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Abstract. Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake). Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the outer product emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the leave-one-out method.

This paper presents the first end-to-end example of a risk model for loss of assets in households due to possible future tsunamis. There is a significant need for Government to assess the generic risk to buildings, and the concrete impact on the full range of assets of households, including the ones that are key to livelihoods such as agricultural land, fishing boats, livestock and equipment. Our approach relies on the Oasis Loss Modelling Framework to integrate hazard and risk. We first generate 25 representative events of tsunamigenic earthquakes off the Southern coast of Java, Indonesia. We then create a new vulnerability function based upon the Indonesian household survey STAR1 of how much assets have been reduced in each household after the 2004 tsunami. We run a multinomial logit regression to precisely allocate the probabilistic impacts to bins that correspond with levels of financial reduction in assets. We focus on the town of Cilacap for which we build loss exceedance curves, which represent the financial losses that may be exceeded at a range of future timelines, using future tsunami inundations over a surveyed layout and value of assets over the city. Our loss calculations show that losses increase sharply, especially for events with return periods beyond 250 years. These series of computations will allow more accurate investigations of impacts on livelihoods and thus will help design mitigation strategies as well as policies to minimize suffering from tsunamis. ; Lloyd's Tercentenary Research Foundation; Lighthill Risk Network; Alan Turing Institute project "Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models", EPSRC EP/N510129/1; Royal Society, the United Kingdom CHL/R1/180173.

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. © 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 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).