov, and H.O. Mofjeld (2008): Tsunameter network design for the U.S. DART ® arrays in the Pacific and Atlantic Oceans . NOAA Tech. Memo. OAR PMEL-143, 165 pp. [PDF]

Synolakis, C.E., E.N. Bernard, V.V. Titov, U. Kânoğlu, and F.I. González (2008): Validation and verification of tsunami numerical models . Pure Appl. Geophys. , 165 (11-12), 2197-2228.

Tang, L., C. Chamberlin, and V.V. Titov (2008): Developing tsunami forecast inundation models for Hawaii: Procedures and testing . NOAA Tech. Memo. OAR PMEL-141, 46 pp. [PDF]

Tang, L., V.V. Titov, Y. Wei, H.O. Mofjeld, M. Spillane, D. Arcas, E.N. Bernard, C. Chamberlin, E. Gica, and J. Newman (2008): Tsunami forecast analysis for the May 2006 Tonga tsunami . J. Geophys. Res. , 113 , C12015, doi: 10.1029/2008JC004922.

Vance, T.C., S. Cross, B. Megrey, S. Mesick, and C. Moore (2008): GeoFish—visualization and analysis of particle tracking model output for fish and shellfish larvae. In ICES 2008 Annual Science Conference—theme session R (Data Mangement), Halifax, Nova Scotia, 22-26 September 2008.

Wei, Y., E. Bernard, L. Tang, R. Weiss, V. Titov, C. Moore, M. Spillane, M. Hopkins, and U. Kânoğlu (2008): Real-time experimental forecast of the Peruvian tsunami of August 2007 for U.S. coastlines . Geophys. Res. Lett. , 35 , L04609, doi: 10.1029/2007GL032250.

Wei, Y. (2008). Tsunami impact assessment for Unalaska, Alaska. Proceedings of the Conference on Solutions to Coastal Disasters, American Society of Civil Engineers, Ed. L. Wallendorf, L. Ewing, C. Jones, and B. Jaffe, 118-131, Oahu, Hawaii.

Weiss, R. (2008): Sediment grains moved by passing tsunami waves: Tsunami deposits in deep water . Mar. Geol. , 250 (3-4), 251-257.

Arcas, D., and V. Titov (2006): Sumatra tsunami: lessons from modeling . Surv. Geophys. , 27 (6), doi: 10.1007/s10712-006-9, 679-705.

Bahlburg, H., and R. Weiss (2007): Sedimentology of the December 26, 2004, Sumatra Tsunami deposits in eastern India (Tamil Nadu) and Kenya . Int. J. Earth Sci. , 96 (6), doi: 10.1007/s00531-006-0148-9, 1195-1209.

Bernard, E.N. (2007): Modeling the December 26, 2004, Sumatra Tsunami . In An Introduction to the World's Oceans , Sverdrup, K.A., and E.V. Armbrust (eds.), 9th Edition, Chapter 10, The Waves, McGraw Hill, 266-268.

Bernard, E.N., L. Dengler, and S. Yim (2007): National Tsunami Research Plan: Report of a workshop sponsored by NSF/NOAA . NOAA Tech. Memo. OAR PMEL-133, 135 pp. [PDF]

Bernard, E.N., and V.V. Titov (2007): Improving tsunami forecast skill using deep ocean observations . Mar. Tech. Soc. J. , 40 (3), 23-26.

Borrero, J., B. Uslu, C. Synolakis, and V.V. Titov (2007): Modeling far-field tsunamis for California ports and harbors . In Coastal Engineering 2006—Proceedings of the 30th International Conference , San Diego, CA, 3-8 December 2006, 1566-1578.

Burwell, D., E. Tolkova, and A. Chawla (2007): Diffusion and dispersion characterization of a numerical tsunami model . Ocean Modelling , 19 (1-2), 10-30.

Cherniawsky, J.Y., V.V. Titov, K. Wang, and J.-Y. Li, (2007), Numerical Simulations of Tsunami Waves and Currents for Southern Vancouver Island from a Cascadia Megathrust Earthquake, Pure appl. geophys. 164, 465-495.

Denbo, D.W., K.T. McHugh, J.R. Osborne, P. Sorvik, and A.J. Venturato. (2007): NOAA tsunami forecasting system: Design and implementation using service oriented architecture . In 23rd Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology , 87th AMS Annual Meeting, San Antonio, TX, 14-18 January 2007.

Fritz, H.M., W. Kongko, A. Moore, B. McAdoo, J. Goff, C. Harbitz, B. Uslu, N. Kalligeris, D. Suteja, K. Kalsum, V. Titov, A. Gusman, H. Latief, E. Santoso, S. Sujoko, D. Djulkarnaen, H. Sunendar, and C. Synolakis (2007): Extreme run-up from the 17 July 2006 Java tsunami . Geophys. Res. Lett. , 34 , L12602, doi: 10.1029/2007GL029404.

Gica, E., M.H. Teng, P.L.-F. Liu, V. Titov, and H. Zhou (2007): Sensitivity analysis of source parameters for earthquake-generated distant tsunamis . J. Waterw. Port Coast. Ocean Eng. , 133 (6), 429-441.

Geist, E.L., V.V. Titov, D. Arcas, F.F. Pollitz, and S.L. Bilek (2007): Implications of the 26 December 2004 Sumatra-Andaman Earthquake on tsunami forecast and assessment models for great subduction-zone earthquakes . Bull. Seis , 97 (1A), doi: 10.1785/0120050619, S249-S270.  

González, F.I., E. Bernard, P. Dunbar, E. Geist, B. Jaffe, U. Kânoğlu, J. Locat, H. Mofjeld, A. Moore, C. Synolakis, V. Titov, and R. Weiss (Science Review Working Group) (2007): Scientific and technical issues in tsunami hazard assessment of nuclear power plant sites . NOAA Tech. Memo. OAR PMEL-136, Pacific Marine Environmental Laboratory, Seattle, WA, 125 pp. + appendices on CD. [PDF]

Koshimura, S., T. Katada, H.O. Mofjeld, and Y. Kawata (2006): A method for estimating casualties due to the tsunami inundation flow . Nat. Hazards , 39 (2), 265-274.

Mofjeld, H.O., F.I. González, V.V. Titov, A.J. Venturato, and J.C. Newman (2007): Effects of tides on maximum tsunami wave heights: Probability distributions . J. Atmos. Ocean. Tech. , 24 (1), 117-123.

Okal, E.A., and V.V. Titov (2007): MTSU: Recovering seismic moments from tsunameter records . Pure Appl. Geophys. , 164 (2-3), doi: 10.1007/s00024-006-0, 355-378

Synolakis, C.E., E.N. Bernard, V.V. Titov, U. Kânoğlu, and F.I. González (2007): Standards, criteria, and procedures for NOAA evaluation of tsunami numerical models . NOAA Tech. Memo. OAR PMEL-135, NOAA/Pacific Marine Environmental Laboratory, Seattle, WA, 55 pp. [PDF]

Tolkova, E. (2007): Compression of MOST Propagation Database . NOAA Tech. Memo. OAR PMEL-134, NTIS: PB2007-108218, 9 pp. [PDF]

Vance, T., N. Merati, S. Mesick, C. Moore, and D. Wright (2007): Tightly linking scientific models and data with a GIS for scenario testing and geovisualization. In 15th ACM International Symposium on Advances in Geographic Information Systems (ACM GIS 2007), Seattle, WA, 7-9 November 2007.

Venturato, A.J., D. Arcas, V.V. Titov, H.O. Mofjeld, C.C. Chamberlin, and F.I. Gonzalez (2007):  Tacoma, Washington, tsunami hazard mapping project: Modeling tsunami inundation from Tacoma and Seattle fault earthquakes .  NOAA Tech. Memo. OAR PMEL-132, 23pp.

Venturato, A.J., D. Arcas, and U. Kânoğlu (2007): Modeling tsunami inundation from a Cascadia subduction zone earthquake for Long Beach and Ocean Shores, Washington. NOAA Tech. Memo. OAR PMEL-137, NOAA/Pacific Marine Environmental Laboratory, Seattle, WA, 26 pp. [PDF]

Venturato, A.J., D.W. Denbo, K.T. McHugh, J.R. Osborne, P. Sorvik, and C. Moore (2007): NOAA tsunami forecasting system: Using numerical modeling tools to assist in tsunami warning guidance . In 23rd Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology , 87th AMS Annual Meeting, San Antonio, TX, 14-18 January 2007, Paper 3A.8.

Venturato, A.J., E. Gica, D.W. Denbo, and V.V. Titov (2007): Assimilation of real-time tsunami event data into forecasting models . In 11th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) , 7th AMS Annual Meeting, San Antonio, TX, 14-18 January 2007, Paper P1.10

Weiss, R., K. Wünnemann, and H. Bahlburg (2006): Numerical modelling of generation, propagation and run-up of tsunamis caused by oceanic impacts: model strategy and technical solutions . Geophys. J. Int. , 167 (1), doi: 10.1111/j.1365-246X., 77-88.

Wong, F.L., A.J. Venturato, and E.L. Geist (2007): Seaside, Oregon Tsunami Pilot Study—Modernization of FEMA Flood Maps: GIS Data . U.S. Geological Survey Open-File Report, Data Series 236, http://pubs.usgs.gov/ds/2006/236/. [Full Text]

Wong, F.L., A.J. Venturato, and E.L. Geist (2007): GIS data for the Seaside, Oregon, tsunami pilot study to modernize FEMA flood hazard maps . In Proceedings of Coastal Zone 07 , Portland, Oregon, 22-26 July 2007, Paper 3029

Wünnemann, K., R. Weiss, and K. Hoffmann (2007): Characteristics of oceanic impact-induced large waves—Re-evaluation of the tsunami hazard . Meteorit. Planet. Sci., 42(11), doi: 10.1111/j.1945-5100.2007.tb00548.x, 1893-1903.

Bernard, E.N., H.O. Mofjeld, V.V. Titov, C.E. Synolakis, and F.I. González (2006): Tsunami: Scientific frontiers, mitigation, forecasting, and policy implications . Phil. Trans. Roy. Soc. Lon. A , 364 (1845), doi: 10.1098/rsta.2006.1809, 1989-2007.  

Geist, E.L., S.L. Bilek, D. Arcas, and V.V. Titov (2006): Differences in tsunami generation between the December 26, 2004 and March 28, 2005 Sumatra earthquakes . Earth Planets Space , 58 (2), 185-193.

Geist, E.L., V.V. Titov, and C.E. Synolakis (2006): Tsunami: Wave of change . Scientific American, 294 (1), 56-63. Text of article from the Scientific American website .

Synolakis, C.E., and E.N. Bernard (2006): Tsunami science before and beyond Boxing Day 2004 . Phil. Trans. Roy. Soc. Lon. A , 364 (1845), doi: 10.1098/rsta.2006.1824, 2231-2265.

Tang, L., C. Chamberlin, E. Tolkova, M. Spillane, V.V. Titov, E.N. Bernard, and H.O. Mofjeld (2006): Assessment of potential tsunami impact for Pearl Harbor, Hawaii . NOAA Tech. Memo. OAR PMEL.

Weiss, R., and H. Bahlburg (2006): The Coast of Kenya Field Survey after the December 2004 Indian Ocean Tsunami . Earthquake Spectra, 22(S3) , S235-S240. doi:10.1193/1.2201970

Weiss, R., Kai Wünnemann, Heinrich Bahlburg (2006): Numerical modelling of generation, propagation and run-up of tsunamis caused by oceanic impacts: model strategy and technical solutions . Geophysical Journal International 167 (1), 77-88. doi:10.1111/j.1365-246X.2006.02889.x

Weiss, R., H. Bahlburg (2006): A note on the preservation potential of offshore tsunami deposits. Journal of Sedimentary Research . Vol. 76 , DOI: 10.2110/jsr.2006.110.12

Bernard, E.N. (2005): The U.S. National Tsunami Hazard Mitigation Program: A successful state-federal partnership . Nat. Hazards, 35(1), Special Issue, U.S. National Tsunami Hazard Mitigation Program, 5-24.

Bernard, E.N. (ed.) (2005): Developing Tsunami-Resilient Communities: The National Tsunami Hazard Mitigation Program .. (Reprinted from Natural Hazards, 35(1), 2005), Springer, The Netherlands, 184 pp.

Bernard, E.N., F.I. González, and V.V. Titov (2005): The tsunameter and real-time tsunami forecasting . Chikyu Monthly, 27(3), 210-215 (in Japanese).

González, F.I., E.N. Bernard, C. Meinig, M. Eble, H.O. Mofjeld, and S. Stalin (2005): The NTHMP tsunameter network. Nat. Hazards , 35(1), Special Issue, U.S. National Tsunami Hazard Mitigation Program, 25-39.

González, F.I., V.V. Titov, H.O. Mofjeld, A. Venturato, S. Simmons, R. Hansen, R. Combellick, R. Eisner, D. Hoirup, B. Yanagi, S. Yong, M. Darienzo, G. Priest, G. Crawford, and T. Walsh (2005): Progress in NTHMP hazard assessment . Nat. Hazards, 35 (1), Special Issue, U.S. National Tsunami Hazard Mitigation Program, 89-110.

Meinig, C., S.E. Stalin, A.I. Nakamura, F. González, and H.G. Milburn (2005): Technology Developments in Real-Time Tsunami Measuring, Monitoring and Forecasting. In Oceans 2005 MTS/IEEE, 19-23 September 2005, Washington, D.C.

Meinig, C., S.E. Stalin, A.I. Nakamura, H.B. Milburn (2005), Real-Time Deep-Ocean Tsunami Measuring, Monitoring, and Reporting System: The NOAA DART II Description and Disclosure .

WHF Smith, R Scharroo, VV Titov, D Arcas, BK Arbic (2005): Satellite Altimeters Measure Tsunami. Oceanography Vol.18, No.2, June 2005. ( PDF )

Titov, V.V., F.I. González, E.N. Bernard, M.C. Eble, H.O. Mofjeld, J.C. Newman, and A.J. Venturato (2005): Real-time tsunami forecasting: Challenges and solutions . Nat. Hazards, 35(1), Special Issue, U.S. National Tsunami Hazard Mitigation Program, 41-58.

Titov, V.V., A.B. Rabinovich, H.O. Mofjeld, R.E. Thomson, and F.I. González (2005): The global reach of the 26 December 2004 Sumatra Tsunami .. Science, 309(5743), 2045-2048. Download: Abstract or Reprint (PDF) or Full Text

Venturato, A.J., V.V. Titov, D. Arcas, F.I. González, and C.D. Chamberlin (2005): Reducing the impact: U.S. tsunami forecast modeling and mapping efforts . In ESRI International User Conference Proceedings (UC2471), 25-29 July 2005, San Diego, CA.

Wong, F.L., E.L. Geist, and A.J. Venturato (2005): Probabilistic tsunami hazard maps and GIS . In ESRI International User Conference Proceedings (UC2000), 25-29 July 2005, San Diego, CA.

Mofjeld, H.O., A.J. Venturato, F.I. González, V.V. Titov, and J.C. Newman (2004): The harmonic constant datum method: Options for overcoming datum discontinuities at mixed-diurnal tidal transitions .. J. Atmos. Ocean. Tech., 21, 95-104.

González, F.I., B.L. Sherrod, B.F. Atwater, A.P. Frankel, S.P. Palmer, M.L. Holmes, R.E. Karlin, B.E. Jaffe, V.V. Titov, H.O. Mofjeld, and A.J. Venturato (2003): Puget Sound Tsunami Sources—2002 Workshop Report .. A contribution to the Inundation Mapping Project of the U.S. National Tsunami Hazard Mitigation Program, NOAA OAR Special Report, NOAA/OAR/PMEL, 34 pp. [Full Text]

Titov, V.V., F.I. González, H.O. Mofjeld, and A.J. Venturato (2003): NOAA TIME Seattle Tsunami Mapping Project: Procedures, data sources, and products . NOAA Tech. Memo. OAR PMEL-124, NTIS: PB2004-101635, 21 pp. [PDF]

Taft, B., C. Meinig, L. Bernard, C. Teng, S. Stalin, K. O'Neil, M. Eble, and C. Demers (2003): Transition of the Deep-ocean Assessment and Reporting of Tsunamis network—A technology transfer from NOAA Research to NOAA Operations . In Proceedings of Oceans 2003, San Diego, CA, September 2003, 2582-2588.

Walsh, T.J., V.V. Titov, A.J. Venturato, H.O. Mofjeld, and F.I. González (2003): Tsunami hazard map of the Elliott Bay area, Seattle, Washington—Modeled tsunami inundation from a Seattle fault earthquake . Washington State Department of Natural Resources Open File Report 2003-14, 1 plate, scale 1:50,000. [PDF]

Bernard, E.N. (2002): The U.S. National Tsunami Hazard Mitigation Program .. In Proceedings of Solutions to Coastal Disasters '02, L. Ewing and L. Wallendorf (eds.), ASCE, San Diego, CA, 24-27 February 2002, 964-971.

Koshimura, S., H.O. Mofjeld, F.I. González, and A.L. Moore (2002): Modeling the 1100 bp paleotsunami in Puget Sound, Washington . Geophys. Res. Lett., 29 (20), 1948, doi: 10.1029/2002GL015170.

Mofjeld, H.O., A.J. Venturato, V.V. Titov, F.I. González, and J.C. Newman (2002): Tidal datum distributions in Puget Sound, Washington, based on a tidal model . NOAA Tech. Memo. OAR PMEL-122 (PB2003102259), NOAAA/Pacific Marine Environmental Laboratory, Seattle, WA, 35 pp. [PDF]  

Titov, V.V., and F.I. González (2002): Modeling solutions for short-term inundation forecasting for tsunamis . In Underwater Ground Failures on Tsunami Generation, Modeling, Risk and Mitigation , NATO Advanced Workshop, Istanbul, Turkey, 23-26 May 2001, 227-230.

Bernard, E.N. (2001): The U.S. National Tsunami Hazard Mitigation Program Summary . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), NTHMP Review Session, R-1, Seattle, WA, 7-10 August 2001, 21-27.  

Bernard, E.N. (2001): Tsunami: Reduction Of Impacts through three Key Actions (TROIKA) . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 1-1, Seattle, WA, 7-10 August 2001, 247-262.

Bernard, E.N., F.I. González, C. Meinig, and H.B. Milburn (2001): Early detection and real-time reporting of deep-ocean tsunamis . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), NTHMP Review Session, R-6, Seattle, WA, 7-10 August 2001, 97-108.

Eble, M.C., S.E. Stalin, and E.F. Burger (2001): Acquisition and quality assurance of DART data . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 5-9, Seattle, WA, 7-10 August 2001, 625-632.

González, F.I., V.V. Titov, H.O. Mofjeld, A.J. Venturato, and J.C. Newman (2001): The NTHMP Inundation Mapping Program . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), NTHMP Review Session, R-2, Seattle, WA, 7-10 August 2001, 29-54.  

Imamura, F., S. Koshimura, H. Iwasa, S. Imoto, K. Sato, and N. Shuto (2001): TIMING: Sanriku network for the exchange of tsunami information . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), 5-3, Seattle, WA, 7-10 August 2001, 589-593.  

Koshimura, S., and H.O. Mofjeld (2001): Inundation modeling of local tsunamis in Puget Sound, Washington, due to potential earthquakes . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 7-18, Seattle, WA, 7-10 August 2001, 861-873.  

Koshimura, S., A.L. Moore, and H.O. Mofjeld (2001): Simulation of paleotsunamis in Puget Sound, Washington . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 7-7, Seattle, WA, 7-10 August 2001, 761-773.  

S. Koshimura and H. O. Mofjeld: Puget Sound Tsunami Inundation Modeling Preliminary Report: Phase 2 ( April, 2001)

S. Koshimura and H. O. Mofjeld: Puget Sound Tsunamis - A New Partnership to Model and Map the Hazard , USGS/Project Impact Meeting, November 29, 2000

Meinig, C., M.C. Eble, and S.E. Stalin (2001): System development and performance of the Deep-ocean Assessment and Reporting of Tsunamis (DART) system from 1997-2001 . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), NTHMP Review Session, R-24, Seattle, WA, 7-10 August 2001, 235-242.  

Mofjeld, H.O., V.V. Titov, F.I. González, and J.C. Newman (2001): Tsunami scattering provinces in the Pacific Ocean . Geophys. Res. Lett., 28 (2), 335-337.

Mofjeld, H.O., P.M. Whitmore, M.C. Eble, F.I. González, and J.C. Newman (2001): Seismic-wave contributions to bottom pressure fluctuations in the North Pacific—Implications for the DART Tsunami Array . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 5-10, Seattle, WA, 7-10 August 2001, 633-641.  

Titov, V.V., and F.I. González (2001): Numerical study of the source of the July 17, 1998 PNG tsunami . In Tsunami Research at the End of a Critical Decade , G.T. Hebenstreit (ed.), Kluwer Academic Publishers, 197-207.

Titov, V.V., F.I. González, H.O. Mofjeld, and J.C. Newman (2001): Project SIFT (Short-term Inundation Forecasting for Tsunamis) . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 7-2, Seattle, WA, 7-10 August 2001, 715-721.

Titov, V.V., B. Jaffe, F.I. González, and G. Gelfenbaum (2001): Re-evaluating source mechanisms for the 1998 Papua New Guinea tsunami using revised slump estimates and sedimentation modeling . In Proceedings of the International Tsunami Symposium 2001 (ITS 2001) (on CD-ROM), Session 2-4, Seattle, WA, 7-10 August 2001, 389-395.

Titov, V.V., H.O. Mofjeld, F.I. González, and J.C. Newman (2001): Offshore forecasting of Alaska tsunamis in Hawaii . In Tsunami Research at the End of a Critical Decade , G.T. Hebenstreit (ed.), Kluwer Academic Publishers, 75-90.  

Mofjeld, H.O., F.I. González, E.N. Bernard, and J.C. Newman (2000): Forecasting the heights of later waves in Pacific-wide tsunamis . Nat. Hazards, 22 , 71-89.

Mofjeld, H.O., V.V. Titov, F.I. Gonzalez, and J.C. Newman (2000): Analytic theory of tsunami wave scattering in the open ocean with application to the North Pacific Ocean NOAA Technical Memorandum ERL PMEL-116 38 pp [PDF]  

Bourgeois, J., C. Petroff, H. Yeh, V. Titov, C.E. Synolakis, B. Benson, J. Kuroiwa, J. Lander, and E. Norabuena (1999): Geologic setting, field survey and modeling of the Chimbote, Northern Peru, tsunami of 21 February 1996 . Pure Appl. Geophys., 154 , 513-540.

Mofjeld, H.O., F.I. González, and J.C. Newman (1999): Tsunami prediction in U.S. coastal regions . In Coastal Ocean Prediction, Coastal and Estuarine Studies 56, C. Mooers (ed.), chapter 14, American Geophysical Union, 353-375.  

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Mofjeld, H.O., F.I. González, and M.C. Eble (1996): Subtidal bottom pressure observed at Axial Seamount in the northeastern continental margin of the Pacific Ocean . J. Geophys. Res., 101 (C7), 16,381-16,390.  

Blackford, M., and H. Kanamori (1995): Tsunami Warning System Workshop Report (September 14-15, 1994) . NOAA Tech. Memo. ERL PMEL-105 (PB95-187175), 95 pp.  

González, F.I., E.N. Bernard, and K. Satake (1995): The Cape Mendocino tsunami, 25 April 1992 . In Tsunami: Progress in Prediction, Disaster Prevention and Warning , Y. Tsuchiya and N. Shuto (eds.), Series of Advances in Natural and Technological Hazards Research, Kluwer Academic Publishers, 151-158.  

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Good, J.W. (1995): Tsunami Education Planning Workshop, Findings and Recommendations . NOAA Tech. Memo. ERL PMEL-106 (PB95-195970), 41 pp.

Mofjeld, H.O., F.I. González, M.C. Eble, and J.C. Newman (1995): Ocean tides in the continental margin off the Pacific Northwest Shelf . J. Geophys. Res., 100 (C6), 10,789-10,800.  

Ritsema, J., S.N. Ward, and F.I. González (1995): Inversion of deep-ocean tsunami records for 1987-88 Gulf of Alaska earthquake parameters . Bull. Seismol. Soc. Am., 85 , 747-754.

Bernard, E.N., and F.I. González (1994): Tsunami runup distribution generated by the July 12, 1993, Hokkaido-Nansei-Oki earthquake . Proceedings, 26th Meeting of the U.S.-Japan Cooperative Program in Natural Resources Panel on Wind and Seismic Effects, Gaithersburg, MD, 16-20 May 1994, 335-343.  

Bernard, E.N., and F.I. González (1994): Tsunami Inundation Modeling Workshop Report (November 16-18, 1993) . NOAA Tech. Memo. ERL PMEL-100 (PB94-143377), 139 pp

González, F.I., T. Mero, and D. Castel (1994): U.S. tsunami measurement capabilities . Proceedings, Third UJNR Tsunami Workshop, 27 August 1993, Osaka, Japan, 46-49.

Tanioka, Y., K. Satake, L. Ruff, and F. González (1994): Fault parameters and tsunami excitation of the May 13, 1993, Sumagin Islands earthquake . Geophys. Res. Lett., 21 (11), 967-970.

Bernard, E.N., and F.I. González (1993): Tsunami devastates Japanese coastal region . Eos Trans. Am. Geophys. Union, 74 (37), 417, 432.

Bernard, E.N., F.I. González, and K. Satake (1993): The Cape Mendocino Tsunami, 25 April 1992 . Tsunami '93, Proceedings of the IUGG/IOC International Tsunami Symposium, Wakayama, Japan, August 23-27, 1993, 461-467.

González, F., S. Sutisna, P. Hadi, E. Bernard, and P. Winarso (1993): Some observations related to the Flores Island earthquake and tsunami . Tsunami '93, Proceedings of the IUGG/IOC International Tsunami Symposium, Wakayama, Japan, August 23-27, 1993, 789-801.

González, F.I., and E.N. Bernard (1993): The Cape Mendocino Tsunami . Earthquakes and Volcanoes, 23 (3), 135-138.  

González, F.I., and Ye.A. Kulikov (1993): Tsunami dispersion observed in the deep ocean . In: Tsunamis in the World, S. Tinti (ed.), Kluwer Pub. Co., 7-16.  

Mader, C.L., and E.N. Bernard (1993): Modeling tsunami flooding of Crescent City . Tsunami '93, Proceedings of the IUGG/IOC International Tsunami Symposium, Wakayama, Japan, August 23-27, 1993, 321-326.

McCarthy, R.J., E.N. Bernard, and M.R. Legg (1993): The Cape Mendocino Earthquake: A local tsunami wakeup call? Proceedings of the Eighth Symposium on Coastal and Ocean Management, Vol. III, 2812-2828.

Oppenheimer, D., G. Beroza, G. Carver, L. Dengler, J. Eaton, L. Gee, F. González, A. Jayko, W.H. Li, M. Lisowski, M. Magee, G. Marshall, M. Murray, R. McPherson, B. Romanowicz, K. Satake, R. Simpson, P. Somerville, R. Stein, and D. Valentine (1993): The Cape Mendocino, California, Earthquakes of April 1992: Subduction at the Triple Junction . Science, 261 , 433-438

Walker, D.A., and E.N. Bernard (1993): Comparison of T-phase spectra and tsunami amplitudes for tsunamigenic and other earthquakes . J. Geophys. Res., 98 (C7), 12,557-12,565.

Mofjeld, H.O. (1992): Subtidal sea level fluctuations in a large fjord system . J. Geophys. Res., 97 (C12), 20,191-20,199.  

Bernard, E.N., and H.B. Milburn (1991): Improved satellite-based emergency alerting system . J. Atmos. Ocean. Tech., 8 (6), 879-883.  

Eble, M.C., and F.I. González (1991): Deep-ocean bottom pressure measurements in the northeast Pacific . J. Atmos. Ocean. Tech., 8(2), 221-233.

González, F.I., C.L. Mader, M.C. Eble, and E.N. Bernard (1991): The 1987-88 Alaskan Bight Tsunamis: Deep ocean data and model comparisons . Special Issue on Tsunami Hazard (E.N. Bernard, ed.), Nat. Hazards, 4 (2,3), 119-139.  

Bernard, E.N., R.R. Behn, G.T. Hebenstreit, F.I. González, P. Krumpe, J.F. Lander, E. Lorca, P.M. McManamon, and H.B. Milburn (1990): On mitigating rapid onset natural disasters: Project THRUST (Tsunami Hazards Reduction Utilizing Systems Technology) . In: Proceedings of Workshop XLVI, The 7th U.S.-Japan Seminar on Earthquake Prediction, 12-15 September 1988, Menlo Park, CA, USGS Open-File Report 90-98, 125-130.

González, F.I., and Ye.E. Kulikov (1990): On frequency modulation observed in two PacTOP deep ocean tsunami records . Proceedings, 2nd UJNR Workshop, Honolulu, HI, 5-6 November 1990, A.M. Brennan and J.F. Lander (eds.), NGDC Geophysical Record Document No. 24, 27-29

Bernard, E.N. (1989): Early warning system for tsunamis is tested successfully in Chile . Water Res. J. , September 1989, 66-69.

Bernard, E.N. (1989): Early warning system for tsunamis is tested successfully in Chile . Earth in Space, 1 (9), 7-10.

Bernard, E.N., R.R. Behn, G.T. Hebenstreit, F.I. González, P. Krumpe, J.F. Lander, E. Lorca, P.M. McManamon, and H.B. Milburn (1988): On mitigating rapid onset natural disasters: Project THRUST (Tsunami Hazards Reduction Utilizing Systems Technology) . Eos Trans. Am. Geophys. Union, 69 (24), 649-661.

Bernard, E.N., R.R. Behn, and H.B. Milburn (1988): A tsunami early warning system — Summary . Proceedings of the International Tsunami Symposium, International Union of Geodesy and Geophysics, August 18-19 1987, Vancouver, B.C., Canada, 276-290.  

González, F.I., E.N. Bernard, H.B. Milburn, D. Castel, J. Thomas, and J.M. Hemsley (1988): The Pacific Tsunami Observation Program (PacTOP) . Proceedings of the International Tsunami Symposium, International Union of Geodesy and Geophysics, August 18-19, 1987, Vancouver, B.C., Canada, 3-19

González, F.I., C. Fox, and E.N. Bernard (1988): Tsunami source definition through pre- and post-event seafloor mapping . Proceedings of the Third Biennial National Ocean Service International Hydrographic Conference, Baltimore, MD, April 12-15, 102-108

González, F.I., E.N. Bernard, and H.B. Milburn (1987): A program to acquire deep ocean tsunami measurements in the North Pacific . In: Proceedings of Coastal Zone 87, WW Div., ASCE, 26-29 May 1987, Seattle, WA, 3373-3381.

Bernard, E.N., and R.R. Behn (1986): Regional Tsunami Warning System (THRUST) . In: Wind and Seismic Effects , Proceedings of the 17th Joint Panel Meeting of the U.S.-Japan Cooperative Program in Wind and Seismic Effects, National Bureau of Standards, NBS IR 86-3364, 644-649.  

Bernard, E.N., and R.R. Behn (1985): Regional Tsunami Warning System (THRUST) . In: Proceedings of the International Tsunami Symposium, Victoria, B.C., August 6-9, 1985, 94-102.  

González, F.I., E.N. Bernard, and R.R. Behn (1985): On the Chilean tsunami of March 3, 1985 . In: Proceedings of the International Tsunami Symposium, Victoria, B.C., 6-9 August, 1985, 57-61.

Hebenstreit, G.T., and E.N. Bernard (1985): Azimuthal variations in tsunami interactions with multiple-island systems . J. Geophys. Res., 90 (C2), 3353-3360.

Bernard, E.N., G.T. Hebenstreit, J.F. Lander, and P.F. Krumpe (1984): Regional tsunami warnings using satellites . In: Proceedings of the 1983 Tsunami Symposium, Hamburg, FRG, August 1983. ERL Special Report, 117-129.

Bernard, E.N. (1983): A tsunami research plan for the United States . Earthquake Engineering Research Institute, 17 , 13-26.  

González, F.I., and C.L. Rosenfeld (1983): Transformation of wave spectra at a tidal inlet . Proceedings of the 1983 International Geoscience and Remote Sensing Symposium, II , 31 August-2 September, San Francisco, CA, 5.1-5.7

Bernard, E.N., J.F. Lander, and G.T. Hebenstreit (1982): Feasibility study on mitigating tsunami hazards in the Pacific . NOAA Tech. Memo. ERL PMEL-37 (PB83-182311), 41 pp.

Pullen, P.E., and H.M. Byrne (1981): Hydrographic measurements during the 1978 Cooperative Soviet-American Tsunami Expedition . NOAA Data Report ERL PMEL-4 (PB82-215096), 168 pp.

Curtis, G.D., and H.G. Loomis (1980): A small, self-contained water level recorder for tsunami . Proc. of the IUGG Tsunami Symposium, July 1980.

Hebenstreit, G.T., E.N. Bernard, and A.C. Vastano (1980): Application of improved numerical techniques to the tsunami response of island systems . J. Phys. Oceanogr., 10 (7), 1134-1140.

Parke, M.E., and M.C. Hendershott (1980): M2, S2, K1 models of the global ocean tide on an elastic earth. Mar. Geod., 3, 379-408.

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A comprehensive review on structural tsunami countermeasures

• Review Article
• Open access
• Published: 16 May 2022
• Volume 113 , pages 1419–1449, ( 2022 )

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• Jan Oetjen   ORCID: orcid.org/0000-0002-3779-7020 1 ,
• Vallam Sundar   ORCID: orcid.org/0000-0001-7421-0543 2 ,
• Sriram Venkatachalam   ORCID: orcid.org/0000-0003-3586-9577 2 ,
• Klaus Reicherter   ORCID: orcid.org/0000-0002-9339-4488 3 ,
• Max Engel   ORCID: orcid.org/0000-0002-2271-4229 4 ,
• Holger Schüttrumpf   ORCID: orcid.org/0000-0002-0104-0499 1 &
• Sannasi Annamalaisamy Sannasiraj   ORCID: orcid.org/0000-0002-5788-6696 2  

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Tsunamis pose a substantial threat to coastal communities around the globe. To counter their effects, several hard and soft mitigation measures are applied, the choice of which essentially depends on regional expectations, historical experiences and economic capabilities. These countermeasures encompass hard measures to physically prevent tsunami impacts such as different types of seawalls or offshore breakwaters, as well as soft measures such as long-term tsunami hazard assessment, tsunami education, evacuation plans, early-warning systems or coastal afforestation. Whist hard countermeasures generally aim at reducing the inundation level and distance, soft countermeasures focus mainly on enhanced resilience and decreased vulnerability or nature-based wave impact mitigation. In this paper, the efficacy of hard countermeasures is evaluated through a comprehensive literature review. The recent large-scale tsunami events facilitate the assessment of performance characteristics of countermeasures and related damaging processes by in-situ observations. An overview and comparison of such damages and dependencies are given and new approaches for mitigating tsunami impacts are presented.

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1 Introduction

Many coastal communities are exposed to the hazards of marine flooding induced by tsunamis or storm surges resulting in adverse impacts on the coastal ecosystem and built environment. The highly destructive energy of tsunamis can cause large numbers of causalities, damages to infrastructure and affect the livelihood of coastal communities. The threat due to tsunamis intensify since the already densely populated coastal areas are experiencing further population growth as predicted by Neumann et al. ( 2015 ). From 625 million people in 2015, the population growth in low-lying coastal areas is expected to raise by 68–122% resulting in about 1052 to 1388 million people by 2060 (Neumann et al. 2015 ). The Indian Ocean Tsunami (IOT) on the Boxing Day of 2004 was the most destructive recent tsunami with about 230,000 fatalities (Telford et al. 2006 ). Apart from instantaneous destruction, tsunamis can cause medium-term impacts such as the destruction of power plants and long-term impacts such as salt-water intrusion in intensely cultivated delta plains (Villholth and Neupane 2011 ; Nakamura et al. 2017 ), which may be mitigated by hard or soft tsunami countermeasures. Although, in several potentially affected locations, authorities operate  state-of-the-art tsunami early-warning systems, the available time for alerts or the evacuation of the threatened coastal population is often insufficient and the possibility of malfunctions cannot be ruled out (UNDRR 2019 ). For instance, the 2004 IOT reached the town of Banda Aceh in northern Sumatra within 15 min after the earthquake. The danger of a malfunctioning of early-warning systems can aggravate the effects of insufficient additional countermeasures (Strunz et al. 2011 ; Bernard and Titov 2015 ; Samarasekara et al. 2017 ). For instance, in the area around Hikkaduwa City in Sri Lanka, about 47% of the residents do not trust in the functionality of the present early-warning tower since it failed during the 2012 Sumatra tsunami (Samarasekara et al. 2017 ). Furthermore, the damages to crucial infrastructure, e.g. communication, freshwater supply, industry or agriculture, are unavoidable, even through early warnings, if the structures are not designed to resist the impact of a tsunami (Palermo et al. 2011 ). Esteban et al. ( 2013 ) claim that combinations of hard and soft countermeasures (multi-layer approaches) should be promoted in tsunami-prone areas. The present review provides an overview on hard tsunami countermeasures classified as having blocking, steering or slowing character. The main body of the review is divided into three sections as projected in Fig.  1 . In order to limit the extent of the present review, soft countermeasures (e.g. vegetation belts, risk management) are not considered in detail here.

Broad classification of structural tsunami countermeasures

2 A brief overview on structural tsunami countermeasures

For protecting coastal settlements from tsunami impact, different mitigation measures are adopted depending on the regional tsunami impact assessment, tsunami awareness and economic capability. Even if existing hard tsunami mitigation measures are often effective against frequently occurring high-energy wave events (Sato 2015 ), recent tsunami events have shown that such countermeasures and their design need to be improved to withstand the impact of extreme tsunami events of unexpected magnitude in some areas. Focusing on tsunamis, such events may be divided into Level 1 and Level 2 tsunamis, where Level 1 events describe tsunamis with a return period of 50–160 years with inundation depths below 10 m while Level 2 tsunamis have a return period of hundreds to thousands of years with inundation depths above 10 m (Shibayama et al. 2013 ). As an example for a Level 2 tsunami, the 2011 Tohoku Tsunami has shown that several of the Japanese defence structures were not designed to withstand the tsunami force that unfolded during the event and that exceeded more recent historical events (Suppasri et al. 2013 ; Takagi and Bricker 2015 ; Goltz and Yamori 2020 ). However, as a highly exposed country, Japan has a long history in tsunami research (Shuto 2019 ). To the best knowledge of the authors, Matuo ( 1934 ) and Takahasi ( 1934 ) were the first to conduct laboratory experiments for examining the effectiveness of seawalls as tsunami mitigation measure. Prior to the Chile Tsunami in 1960, Japan enforced its tsunami countermeasures broadly and during the Chile Tsunami (and the Ise Bay Typhoon in the year before), the installed countermeasures proved their effectiveness. Based on this positive experience the “Chile Tsunami Special Measures Law” was revealed and floodgates and breakwaters were planned as additional countermeasures for preventing tsunami penetration into rivers and bay mouths (Shuto 2019 ). After the 2011 Tohoku Tsunami, structural and non-structural countermeasures have been reinforced again (Strusińska-Correia 2017 ).

In 1933, three months after Japan was exposed to a large tsunami, the Council on Earthquake Disaster Prevention (CEDP) of Japan released ten tsunami countermeasure rules (CEDP 1933 ; Shuto and Fujima 2009 ). In addition to the suggestions of CEDP ( 1933 ), the manuals of the National Oceanic and Atmospheric Administration of the USA (NOAA 2001 ) and UNESCO ( 2011 ) also proposed concepts for mitigation measures. In general, tsunami mitigation measures can be broadly divided into constructional, hard countermeasures, such as dikes and seawalls, and soft countermeasure encompassing nature-based solutions (e.g. coastal afforestation) and those based on the management of the tsunami impact (e.g. evacuation plans, creating public awareness), as projected in Table 1 . CEDP ( 1933 ), NOAA ( 2001 ) and UNESCO ( 2011 ) sometimes use divergent terms that describe basically the same concept or depict a subgroup of each other (e.g. relocation of dwelling houses is a subset of general retreating). Such diverging terminology is addressed in Table 1 . In this paper, only constructional hard countermeasures are considered which have been also discussed by Yamamoto et al. ( 2006 ), Kreibich et al. ( 2009 ) and Strusińska-Correia ( 2017 ), for example.

Common constructional mitigation measures (Fig.  2 ) are designed to avoid or attenuate tsunami impact on the coast and structures, by preventing direct wave impact or dissipating the tsunami impact energy. Today such measures are intended to prevent or mitigate the impact of Level 1 tsunamis. For Level 2 tsunamis, constructional countermeasures may be able to mitigate the tsunami impact to a certain extent or provide additional evacuation time. However, they may not have any mitigating effect at all for Level 2 tsunamis (PARI 2011 ; Shibayama et al. 2013 ; Goltz and Yamori 2020 ). Following UNESCO ( 2011 ) and NOAA ( 2001 ), basically three structural options for preventing/mitigating the risks of damage or loss are available:

Structural (protecting; Fig.  2 a, b, c, e).

Retreating (accommodating, Fig.  2 d).

Non-structural measures.

Basic strategies to reduce tsunami risk following NOAA ( 2001 , modified)

The countermeasures presented in Fig.  2 cannot be applied at every potentially threatened coast and, depending on the regional setting, the optimum option needs to be applied by the responsible authorities. The mitigation measures provided by the NOAA and UNESCO can significantly reduce the expectable damage exerted by an extreme coastal hazard, but certain crucial shortcomings need to be considered.

Option a) Blocking with several options an easily be implemented in a developed environment. However, the structures need to be designed to resist the loads of extreme events, and construction schemes need to be carefully planned as they are site-specific. The structures planned under this option should also allow acceptable risk. Further, uncertainties arise from possible amplifications due to reflection and redirecting of waves to unintended directions, which might happen in densely populated locations or in the vicinity of important infrastructures. The space between the protected structure (e.g. a dwelling unit) and the protection measure (e.g. the blocking wall) could function as a stilling basin, probably inducing wave oscillations between them. This effect, consequently, might lead to hydrodynamic forces on the above stated shore-based structures that are higher than for the case without protection measures. Considering Level 2 tsunamis, blocking has often shown to be an unreliable and insufficient countermeasure (e.g. Onishi 2011 ; Takagi and Bricker 2015 ). However, it is subject of research and there is debate as to whether certain measures (i.e. breakwaters) can mitigate flow velocities and heights, at least regionally (e.g. Tomita et al. 2011 ; Aldrich and Sawada 2015 ).

Option b: Avoiding is only realisable if considered during the planning phase of construction and developing an area. Following the guidelines of NOAA ( 2001 ), this option encompasses constructions above inundation levels (in fact on higher ground and/or at greater longer distance from the shore, which is preferable in undeveloped areas) or building over elevated structures such as piers or hardened podiums. However, even if avoiding might be preferred for undeveloped stretches of the coast, it is not applicable after development and therefore ineligible for subsequent enforcements of coastal areas (Cruz 2014 ). Avoiding in the sense of elevated structures can be sufficient for Level 1 tsunamis. For Level 2 tsunamis, the required construction heights will most probably exceed any reasonable cost-benefit ratio and the structural stability would still be questionable.

Option c: Steering requires more space between protected structures and the shoreline. This option may focus the flood along adjoining structures and may also be dangerous to the community due to increased flow velocities. Due to the above stated facts, this option is unsuitable for coastal areas of dense development and is not a suitable option for Level 2 tsunamis.

Option d: Retreating is, in consequence, the ultimate mitigation measure against high-energy wave impact, if the retreat area is chosen with a sufficient distance and/or height to the shoreline. However, retreating is an immense intervention for local population and is only applicable in recently affected areas or areas under initial or planned development. Most countries publish a related setback line for planning of coastal infrastructure that depends on the frequency and magnitude of the coastal hazards (Simpson et al. 2012 ; Coastal Wiki 2020 ). Retreating can avoid the impact of Level 2 tsunamis on populated areas if the distance is chosen sufficiently. However, the retreat of whole existing coastal cities or villages is not a realistic option for most of such populated areas.

Option e: Slowing is viable in areas that are already densely developed, requires lesser space and is economically feasible in most cases. Slowing the wave impact by macro-roughness elements that can act as dissipators can be adopted for reducing the wave run-up and inundation distance. However, the information on the nature, physics and effectiveness behind such dissipators is scanty till date, with no proper design guidelines in place. The main target of countermeasures aiming at slowing is Level 1 tsunamis. However, if designed in sufficient dimensions a mitigating effect may be possible in regard of Level 2 tsunamis.

3 Hard tsunami mitigation measures

3.1 general.

The two mainly adopted constructional mitigation techniques (Table 2 ) are likely the construction of continuous or detached breakwaters (Fig.  3 a), either of submerged or emerged types (blocking/slowing) or massive seawalls (Fig.  3 b, c; Mikami et al. 2015 ). Sea dikes (Fig.  3 d) are usually applied for protecting low-lying areas against flooding. The understanding of hydrodynamic processes on such structures and their mitigation capability are discussed by several authors in detail (e.g. Oshnack et al. 2009 ; Al-Faesly et al. 2012 ; Elchahal et al. 2009 ; Rahman et al. 2014 ; Mikami et al. 2015 ; Chock et al. 2016 ; Chaudhary et al. 2018 ; Ning et al. 2017 ; Sirag 2019 ; Lawrence and Nandasena 2019 ), with some authors questioning their efficacy (e.g. Nateghi et al. 2016 ). Both types of countermeasures have their own functionality, advantages and disadvantages.

Schematic setups and applications of breakwater ( a ), seawalls ( b , c ), and sea dikes ( d ), and the connected dominating forces during the impact of the initial tsunami wave

3.2 Breakwaters

Detached breakwaters (Fig.  3 a) have the original purpose to reduce beach erosion. However, the installation of multiple detached breakwaters, each of comparably small dimension, can mitigate wave impact on the shore by wave reflection and energy dissipation. Detached breakwaters are normally designed as low-crested rubble-mound structures. The comparably small height of detached breakwaters allow significant wave overtopping during storm or tsunami events. Beside detached breakwaters, non-detached breakwaters are often applied to mitigate wave impact and create tranquillity (e.g. in harbours). Breakwaters can be divided into two main types: with sloping or vertical-fronts. Another type of breakwaters, floating breakwaters, is only applied in areas of mild wave climates and are not suitable as protection against tsunami impact (Burcharth and Hughes 2003 ), and are not discussed here. The construction of breakwaters is a significant intervention in the water ecology with potentially negative impacts on the environment (Dugan et al. 2011 and references therein). Further resentments arise from the possible negative consequences on tourism (Nateghi et al. 2016 ; Reuters 2018 ).

A comprehensive overview on possible breakwater failures during tsunami impact has been reported by the National Institute for Land and Infrastructure Management, Japan (NILIM 2013a ; Raby et al. 2015 ). A key lesson from the breakwater failures in 2011 was that such failures are connected to scour on the lee side due to wave overtopping. Subsequently, it was recommended to strengthen the lee side of breakwaters by providing proper toe protection and to provide innovative crown shapes for redirecting the flow towards the sea (NILIM 2013b ; Raby et al. 2015 ). Esteban et al. ( 2009 ) conducted physical experiments on the stability of breakwaters and found that the breakwater location is a crucial parameter defining its resisting capability. In deep water, the breakwater is reported to be washed away when hit by a tsunami, while it is able to withstand the impact in shallower waters (Esteban et al. 2008a , b , 2009 ). In contrast, Hanzawa and Matsumoto ( 2012 ) described that breakwaters in shallower water are more damaged by solitary wave impact compared to breakwaters in deeper water. However, Esteban et al. ( 2015a , b and references therein) reported that the most destabilising process occurs during overflow of the breakwater and that the approach of solitary waves as destabilising event is not substantial. Hanzawa and Matsumoto ( 2015 ) have stated that detached breakwaters can reduce the run-up by 30% to 90%, when exposed to solitary waves, and that damaged breakwaters can still reduce the wave-induced pressure by about 40%. As projected in Fig.  3 , detached breakwaters and seawalls are constructed alongshore and are designed to prevent the lee side against overtopping or flooding (Burchath and Hughes 2003 ). In general, detached breakwaters serve as a coastal protection and help to redeposit lost beach substrate. However, the spacing must be carefully planned as it might lead to the generation of rip currents.

3.3 Seawalls, coastal dikes and water gates

Seawalls can either completely protect settlements from tsunami impact or extend the available time for evacuation if they are suitably designed (Samarasekara et al. 2017 ). However, they also could increase the hazard if they fail or allow overtopping (Reuters 2018 ). Obviously, seawalls avoid coastal damages if they are designed as non-overtopping structure, otherwise, they are likely to be destroyed by an extreme-wave event. Furthermore, even if seawalls have a significant potential to protect coastal areas completely against extreme-wave events, their application is expensive (Reuters 2018 ). On the other hand, seawalls can create the impression of false security leading to settlement in dangerous areas or reduced willingness or preparedness to evacuate. Nagethi et al. ( 2016 ) reported that seawalls of 5 m height in Japan lead to forced development in vulnerable areas and can subsequently result in an increased damage during extreme events.

Several designs exist for sea dikes which are mostly constructed from fine-grained materials like sand, silt and clay with surfaces of grass, asphalt, stones or concrete with or without berms (Burcharth and Hughes 2003 ). Most seawalls in Japan, as along the Minami-Sanriku coast, were designed based on the experience from historical tsunamis occurring during the past century. However, considering the regional tsunami spectrum over only short historical periods as a basis for structural countermeasures may be insufficient, as demonstrated by the 2011 Tohoku Tsunami (Kato et al. 2012 ; Goto et al. 2014 ; Strusińska-Correia 2017 ), an event with a recurrence interval of c. 500–800 years (Sawai et al. 2012 ; Goto et al. 2014 ). This example clearly shows that long-term tsunami hazard assessment integrating instrumental, historical and geological data is crucial for designing downstream hard and soft countermeasures (Weiss and Bourgeois 2012 ; Engel et al. 2020 ). Damage to coastal dikes and seawalls is connected to several processes depending on the structural design. On armoured dikes, it was observed that during the overflow scour occurred on their lee side resulting in destabilisation of the armour layer. Scour failure on the leeward toe of coastal dikes and seawalls is reported as the major failure type during the 2011 Tohoku Tsunami on tsunami countermeasures in Japan. The failure mechanism is attributed to wave overtopping and the resulting turbulent flow at the toe. With decreasing flow velocity, the acting pressure on the bed stratum decreases and coinciding with a rise in the pore-pressure gradient, the effective stress within the soil medium is reduced (Tonkin et al. 2003 ; Jayaratne et al. 2015 ). Over time, the armour is detached by the overflow enabling further removal of the dike interior (fine sediment, gravel), leading to a general malfunction of the structure (Kato et al. 2012 ). This type of failure is reported to be independent from additional seaward dike protections with artificial armour blocks like tetrapods.

Japanese seawalls were not designed considering wave overtopping as potential design criteria. Therefore, the leeward toe of the seawalls was not designed to resist destabilising erosional processes, which subsequently lead to overturning or sliding. However, even a failing seawall can possibly reduce the tsunami impact (Guler et al. 2018 ). In summary, Jayaratne et al. ( 2015 ) identified six main failure types of seawalls and sea dikes during field surveys in the aftermath of the 2011 Tohoku Tsunami, which are described in Figs. 4 and 5 . It is stated that seaward toe scour was not often observed during the 2011 Tohoku Tsunami. However, this failure mechanism may occur during the backwash of a tsunami, destabilising the seaward dike armour (Fig.  5 b; Jayaratne et al. 2015 ) as observed by Sundar et al. ( 2014 ) elsewhere. Whilst the tsunami impact on vertical walls/seawalls is broadly investigated (e.g. Asakura et al. 2003 ; Kato et al. 2012 ; Mizutani and Imamura 2000 ), the effect of preceding breakwaters is understudied as pointed out by Hanzawa and Matsumoto ( 2015 ).

Dike failure due to overflow-induced erosion. a Scouring on the landward toe, b scouring on the seaward toe, c malfunction of the landward armour and subsequent erosion, d failure of crown armour and subsequent inner erosion (modified and redrawn from Kato et al. 2012 and Jayaratne et al. 2015 )

Typical tsunami-induced seawall failure. a Landward scour leads to seawall instability. b Tsunami impact forces lead to overturning. c The backwash current after overtopping leads to seaward overturning (modified and redrawn from Jayaratne et al. 2015 )

3.4 Effectiveness of breakwaters and seawalls

3.4.1 breakwaters.

The mitigation measures prior to the 2011 Tohoku Tsunami in Japan were less effective due to the failures which were mainly caused by scour at the foundations and sliding/overturning due to hydrodynamic forces. However, even the failed structural mitigation measures are reported to have reduced the wave height and delayed the flood impact by several minutes and, thus, still saved lives (PARI 2011 ; Goltz and Yamori 2020 ).

Regarding effectiveness, breakwaters showed divergent performance during the 2011 Tohoku Tsunami. Mikami et al. ( 2015 ) investigated detached breakwaters in front of coastal dikes considering the openings between a pair of breakwaters and were unable to obtain a clear relationship between dike damages and the location of breakwater openings. They described cases in which coastal areas on the lee side of breakwaters were clearly protected compared to areas behind the openings. Subsequent experimental investigations indicated an effective breakwater application with a low ratio of the gap between the breakwaters and its distance from the shore (Mikami et al. 2015 ). However, the world’s largest breakwater in Kamaishi (Japan) failed during the Tohoku Tsunami 2011 resulting in massive damage. Furthermore, the Kamaishi breakwater is suspected to have even increased the tsunami damage due to wave deflection (Onishi 2011 ). Aldrich and Sawada ( 2015 ) concluded that the Kamaishi breakwater was not able to provide any protection to the adjacent town. In contrary, Tomita et al. ( 2011 ) have stated that the breakwater was able to reduce the flow velocity and height significantly and provided additional evacuation time (see also Nagheti et al. 2016 ).

The possibility of increased damage due to insufficiently designed countermeasures of any type (barriers, water gates, tree belts) is also indicated by the tsunami impact at Iwaizumi, Iwate prefecture (Japan) (Ogasawara et al. 2012 ). Takagi and Bricker ( 2015 ) analysed breakwater failures during the 2011 Tohoku Tsunami numerically and revealed that a breakwater of width below 8 m always suffered damage if the wave height exceeded 14 m. Furthermore, no damage was found at breakwaters broader than 14 m if the tsunami height was below 6 m. In contrary to the overall relations, Takagi and Bricker ( 2015 ) were not able to identify significant wave reductions behind the Ishinomaki breakwater (armour block height of 7.00 m above low-water level) and attributed this to the comparably wide openings between the breakwaters. Subsequently, the tsunami was able to enter through these gaps, with an accelerated flow. The case of the Ishinomaki breakwater shows that the use of “permeability” (compare paragraph 5.1) needs to be handled carefully and high attention needs to be paid to the ratio between openings and blocking elements (breakwater elements). However, the numerical investigations were based on 2D simulations with the Delft3D numerical modelling suite. Due to the two-dimensional simulation, vertical velocities and force transfers are neglected. Hence, the simulation suffered from some crucial shortcomings (Bricker et al. 2013 ; Takagi and Bricker 2015 ):

Neglecting vertical motions can result in an enhanced fluid energy.

The shallow nearshore bathymetry enforces the emergence of bores. This process is not resolved by the horizontal 2D model.

For better understanding of the processes acting during the impact of the 2011 Tohoku Tsunami, completely three-dimensional numerical models based on sufficiently fine meshes (as recommended in Takagi and Bricker 2015 ) or even conducted by meshless methods, could be an option if the computational costs can be reduced to permit a practical application.

3.4.2 Seawalls

Based on their post-tsunami surveys, Sundar et al. ( 2014 ) and Sundar and Sannasiraj ( 2018 ) showed that the seawall constructed over a length of about 300 km along the coast of Kerala at the southwest coast of the Indian peninsula was damaged at several locations mainly due to significant overtopping at lower crest elevations, not only during the 2004 IOT but also during storm-wave run-up.

During the 2011 Tohoku Tsunami, the Noda Bay (Noda village, Japan) was protected by two seawall lines (concrete-covered and buttress type) of 10.3 m and 12.0 m height above sea level and a length of 875 m and 380 m, respectively (Ogasawara et al. 2012 ). Ogasawara et al. ( 2012 ) observed that the additional water gates shielding the three rivers crossing Noda village (Myonai, Ube, Izumisawa) were significantly damaged during the tsunami. The seawalls showed differential effectiveness. While the 12.0 m high seawall did not break at all and only the landward slope was eroded, the 10.3 m high seawall did break. The additionally installed natural barrier of pine trees was also not able to withstand the tsunami. Trunks were broken and trees were washed away causing additional damage to many buildings. In Iwaizumi town the present seawall (design tsunami height 13.3 m) and river water gate were overtopped by the tsunami. Furthermore, both, left and right, riverbanks at the water gate were overtopped resulting in large damages to many houses in the lower areas. In contrary, in Fudai village the installed countermeasures showed a good performance during the same tsunami. Even if the present water gate (15.5 m high) was overflowed during the tsunami, the gate did not fail which is addressed to its design: The Fudai water gate and seawall are connected to the adjacent mountains providing additional stability to the structure (Ogasawara et al. 2012 ). In Taro town, an X-shaped seawall system of 10 m height existed before the tsunami of 2011, but its effectiveness is questionable (Yamashita 2003 ; Ogasawara et al. 2012 ; Tachibana 2015 ). Based on in-situ observations, Tachibana ( 2015 ) was unable to finally determine if the seawalls in Taro town even influenced the inundation pattern. Only for the western edge of the seawalls, the flow direction was influenced notably. It is finally concluded that the seawalls were likely not reducing the damage in Taro, overall.

The uncertainties in the design of structural countermeasures against tsunamis are widely reported in literature and the research community agrees that existing design guidelines (e.g. for breakwaters or seawalls) need to be revised based on the observations of recent tsunami events and that additional advanced mitigation techniques (e.g. combined techniques, systematic plantation) are needed in order to be better prepared for future events (Rahman et al. 2014 ; Suppasri et al. 2016 ). In particular, the need for a better understanding of the interaction of tsunamis with countermeasures during the phases of wave impact, flooding and possible backflows has been highlighted (e.g. Palermo and Nistor 2008 ; Macabuag et al. 2018 ). Nevertheless, the 2011 Tohoku Tsunami has led to considerable insights into the functionality and effectiveness of breakwaters as tsunami mitigation measure (e.g. Mimura et al. 2011 ; Takahashi et al. 2014 ; Mikami et al. 2015 ; Raby et al. 2015 ; Sozdinler et al. 2015 ; Suppasri et al. 2016 ).

3.5 Comparison between seawalls and breakwaters

A summary on the advantages and disadvantages of seawalls and offshore detached breakwaters as coastal protection and tsunami mitigation measures are discussed below.

3.5.1 Seawalls

Seawalls act as mitigation measure against flooding and coastal erosion. Their benefits are: Prevention of hinterland erosion, increased security for property from flooding, physical barrier between land and sea, increased perceived security of local people and maintenance of hinterland value. However, crucial shortcomings are adverse impacts on fronting beaches up to a total loss of them, interruption of longshore sediment movement, disturbance of sediment budgets and coastal ecosystems, increased erosion down drift (terminal scour), and freezing the coast and thus preventing its response to recent and future sea-level rise. The recommended usage of seawalls is to protect high-value hinterland development and to increase and protect amenity usage where other solutions are not suitable. Questions remain, however, regarding overtopping and run-up particularly during tsunamis.

3.5.2 Offshore breakwaters

The benefits of construction of offshore breakwaters as mitigation measures against tsunamis are reduction in wave activity received at the coast, increased sedimentation and beach formation, reduction of flood risk due to wave overtopping at the coast, reduction in sediment loss through rip-cell activity, formation of new “reef” ecosystems and increased biodiversity. Whereas the problems associated with offshore breakwaters are possible deflection and modification of longshore currents, high construction and maintenance costs, possible scour problems through gaps in segmented breakwaters and retention of sediment with corresponding increased erosion elsewhere along the coast. The usage of offshore breakwaters is recommended in: Coastal areas experiencing erosion because of wave activity and excessive sediment loss by shore normal currents, and where sediment build up would enhance coastal resilience.

4 Integrated and combined approaches

4.1 integrated mitigation measures.

Beside structures designed solely as mitigation measure against coastal erosion or tsunamis, they can also be integrated as part of infrastructure constructions. At the coast of Banda Aceh (Indonesia), it is proposed to construct a circuit road (Banda Aceh Outer Ring Road; BORR) intended to also act as tsunami mitigation measure (Syamsidik et al. 2019 ). During the 2004 IOT, the maximum tsunami height in Banda Aceh is estimated to be 15 m (Lavigne et al. 2009 ) and its impact resulted in a death toll of about 26,000 (Doocy et al. 2007 ). The BORR is planned to be constructed as an elevated road (3 m) as shown in Fig.  6 to act as a mitigation measure and shall be located behind the shoreline in Banda Aceh. Syamsidik et al. ( 2019 ) showed that the construction of the BORR may reduce the area of inundation by 8–22%, depending on the tsunami intensity, but also point to the possibility of damage (e.g. due to breaching) which needs to be examined further.

Intended course of the elevated road in Banda Aceh. Left: Consequences of the 2004 IOT in Banda Aceh (Satellite data composite from Maxar Technologies accessed through Google Earth Pro, vers. 7.3.4.8248)

Samarasekara et al. ( 2017 ) discussed the reinforcement of an existing railway embankment as an additional tsunami countermeasure in the two coastal villages Dimbuldooa and Wenamulla in Sri Lanka. While they clearly found a tsunami-mitigating effect by enhancing the present rail embankments, the expected benefit (protected goods) seems to not compensate the anticipated costs.

4.2 Alternative approaches

4.2.1 multi-layer approach.

Several studies address multi-layer approaches (sometimes referred to as multi-layer safety) regarding tsunami impact mitigation (Fig.  7 ). This approach has received greater interest after the 2011 Tohoku Tsunami (Tsimopoulou et al. 2015 ; Samarasekara et al. 2017 ). Both studies referred to the National Water Plan of the Netherlands 2009–2015, which is explained in detail by Hoss et al. ( 2011 ). The Dutch multi-layer approach encompasses three main components:

Layer 1 as prevention that encompasses all measures focussing on preventing floods (e.g. seawalls).

Layer 2 as spatial solution addresses the spatial planning of areas and buildings in flood threated areas.

Layer 3 as emergency management that focusses on the hazard management in terms of hazard awareness among the population, evacuation plans or early-warning systems (Hoss et al. 2011 ; Esteban et al. 2013 ).

A schematic view of the multi-layer approach. Layer 1: Prevention (e.g. by offshore breakwaters or seawalls). Layer 2: Spatial planning (e.g. creating retention areas or lifted structures with porous structures). Layer 3: Management (e.g. evacuation plans, early-warning systems) (modified and redrawn from Tsimopoulou et al. 2013 , 2015 )

The application of multi-layer or prioritisation of a particular layer depends on the region and country. In developing countries, single-mitigation measures are often preferred since they are economically more feasible. In developed countries on the other hand, more financial resources are available and, additionally, the assets at risk are economically more valuable. This leads to more comprehensive mitigation measures, for instance, in Japan (Esteban et al. 2013 ). In general, multi-layer approaches are considered as a parallel system instead of a serial system. This means, if one of the three layers fails, the remaining layers still provide mitigation (Jongejan et al. 2012 ; Tsimopoulou et al. 2013 ). However, in the case of tsunami mitigation, this is not entirely valid since a failure of Layer 1 (e.g. a seawall) may cause additional damage. Tsimopoulou et al. ( 2013 ) illustrated this by referring to a dike-ring area in the Netherlands. If the probability of a failure of an evacuation plan (Layer 3) is higher than the possibility for a dike failure (Layer 1), the synergy between Layer 1 and Layer 3 diminishes and the costs for establishing the evacuation plan may surpass its expected benefit (Tsimopoulou et al. 2013 ). Furthermore, in the case of Layers 2 and 3 a threshold for the accepted damage in case of a hazard needs to be defined, determining the boundary conditions for these layers (e.g. settlement retreat from the coast; Layer 2) (Tsimopoulou et al. 2013 ).

In Tohoku region, a multi-layer mitigation approach already existed prior to the 2011 tsunami. However, it is not clear to what extent the approach was strategically planned and coordinated by local authorities or if it was implemented rather unintentionally/accidentally. In fact, the system failed in 2011 starting from the breakdown of most of the Level 1 measures (breakwaters, seawalls). On Layer 2, the early-warning system did respond and provided warning only three minutes after the earthquake, but the local emergency plans were not prepared for such an intense tsunami. Even some evacuation buildings were partially overtopped, while, in the low-lying areas people did not reach them in time (Tsimopoulou et al. 2013 ). Based on the analysis of Tsimopoulou et al. ( 2015 ) in Tohoku, it is recommended to elaborate risk-based multi-layer approaches based on damage and casualty thresholds determining the point of “failure” of a layer. Such an approach would provide additional protection in a multi-layer system. Furthermore, the authors emphasised the importance of tsunami awareness among the population for a functional multi-layer safety approach, based on a case study in the city of Rikuzentakata (Iwate Prefecture, Japan; Tsimopoulou et al. 2015 ).

4.2.2 Channels and dug pools

The Buckingham Canal along the city of Chennai situated along the southeast of India is a 30 m wide, 10 m deep and 310 km long channel flowing at a distance of 1 to 2 km parallel to the shoreline. In the area between Buckingham Canal and the shoreline, hamlets inhabited by several thousands of fishermen are located. During the 2004 IOT, the canal preserved elevated patches in this area from tsunami damage since the tsunami run-up approached and filled the canal at first, which then acted as an additional buffer zone (Rao 2005 ). The canal regulated the run-up back to the sea within 10 to 15 min. From this observation, Rao ( 2005 ) suggested investigating the influence of channels on tsunami run-up scientifically by considering further geomorphologic features and coastal inlets. Furthermore, Dao et al. ( 2013 ), Usman et al. ( 2014 ) and Rahman et al. ( 2017 ) investigated the application of channels and depressions as tsunami countermeasures both experimentally and numerically.

Dao et al. ( 2013 ) investigated the Kita-Teizan Canal in Sendai (Japan) numerically, which is assumed to have mitigated the impact of the 2011 Tohoku Tsunami significantly (Tokida and Tanimoto 2012 ). The Kita-Teizan Canal is a 9 km long canal running parallel to the shoreline at a distance of about 300 m to 400 m and is 40 m wide and 2 m deep. By several setups with and without the canal as well as different canal dimensions, the canal is found to be capable of reducing the tsunami energy significantly and its effectiveness would increase by greater width and depth. The canal effectiveness in terms of reducing tsunami overland flow velocity is reported to vary from about 13% to 20% during the 2011 Tohoku Tsunami. By applying fragility curves (Gokon et al. 2011 ) for structures, Dao et al. ( 2013 ) furthermore assumed that the canal’s contribution corresponds to a reduction of structural damage of 3–4%.

Rahman et al. ( 2017 ) studied different configurations of canal dimensions (width, depth) and additional countermeasures (dunes) for tsunami mitigation and identified a combined approach to be most promising. In general, the canal of largest dimensions (depth, width) showed the best mitigation performance. Flat but wide canals showed high wave reflections. However, all tested canals had a considerable mitigation effect in terms of reduced tsunami velocity and delayed tsunami flooding. Even though shore-parallel canals were capable to reduce the energy of the tsunami impact, there was no influence on inundation depth. The combination of sand dunes and a canal reduced both inundation depth and flow velocity (Rahman et al. 2017 ). Further studies on canal geometries as well as combinations of canals and traditional countermeasures for tsunami mitigation were suggested.

The mitigation function of canals, channels or dug pools was accidentally identified and also today such structures are not planned by intention. However, based on the experiences of the 2004 IOT and 2001 Tohoku Tsunami, the interest in understanding the associated hydrodynamic processes and elaborating quantifiable mitigation potentials of such structures is increasing.

4.2.3 Vertical evacuation

Structures for vertical evacuation could be considered both as hard and as soft tsunami countermeasures. However, in areas without natural high grounds as evacuation space, the construction of artificial structures is an option for shortening evacuation distances. These structures might further be divided into those originally designed as evacuation shelters or those constructed for other purposes (e.g. parking garages, hotels, etc.). However, if existing buildings are assigned as evacuation location, their stability against tsunami impact and the accessibility needs to be ensured (Goltz and Yamori 2020 ). The construction of elevated or high grounds as evacuation sites is another option for designed vertical evacuation space. Such high grounds are suggested by the Federal Emergency Management Agency of the US (FEMA 2019 ) as comparably cost-effective structure for vertical evacuation compared to stand-alone structures or buildings. A provision of bottom clearance to the building by using continuous stilts was found to reduce the pressure impulse of the order of 20% to 30% through numerical and experimental investigations (Sannasiraj and Yeh 2011 ). However, beyond a certain elevation extent, the clearance may not yield further reduction of the impact.

5 Future directions

5.1 use of permeability.

Mitigation structures of staggered non-continuous configurations lead to a reduction in the hydrostatic and hydrodynamic stresses during the initial wave impact, ongoing wave penetration and backflow. Recent research proves the linkage between hydrodynamic loads of tsunamis and the permeability of coastal structures, e.g. in terms of opened or closed windows. In all of these studies (e.g. Thusyanthan and Madabhushi 2008 ; Wilson et al. 2009 ; Lukkunaprasit et al. 2009 ; Triatmadja and Nurhasanah 2012 ), authors confirmed the effect of solid or elastic structures in combination with openings that permit free flow and provide energy dissipation. Lukkunaprasit et al. ( 2009 ), and independently Wilson et al. ( 2009 ), found that opening a structure of 25% and 50% reduces the hydrodynamic force by 15–25% and 30–40%, respectively. Such low or no-resistance mitigation measures (which are based on the idea of least resistance) should be based on openings in buildings as large as possible, or the implementation of weak and non-stability-supporting elements in the building in order to provide a calculated path for the flow that does not affect the stability of the building (ASCE 2017 ). An increase in the permeability of coastal buildings increases their stability, but the buildings will still be affected by flooding, and the final success is highly depending on the existing structure strength. Increasing the permeability of existing structures (e.g. open windows, doors, etc.) is a reasonable approach in order to mitigate the worst case and should be the last mitigation option since certain types of mid-term and long-term damages (in particular regarding crucial infrastructure or flood-caused diseases) may not be prevented. For tsunami-prone areas, it is strongly recommended to leave sufficient space between ground level and the floor level of dwelling units. For critical installations, such as power plants, adequate caution should be taken by locating the sensitive components at high grounds to avoid any tsunami flooding.

5.2 Slowing by artificial elements (buffer blocks)

As explained in the previous paragraph, the use of permeability in tsunami mitigation measures is a promising approach. The main purpose of such constructions is to dissipate the impact energy and, therefore, also to reduce tsunami height. Permeable structures generate additional turbulences in the flow field, while they are not designed to resist the full wave impact energy. The dissipation results from the flow through the elements on both sides and over its top. Basically, the concept is comparable to the increased roughness provided by vegetation which is intensely studied (e.g. Shuto 1987 ; Kathiresan and Rajendran 2005 , 2006 ; Olwig et al. 2007 ; Iverson and Prasad 2008 ; Tanaka 2007 , 2010 ; Baird and Kerr 2008 ; Yanagisawa et al. 2009 ; Sundar et al. 2011 ; Noarayanan et al. 2012 , 2013 ; Strusińska-Correia et al. 2013 ; Nateghi et al. 2016 ).

Until now, and related to mitigating storm surges, buffer blocks have been adopted as roughness elements over dikes and as space-saving reinforcement measure to existing dikes in order to enhance energy dissipation of overflow as shown in Fig.  8 (Oumeraci 2009 ; Hunt-Raby et al. 2010 ; EurOtop 2018 ).

View on small buffer blocks attached to coastal dikes (foreground) and large buffer blocks as mitigation measure against storm waves (photograph by Schüttrumpf, 2003)

Although such buffer blocks are not applied as countermeasure against tsunamis so far, their general applicability as tsunami mitigation measure is discussed by several authors (e.g. Oumeraci 2009 ; Thorenz and Blum 2011 ; Goseberg 2011 , 2013 ; Rahman et al. 2014 ; Capel 2015 ). In his flume experiments, Goseberg ( 2011 , 2013 ) showed that macro-roughness elements have a significant effect on the run-up height of non-breaking long waves mainly depending on element configuration (aligned, staggered) and wave direction. Goseberg ( 2011 , 2013 ) focussed on the run-up reduction due to the presence of macro-roughness elements as buildings (referred to as coastal urban settlements), which are not fully submerged during the run-up, but did not consider the force reduction behind the macro-roughness elements (Fig.  9 ). The run-up reduction was mainly addressed towards momentum exchanges within the wave during the overflow of the macro-roughness elements, leading to the generation of higher turbulences. These preliminary findings support the use of buffer blocks for tsunami mitigation (Goseberg 2011 , 2013 ). Similarly, Giridhar and Muni Reddy ( 2015 ) investigated the effect of different shapes of buffer blocks (rectangular, semi-circular, trapezoidal) installed over sloped structures to assess their effectiveness in the reduction of wave run-up and reflection. Rahman et al. ( 2014 , 2017 ) investigated the performance of continuous seawalls of two different heights and one perforated seawall regarding wave impact attenuation. A dam-break setup and a load cell for investigating the bore impact were used. The load cell was installed behind the seawall to gain insights into the mitigation characteristics of these structures (Fig.  9 ). For continuous seawalls, the performance of higher seawalls built closer to a structure of interest led to the highest impact-force reduction on the structure of interest. Nevertheless, the perforated seawall exhibited a reduction in wave height and force of about 35% compared to no protection. Furthermore, the perforated seawall allows overtopping and backflow into the sea, resulting in decreased forces acting on nearby structures. The perforated seawall had the same total height as the continuous sea wall (8 cm) but is divided into a lower continuous section (3.8 cm height) and an upper discontinuous section (elements of 4.2 cm height). This results in material savings to an extent of about 25% with good attenuation characteristics.

Simplified sketch of the laboratory experiments of Goseberg ( 2011 , 2013 ; redrawn) and Rahman et al. ( 2014 ; redrawn). Goseberg ( 2013 ) shows that buffer elements can reduce the run-up significantly. In Rahman et al. ( 2014 ) the continuous seawall leads to a force reduction of 41% in the experiments, while the perforated seawall reduces the impact force by 35% of the case without any protection of the load cell

5.3 Recurved seawalls

Recurved seawalls (also recurved parapet walls) or breakwaters are occasionally applied as storm-wave countermeasure (Fig.  10 ). Their application as tsunami countermeasure is not common and, to the best knowledge of the authors, no publication addressing tsunamis is available beside a patent application of Igawa ( 2012 , Fig.  10 c).

Recurved seawalls on a mounted breakwater ( a ) and as a coastal seawall ( b ). c Approach of Igawa ( 2012 ) which aims at more controlled flow redirection

Anand et al. ( 2011 ) compared the hydrodynamic characteristics of seawall profiles and found the lowest reflections for circular cum parabola shapes (CPS) followed by Galveston wall shapes (GS). The CPS shape mentioned in the patent of Weber ( 1934 ) consists of a smooth parabola to gently guide the incoming waves to the quadrant circle at the top that redirects the waves back to the seaside. The Galveston wall shape (GS) consisting of two radii of curvature has been earlier adopted as a seawall at Galveston, Texas, USA (Anand et al. 2011 ).

Molines et al. ( 2019 , 2020 ) investigated mound breakwaters enforced with parapet walls regarding wave forces by flume experiments and numerical simulations using OpenFOAM. They have reported that the horizontal wave forces increase by a factor of 2 compared to standard vertical wall breakwaters. However, they showed that curved crowns are able to reduce wave overtopping significantly until the impact discharge is too high. Then, no further significant influence of the curved parapet on wave overtopping was observed.

Castellino et al. ( 2018 ) conducted two-dimensional numerical investigations on the interactions between curved seawalls and impulsive forces. It was shown that the hydrodynamic pressures due to non-breaking waves increase significantly on a larger portion of the fully submerged recurved parapet wall. A high influence on the impact forces is attributed to the opening angle of the curve. Investigations on the correlation between wave period and wave impact on the curved seawall crest show that the wave load increases with wave steepness (Castellino et al. 2018 ).

Martinelli et al. (2018) investigated the loads  of non-breaking waves on a recurved parapet with different exit angles. They reported “partially recurved parapets” with exit angles of 60° to be a good compromise between the reduction of forces and overtopping. Ravindar et al. ( 2019 ) studied the characteristics of wave impact on vertical walls with recurve in large scale and analyse the variation of impact pressure. Stagonas et al. ( 2020 ) compared the impact forces on three types of recurves based on large-scale experiments and found that the mean of the largest peak force increases with an increasing angle of curvature. Recently, Ravindar and Sriram ( 2021 ) reported on the influence of three recurved and plain parapets on the top of vertical walls. It was concluded that large parapets seem to be most effective in the reduction of forces for higher waves compared to other parapet types.

5.4 Large tsunami barrier

Scheel ( 2014a , b , c ) proposed a novel tsunami countermeasure based on shoaling processes and preventing steepening of waves in the nearshore (Fig.  11 ). The idea is based on reflecting the wave motion by a submerged vertical wall in front of the shoreline. The vertical wall needs to be placed up to several tens of kilometres offshore at a depth between 20 and 200 m (Scheel 2014b ) or 50 m and 500 m (Scheel 2014a ), respectively. The crest is equipped with an extending wall of 6 m to 8 m on top of the vertical wall. To avoid wave reflections, Scheel ( 2014a , b , c ) suggested a slight inclination in the wall, irregular shapes or optimised surface roughness for introducing wave distraction to the reflected wave. Scheel ( 2014a , b , c ) acknowledged the large financial and material demands of this measure and proposed to reclaim the space between wall and shoreline as additional land. This type of measure could be considered for protecting crucial installations that cannot easily be protected or relocated, or which pose a hazard themselves in the event of a collapse, such as nuclear power plants. However, scour could be a serious problem if not properly addressed. As another option, Scheel ( 2014a , b , c ) recommended constructing not one single deep vertical wall but to implement several smaller walls for reducing the costs (Fig.  11 c).

Tsunami countermeasure after Scheel ( 2014a , b , c ). a No tsunami countermeasures. b Tsunami barrier in large distance to the high-tide line (dashed line). c Fragmentation of the barrier into several sub-terraces in order to save material and costs (redrawn and extended after Scheel 2014a , b , c )

Furthermore, Scheel ( 2014b ) suggested combining the tsunami countermeasure with hydro-power plants. Here, the vertical wall would be equipped with turbines driven by the tidal current. Alternatively, the space between the vertical walls is proposed to be used for fish farming (Scheel 2014a , b , c ). A numerical study by Elsafti et al. ( 2017 ) revealed that such a barrier is effective in reducing the tsunami energy significantly before reaching the shoreline. However, at the wall, the run-up height increases more than twice the height of the approaching tsunami, and the influence of the face-roughness of the barrier has only minor influence on wave run-up and reflection. The approach of Scheel ( 2014a , b , c ) seems to have not been validated or tested physically so far. Furthermore, the construction of such countermeasures would require substantial fundamental research not only on the hydrodynamic characteristics and design but also on the construction sequence and procedure, which might require further innovations (Scheel 2014b ). A further adverse effect would be imposed on the ecology of shallow marine environments around and behind these barriers.

6 Discussion

The review revealed that a range of hard countermeasures for mitigating tsunami impact exist, but that they also need a critical evaluation prior to installation. In most cases, the local environmental, social and financial factors determine the technique to be adopted. Hard structural measures like dykes, seawalls or breakwaters have high construction costs and can provide a false feeling of security which might even increase the structural damage and fatalities if they fail during tsunami impact. Due to the known disadvantages of seawalls and dykes (Sect.  3.5 ), further developments in the field of structural tsunami countermeasures are necessary, some of which are summarised in Table  3 .

Despite breakwaters and seawalls do have disadvantages, a re-design of such structures (e.g. by raising their crest elevation or applying recurved parapets) can, at least marginally, increase their efficacy during the tsunami ingress. On the other hand, physical and numerical investigations show that hydrodynamic forces acting on the walls increase significantly due to the recurved parapet. Based on the high hydrodynamic energy of tsunamis, it is questionable how reliable such recurved seawalls in dimensions sufficiently high for large tsunamis would be (i.e. if they are reasonable applicable for Level 2 tsunamis). Furthermore, this would involve a huge financial investment; a decision would depend on the local frequency–magnitude pattern of tsunamis, the value of assets, as outlined by Stein and Stein ( 2013 ), and of course the vulnerability of the coastal population. In any case should their dimensions be large enough to reduce the tsunami inundation levels. However, with regard to the perennial problems of coastal erosion, today's breakwaters and seawalls may serve their purpose.

The application of artificial slowing elements (buffer blocks) could be effective as they are easier to install and can serve as buffers in reducing the tsunami inundation. Their general applicability is already proven against storm waves along the coast of  Norderney Island, Germany (Schüttrumpf et al. 2002 ). Such buffer blocks might also be highly useful as (supportive) countermeasure for tsunamis if their dimensions are derived from detailed scientific investigations. Extended investigations are also necessary to determine whether the buffer block approach may also be suitable for Level 2 tsunamis.

Recently integrated tsunami mitigation measures are considered as a practical solution. The reinforcement of existing or construction of combined structures might be a useful alternative especially in regions where financial resources for countermeasures are limited. Especially elevated roads or railway embankments can be suitable options, as in the case of Banda Aceh. Channels and dug pools might also be considered as further integrated mitigation measures. Recent investigations show that channels and topographic depressions are capable to mitigate tsunami run-up and, depending on their arrangement, to steer the backflow to the open sea in a more controlled way. The application of such integrated countermeasures needs to be investigated further and more systematically in terms of sufficient dimensions, integration into the coastal ecosystems and tourism, and economic questions. Nevertheless, the application of channels/topographic depressions would always divide the coastal area into a more and less protected part. Therefore, their application might be combined with a first defence line of breakwaters, seawalls, buffer blocks or vegetation belts. The separation of the coastal area into more and less protected parts, needs to be combined with specifically adapted land use in the flood-prone areas.

The combination of topographic depressions and hard structural countermeasures results in multi-layer approaches. If Layer 1 (e.g. seawalls) fails, Layer 2 (e.g. topographic depressions) will still provide attenuation. However, the failure of the first defence line would lead to additional damage in the area between seawall and depression, while Layer 2 (topographic depression) would prevent areas on its lee side from higher damages. Herein, Layer 3 (emergency management) would act in combination with Layer 2 since the functionality of Layer 2 would highly depend on timely evacuation of the area between Layer 1 (seawall) and 2 (depression). However, as stated by Tsimopoulou et al. ( 2013 ), the Dutch multi-layer approach has to be adjusted in order to be suitable for combating other types of high-energy wave impacts such as tsunamis. A great deal of research on this topic is recommended since none of the presented mitigation measures can serve as an overall valid and completely successful mitigation technique on its own. Furthermore, multi-layer approaches can also be a promising option regarding Level 2 tsunamis if Layers 1 and 2 are considered as “failable” layers which provide additional time for evacuation.

Completely novel approaches of tsunami countermeasures are rare, which might be due to the complexity of the hydrodynamic processes and the low predictability of tsunami occurrence and intensity. Connected to the unpredictability of tsunamis, test applications of novel approaches are not easy to implement. Test areas need to be selected carefully. Whether the selected area will be affected by a tsunami within a manageable period is not predictable. On the other hand, if the effectiveness of such measures cannot be fully proven by numerical or experimental investigations, a remnant risk is associated to the application in populated areas. This might hamper the development and implementation of new approaches.

As stated earlier, a novel tsunami barrier which is based on the idea of preventing a tsunami from shoaling and reducing its impact energy and run-up was proposed by Scheel ( 2014a , b , c ) and Elsafti et al. ( 2017 ). It is at concept stage and substantial research through experimental and numerical investigations as well as trials in the field are required to prove its efficacy. A large amount of economic, material and labour resources would be needed for construction and the (most probably very high) ecological impact is unforeseeable.

The available literature mostly concentrates on failed countermeasures. Naturally, resisting and successful countermeasures do not receive as much attention. Therefore, we encourage to include also successful tsunami countermeasures in future research studies in order to raise datasets showing dependencies between countermeasure type, design and dimensions, and the tsunami impact. Such data would enable authorities and other persons in charge at affected coasts to better evaluate their hazard management. Furthermore, such reviews would highly benefit from preferably comprehensive datasets encompassing data for the tsunami intensity and properties, countermeasure design (dimensions, material, vegetation type, soil type, etc.) and coastal topography and bathymetry. Elaborating such datasets and corresponding correlations would help to increase the planning security at threatened coasts.

As further support to tsunami mitigation, researchers started to utilise tsunami deposits for reconstructing the energy of palaeotsunamis, over the last three decades (Etienne et al. 2011 ; Engel and May 2012 ; Vött et al. 2013 ; Sugawara et al. 2014 ; Costa and Andrade 2020 ; Oetjen et al. 2020 ). Knowledge on palaeotsunamis can help to successfully improve regional specific tsunami countermeasure programmes since they allow to extend the scale of known events to several thousands of years and lead, subsequently, to an increased prepardeness and awareness of possible tsunami events and their energy and flooding potential.

This review shows that tsunami mitigation measures are a broad research field of high interest. Recent destructive tsunamis intensified the research interest further since tsunami hazards can result in enormous damages and fatalities. Past tsunamis show that it is dangerous to base tsunami mitigation on only one layer since its failure highly likely results in disastrous hazards. For establishing new approaches and enhancing existing countermeasures, broad datasets can support researchers in adjusting mitigation measures to specific regional areas, e.g. in terms of land use and topography and expectable tsunami impacts. This requires close collaborations between different scientific disciplines (e.g. engineers, geologists, geographers, sociologists) since knowledge on construction, seismology, palaeotsunamis, and regional social-economic and cultural properties highly determine the success of local mitigation measures and connected management plans.

Regarding the hard countermeasures only, a combination of blocking (e.g. seawalls), slowing (e.g. vegetation, buffer blocks) and steering structures (e.g. channels, topographic depressions) that considers long-term tsunami hazard, people and assets at risk, financial resources and the coastal configuration at a local scale is considered most promising. However, it should always be considered that tsunami mitigation measures as a whole can never provide a safety level of 100%, as there is an upper limit of mitigation investment depending on the assets at risk (Stein and Stein 2013 ) and the magnitude of future tsunamis is still difficult to assess.

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This contribution received funding by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) for the project “Modelling tsunami-induced coarse-clast transport—combination of physical experiments, advanced numerical modelling and field observations” (SCHU 1054/7-1, EN 977/3-1). Further, we would like to acknowledge funding from the Department of Science & Technology, India, Grant No. DST/CCP/CoE/141/2018C under SPLICE—Climate Change Programme.

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Jan Oetjen & Holger Schüttrumpf

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Vallam Sundar, Sriram Venkatachalam & Sannasi Annamalaisamy Sannasiraj

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Oetjen, J., Sundar, V., Venkatachalam, S. et al. A comprehensive review on structural tsunami countermeasures. Nat Hazards 113 , 1419–1449 (2022). https://doi.org/10.1007/s11069-022-05367-y

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DOI : https://doi.org/10.1007/s11069-022-05367-y

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