Reply on RC2

volume that occurred both with a subaerial and a submarine component is mostly consistent with the observed and modelled runup heights at the adjacent shores. Similar models also exist for the 1883 tsunami at Krakatau, with the main purpose being the identification of its generation mechanism (Maeno and Imamura 2011) and how such a tsunami propagates in the far-field (Choi et al. 2003). Predictive studies only considering possible future events are not as abundant, but have been done for Anak Krakatau before the 2018 tsunami (Giachetti et al. 2012; Badriana et al. 2017), with Giachetti et al. (2012) making a remarkably close prediction to the later event. Other volcanoes in Southeast Asia are not as commonly considered. Pranantyo et al. (2021) test the tsunami propagation from Ruang volcano, Indonesia, using and comparing both historical observations and data from the 2018 Anak Krakatau event and reproducing a 25 m runup in the near-field. In Papua New Guinea numerical tsunami models have almost exclusively been considered for the Ritter Island tsunami in 1888 and the reconstruction of its generation (Ward and Day 2003; Karstens et al. 2020). Similarly, numerical tsunami models in the Philippines are mostly limited to Taal volcano, where models are based both on a past tsunami in 1716 (Pakosung et al. 2020) and a predictive study considering scenarios with different explosion sites and energies (Paris et al. 2019). Considering these works, it is clear that tsunamis sourced by volcanoes can be well explained with numerical models, but the considered volcanoes remain limited to a few select sites and scenarios. These models are also typically restricted to one particular volcano and one specific mechanism of tsunami generation as a retrospectively investigation.” Many of these are not commonly considered for this type of We therefore emphasise the to reconsider the current state of monitoring and risk assessment in these areas. Since tsunami warning systems are mostly not designed to detect volcanogenic tsunamis, our results highlight the importance of a reassessment of the current network and additional suitable equipment on the ground and through earth observation satellites. Due to the inherently short warning times of these events, we also recommended increased pre-emptive measures on a local level, such as increased public education programs for coastal communities and the marking evacuation routes along populated coasts.”

In MCDA, the authors considered different factors (H/D-Ratio, Volcanic activity, Tsunamigenic history, Slope angle, and Hazardous Features [Underwater extent, Morphological features, Vegetation, Hydrothermal alteration, Topography between an edifice and the sea]). I suppose that these factors are different in terms of objectivity and uncertainty; in other words, some are objective, while others contain error or subjectivity. For example, H/D-Ratio, slope angle, volcanic activity (if limited to recent activity), and underwater extent are based on rather reliable data. On the other hand, tsunamigenic history should contain many missing events (as the authors mentioned), morphological features cannot be simply quantitatively related to the hazard assessment, the effects of vegetations on edifice stability would depend on their type, etc... I recommend that the Finally, for the separate list the reviewer requests here using only reliable factors, we would refer to our interactive excel sheet which can be freely adjusted for that purpose. If e.g. the hazardous features (as the least reliable factor) should be removed, its weight could simply be set to 0% while increasing the others (see instructions on the sheet).
Page 15 line 323: "A complete, more detailed, and interactive version of this list with individual entries relating to how the points were counted can be found in supplementary material B."

Similar factors in MCDA
Factors of morphological features and hydrothermal alternation seem to be related to the factor of volcanic activity. It seems that these related factors increase the scores for volcanoes that recently erupted. Please show how these factors are correlated with each other. If the correlations are large, some of the factors might be removed.
Reply: We fully agree that further clarifications and an improved explanation is needed. The factors we used are, in fact, largely independent and do not correlate on the scoring. We specifically pick up the reviewer example of hydrothermal alteration and volcanic activity and add a statement highlighting this point in the limitations as follows: Page 14 line 290: "Conducting a comparative ranking can be more challenging if there are major dependencies between the used factors. As an example for our case, it would be reasonable to assume that recent eruptive activity would more likely cause hydrothermal alteration, thus making the eruptive history and hazardous features factors interdependent. However, in our catalogue, only few volcanoes are recorded to have extensive hydrothermal alteration on their flanks and for many of these, no eruption occurred for decades to centuries (e.g. Manuk, Teon, Serua). Hence, we think that these issues are unlikely to significantly affect our results. The only exception is a direct dependence between the H/D-ratio and the slope angle as it is essentially the same value if the volcano is steep slopes on a local level."

Potential spatial impact of volcanogenic tsunamis
The map in Fig. 7 does not add any important information, since the heat map of the volcanic tsunamis' spatial impact shows high density around the high-hazard volcanoes, which is obvious. Also, the assessment of the spatial impacts only based on the tsunami travel times is disappointing. To consider the hazard, tsunami amplitudes on coasts should be taken into account. I understand that it is difficult to assume complex volcanic tsunami sources, the authors are recommended to conduct numerical simulations using linear longwave models, at least with a simple tsunami source model (for example, a Gaussianshape uplift on the sea surface).
Reply: We appreciate this comment and make multiple improvements and clarifications to the text and figures. Indeed, we do not include information on tsunami amplitudes or run-up on coasts. This is intentional as a reliable assessment of volcano-generated tsunami wave amplitudes requires knowledge of many of yet unknown source parameters. Specifically, there are multiple potential processes at volcanoes which may generate a tsunami (explosion, flank collapse, PDC etc.). Each of them has a specific set of parameters describing magnitude, direction, etc. and each of them would result in highly different wave amplitudes. Reliable modelling of volcanogenic tsunamis requires thorough collection and evaluation of these specific source parameters, in addition to the advanced numerical techniques beyond classical nonlinear shallow water (NLSW) algorithms, and is usually applied to specific singular (historical) events. Incorporating such modelling for multiple volcanoes at once (in a ranking study like present) would not only be highly demanding, but, without constraining all the principal source parameters, also highly speculative. This also holds true for simpler Gaussian-shape uplifts as the magnitude of uplift would have to be defined based on speculation. Also, a Gaussian-shape source cannot account for any wave directivity which is typical for flank collapses, which is an issue that would become relevant when specific wave heights are considered.
Instead, we would like to avoid producing highly unconstrained results and pursue a more meaningful approach by limiting our models to the spatial tsunami extent in time and the length of the potentially affected coast. Note that these simple tsunami travel time models have the advantage that they are independent from the wave height and the generation mechanism (as long as it is a point source), so we can make meaningful assessments without assuming a yet unknown tsunami source. possible because the specific local circumstances leading to the tsunami are very well understood, which is knowledge that is lacking for most coastal volcanoes. Here, we provide multiple predictive models for the volcanoes we classified as posing a high tsunamigenic hazard. As volcanogenic tsunamis are caused by a large variety of mechanisms (Fig. 6) we contribute to this aspect by providing a simplified and broader view at the travel times of potential future tsunamis that are unspecific to the mechanism of tsunami generation and their magnitude (with the possible exception of meteotsunamis as seen at Hunga Tonga Haʻapai in 2022, which appear to have different wave propagation properties). We mainly account for the potential spatial impact of volcanogenic tsunamis and extend our tsunami hazard evaluation by assessing the total length of a coastline affected within one and two hours of tsunami propagation for the volcanoes categorised as high hazard in our ranking (except Didicas)" Secondly, we highlight that the amplitudes and wave heights cannot be considered, but that comes with the advantage of the tsunami source independence.
Page 24 line 455: "This means that we can simulate the travel and arrival times of specific volcanoes independent of how the tsunami was generated (as long as it is a point source), but we also cannot consider specific wave heights or runup as these depend strongly on the specific source mechanism and magnitude of the event and require additional and much more specific modelling data for individual sites." Page 26 line 475: "While our models are limited to the travel time, they can be used to estimate the warning time for shores in case a tsunami occurs at one of the considered volcanoes." Thirdly, we agree with the reviewer and recognize the value of models with specific wave heights. While we prefer our simplified broader models, we instead provide an additional paragraph summarising some previous studies specific to single volcanoes and historical events: Page 23 line 444: "In order to assess the risks and impacts of volcanogenic tsunamis, numerical simulations are commonly used, both for distinct future scenarios and in retrospect for past events. For Southeast Asia, a large number of such studies had been conducted. Most models were done for Anak Krakatau looking specifically at the 2018 flank collapse with some using the known event to calibrate and confirm the quality of current simulation methods (Grilli et al. 2019;Borrero et al. 2020;Mulia et al. 2020;Omira and Ramalho 2020;Paris et al. 2020;Zengafinnen et al. 2020), some using the known tsunami data (e.g. from tide gauges) to identify source parameters (Heidarzadeh et al. 2020;Ren et al. 2020;Grilli et al. 2021) and some testing variations in the source parameters volume that occurred both with a subaerial and a submarine component is mostly consistent with the observed and modelled runup heights at the adjacent shores. Similar models also exist for the 1883 tsunami at Krakatau, with the main purpose being the identification of its generation mechanism (Maeno and Imamura 2011) and how such a tsunami propagates in the far-field (Choi et al. 2003). Predictive studies only considering possible future events are not as abundant, but have been done for Anak Krakatau before the 2018 tsunami (Giachetti et al. 2012;Badriana et al. 2017), with Giachetti et al. (2012 making a remarkably close prediction to the later event. Other volcanoes in Southeast Asia are not as commonly considered. Pranantyo et al. (2021) test the tsunami propagation from Ruang volcano, Indonesia, using and comparing both historical observations and data from the 2018 Anak Krakatau event and reproducing a 25 m runup in the near-field. In Papua New Guinea numerical tsunami models have almost exclusively been considered for the Ritter Island tsunami in 1888 and the reconstruction of its generation (Ward and Day 2003;Karstens et al. 2020). Similarly, numerical tsunami models in the Philippines are mostly limited to Taal volcano, where models are based both on a past tsunami in 1716 (Pakosung et al. 2020) and a predictive study considering scenarios with different explosion sites and energies (Paris et al. 2019). Considering these works, it is clear that tsunamis sourced by volcanoes can be well explained with numerical models, but the considered volcanoes remain limited to a few select sites and scenarios. These models are also typically restricted to one particular volcano and one specific mechanism of tsunami generation as a retrospectively investigation." We also make a brief point that our travel-time models could be supplemented with more specific scenario models in future studies.
Page 26 line 489: "For future hazard and risk assessments, we thus recommend supplementing the knowledge from our TTT-models with specific detailed scenario calculations using established numerical modelling approaches, particularly for those highhazard volcanoes where no such models exist (e.g. Batu Tara, Iliwerung, Nila)." Lastly, regarding the heat-map in Fig. 7, while it may seem obvious, highlights the most likely areas for tsunamis to occur. We think this is important to keep as many of the hazardous volcanoes in the highlighted areas have received little attention and study, which is what we point to with our figure. Here we improve the figure by combining it with Fig. 8 to create a more condensed version and to avoid confusion with our travel-time modelling [Minor comments]