Wave sources of the 17th-century tsunami deposits in western Hokkaido, Japan using sediment transport modeling

Wave sources of the 17th-century tsunami deposits in western Hokkaido, Japan using sediment transport modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Wave sources of the 17th-century tsunami deposits in western Hokkaido, Japan using sediment transport modeling Ryo Nakanishi, Tatsuto Kimura, Takeshi Kanno This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4379459/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sediment transport modeling is a powerful tool for identifying wave sources of paleo-tsunami deposits because it can reproduce not only the thickness distribution but also the sediment features. The giant earthquakes in the Kuril Trench have uncertainties in the magnitude depending on the extent to which tsunami deposits widely distributed along the Pacific coast of Hokkaido can be correlated with each other. Multiple tsunami deposits have been found in Kabari, northern Hidaka, Hokkaido, and their wave sources are expected to provide a significant constraint on the tsunami magnitude. We reproduce two layers of tsunami deposits around the 17th century with sediment transport modeling using possible wave source candidate models. The Mt. Komagatake collapse and Kuril Trench earthquake models reproduce the two layers of tsunami deposits, indicating the tsunami distributions along the Pacific coast of Hokkaido are reproduced without Mw > 9 earthquake models. Sediment transport modeling Tsunami deposit Kuril Trench Hokkaido Mt. Komagatake Volcanic tsunami Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Tsunami deposits are an important geological record of past tsunami inundation. The tsunami inundation and transported sediment distribution are not fully coincident (e.g. Abe et al. 2012 ), relating to erosion and transport of sediments. Since this discrepancy varies depending not only on the hydraulic conditions but also on the bottom sediment, past tsunamis require calculating sediment transport. Sediment transport modeling (STM) considering multiple grain sizes allows comparison of observed tsunami deposits on sedimentary structures and grain size composition as well as thickness distribution (Nakanishi and Ashi 2022 ). Therefore, STM can provide new constraints in estimating wave sources, even for multiple event layers with similar sediment distribution or inundation extent. However, the applications of STM to past tsunamis are limited (Sugawara et al. 2019 ; Dourado et al. 2021 ; Nakanishi and Ashi 2022 ), and case studies need to be accumulated. Although a giant earthquake in the southern Kuril Trench is not known from historical records, the geological records in the 17th century suggest the existence of the giant earthquake of Mw ~ 8.8 (Ioki and Tanioka 2016 ; Nakanishi et al. 2022a ) (T10N5S25 model in Fig. 1a). However, it is not clear to which area these deposits are correlated due to several wave source candidates. The 17th-century tsunami deposits are widely distributed along the Pacific coast of western Hokkaido (Nishimura and Miyaji 1995 ; Nakanishi et al. 2014 ; Nakanishi and Okamura 2019 ; Takashimizu et al. 2007 , 2017 ; Nakanishi et al. 2022b ; Nakanishi et al. 2024 ), and if all of them were to be induced by a megathrust earthquake, the magnitude is estimated over M9 (Okamura and Namegaya 2011 ; Hokkaido Government 2012 ; Headquarters for Earthquake Research Promotion 2017 ; Cabinet Office 2022 ). On the other hand, in western Hokkaido, the collapse of Mt. Komagatake in 1640 caused debris flow into the sea and generated a large tsunami (Nishimura and Miyaji 1995 ). Another possibility is that the 1611 CE Keicho tsunami, which caused a large tsunami along the Sanriku coast, also reached Hokkaido (Yamanaka and Tanioka 2022 ). These multiple wave sources may have generated tsunamis in the 17th century and tsunami deposits in the coastal areas. Figure 1. Study ares maps with topography and wave cources. (a) Map for northern Japan and wave source models. Initial water levels due to source fault displacement of wave source candidates are shown. Green circles indicate locations where 17th-century tsunami deposits have been reported. Contour lines indicate the depth of the plate boundary. (b) Current DEM data. Survey lines measuring thickness distribution patterns are shown. (c) 17th-century topography (low ridge) reconstructed from DEM. Dashed lines indicate the zone where transported sediment volumes were calculated. (d) 17th-century topography with beach ridge (high ridge). Shaded zones display source sand areas. (e) Tsunami amplitude off Kabari of each model. Numerical tsunami simulations indicate that the 1640 Komagatake tsunami and the Kuril Trench tsunami reached the eastern Iburi to northern Hidaka coastal areas with wave heights of > 2 m (Kanno et al. 2023 ). Although the single layer of 17th-century tsunami deposits has been reported (Takashimizu et al. 2007 , 2017 ; Nakanishi et al. 2014 ; Nakanishi et al. 2022b ), Nakanishi et al. ( 2024 ) reported two layers of tsunami deposits during the 15th–17th centuries in Kabari, northern Hidaka (Fig. 1a). Identifying the wave sources of these two layers can constrain the maximum magnitude of the giant earthquake in the Kuril Trench. In this study, we reproduce these 17th-century tsunami deposits with STM using possible wave source models. This study provides a good example of refining wave sources by comparing the sediment distribution and detailed sediment features. 2 Tsunami deposit data In Kabari, the 17th-century tsunami deposits were reported by Takashimizu et al. ( 2017 ) and reexamined by Nakanishi et al. ( 2024 ). They found a total of five sand layers in the peat layer and reported that the two sand layers were different event layers that occurred in the 15th–17th century. The upper sand layer (KS1) is more widely distributed than the lower sand layer (KS2). The KS1 layer is less than 10 cm thick and consists of very coarse to fine sand. The KS2 layer is less than 1.5 cm thick and consists of coarse to fine sand. 3 Wave source model The 1640 Komagatake collapse and the 1611 Keicho tsunami based on historical and geological records, and the Kuril Trench earthquake in the 17th century based on geological records are the wave source candidates of the past tsunamis in Kabari. For the 17th-century Kuril Trench earthquake, the T10N5S25 model and its minor change model (T10w70N5S25 model) have been proposed based on the tsunami inundation modeling reproduced the tsunami deposit distribution in eastern Hokkaido (Ioki and Tanioka 2016 ; Nakanishi et al. 2022a ). For the 1640 Komagatake collapse, Kanno et al. ( 2023 ) proposed the models considering the debris inflow process based on the document records and tsunami deposit distributions, which are the inflow volumes of 1.3 and 1.5 km 3 and the estimated inundation can cover the tsunami deposit distribution from the northern Hidaka coast to the Uchiura Bay. The 1611 Keicho tsunami is known to have generated tsunami wave heights of 4–30 m along the Sanriku coast based on document records (Ebina and Imai 2014 ); however, the epicenter is unclear due to the lack of records related to ground motions. Yamanaka and Tanioka ( 2022 ) found that local anomalous wave heights on the Sanriku coast require a short-period component in the Japan Trench. The simple fault model that constructed the two segments off Sanriku roughly reproduces the tsunami wave heights along the Sanriku coast based on the document records. The North Sanriku-oki earthquakes and the Ishikari Lowland eastern edge faults are other possible wave sources of tsunami in the Kabari area although there are no known events in the 17th century. The 1968 North Sanriku-oki Earthquake caused a tsunami of ~ 2 m around the Kabari area (Hatori 1973 ). The fault models based on the run-up height are larger than one based on tide records, which are inverted to Mw 8.4 and 8.1 (Satake 1989 ; Annaka et al. 1999 ). Regarding active faults, the 2018 eastern Iburi earthquake (Mw 6.8) was caused in the northwest area of Kabari. The 1982 Urakawa-oki earthquake (Mw 6.9) occurred on the seaward extension of thrust faults of the Ishikari. We used a hypothetical large fault model with twice the amount of slip off-Kabari based on the fault parameters of the 2018 Iburi earthquake obtained by the SAR analysis by the Geospatial Information Authority (GSI) of Japan (2018). The wave source models used in this study are listed in Table 1 . Table 1 Tsunami and sediment transport modeling calculation conditions. No. Wave Source Magnitude Topography Manning's Roughness coefficient Sea level (m) TSV (m 3 ) Reference Tsunami modeling Komagatake-small Komagatake Volume 1.3 km 3 0.03 Kanno et al. ( 2023 ) Komagatake-large Komagatake Volume 1.5 km 3 0.03 Kanno et al. ( 2023 ) Ku-T10N5S25 Kuril Trench Mw 8.8 0.03 Ioki and Tanioka ( 2016 ) Ku-T10w70N5S25 Kuril Trench Mw 8.8 0.03 Nakanishi et al. ( 2022b ) Sa-1611 Off Sanriku Mw 8.5 0.03 Yamanaka and Tanioka ( 2022 ) NSa-1968 Off North Sanriku Mw 8.4 0.03 Annaka et al. ( 1999 ) NSs-1968 Off North Sanriku Mw 8.1 0.03 Satake ( 1989 ) Active fault Off Kabari Mw 6.8 0.03 modified GSI (2018) Sediment transport modeling Ko-ThM30S0 Komagatake Volume 1.3 km 3 High ridge 0.03 0 918 Kanno et al. ( 2023 ) Ko-ThM30S1 High ridge 0.03 1 978 Ko-ThM40S0 High ridge 0.04 0 464 Ko-ThM40S1 High ridge 0.04 1 493 Ko-TlM30S0 Low ridge 0.03 0 569 Ko-TlM30S1 Low ridge 0.03 1 659 *Ko-TlM40S0 Low ridge 0.04 0 291 Ko-TlM40S1 Low ridge 0.04 1 292 NSa-ThM30S0 Off North Sanriku Mw 8.4 High ridge 0.03 0 161 Annaka et al. ( 1999 ) NSa-ThM30S1 High ridge 0.03 1 392 NSa-ThM40S0 High ridge 0.04 0 36 NSa-ThM40S1 High ridge 0.04 1 120 NSa-TlM30S0 Low ridge 0.03 0 80 NSa-TlM30S1 Low ridge 0.03 1 117 *NSa-TlM40S0 Low ridge 0.04 0 22 NSa-TlM40S1 Low ridge 0.04 1 47 NSa-TlM40S05 Low ridge 0.04 0.5 336 NSa-ThM30S05 High ridge 0.03 0.5 44 Ku-1 Kuril Trench Mw 8.8 Low ridge 0.04 1 11 Ioki and Tanioka ( 2016 ) Ku-2 Low ridge 0.03 1 63 Ku-3 High ridge 0.03 1 123 Ku-4 High ridge 0.04 1 30 *Ku-5 Low ridge 0.04 0.5 21 Ke-1 Off Sanriku Mw 8.5 High ridge 0.03 1 13 Yamanaka and Tanioka ( 2022 ) Ke-2 Low ridge 0.04 1 2 Ke-3 Low ridge 0.03 1 9 * Base scenario 4 Method Wave propagations and STMs are used Delft-3D (Delfters 2020). The basic tsunami and STM methods are followed by Nakanishi and Ashi ( 2022 ). Topography and bathymetry data are nested at 405, 135, 45, 15, and 5-m mesh sizes. The bathymetry and topography data are from the Global Tsunami Terrain Model (Chikasada 2020 : doi: 10.17598/NIED.0021 ) and the 5-m mesh digital elevation model (DEM; from GSI: https://fgd.gsi.go.jp/download/mapGis.php?tab=dem ). The time step for the 5-m mesh domain is set to < 0.1 s to stabilize these calculations. 5 Parameter setting The topography around the 17th century is reconstructed based on coastal 5-m DEM data and core samples (Fig. 1b). Artificial structures (roads, bridges, and wharf) were removed from the DEM data, and the coastline was established based on aerial photograph data in 1944 (GSI, https://mapps.gsi.go.jp/maplibSearch.do#1 ) before starting the significant coastal erosion in the 1950s (Mizogami et al. 1970 ) (Fig. 1cd). The elevation is reconstructed based on the depth of the 17th-century volcanic ash layers from core samples (Nakanishi et al. 2024 ). The sand sources are defined from 1944 aerial photographs and field surveys (Fig. 1d). The grain size compositions of the sand source are divided into two areas based on the grain sizes of modern sand samples, which are three-grain sizes (D50 are 0.2, 0.3, and 1.5 mm) with a normal distribution (D10 = 0.75 × D50 and D90 = 1.5 × D50). The very coarse and medium sands are distributed on the beaches and ridges in the west area, while fine sand is distributed near the river mouth and the beach in the east area (Nakanishi et al. 2024 ). A transported sediment volume (TSV) is used to compare observed and estimated sediments because many factors can cause differences between both layer thicknesses (Sugawara et al. 2014 ; Dourado et al. 2021 ; Masuda et al. 2022 ). Sugawara et al. ( 2014 ) also found that layer thickness is largely affected by parameters, while patterns of layer thickness distribution are less sensitive. Among the past topographic conditions that are difficult to reconstruct, ridge height, roughness of sand source area, and sea level (tidal and global change) are parameters that have a significant impact on the results of STM (Nakanishi and Ashi 2022 ). The manning’s roughness coefficients of the sand area are 0.03 and 0.04 m − 1/3 /s. Ridge heights were unchanged from the DEM data in the low ridge scenario (3–4 m height: Fig. 1c) and are increased by 2 m in the high ridge scenario (5–6 m height: Fig. 1d). Based on the sea level index points reconstructed by Nakanishi et al. ( 2024 ), the sea level in the 17th century was as close to the present sea level. The difference between mean higher high water and mean lower low water is ~ 0.9 m. Therefore, we use the condition where the sea level is 0.5 m and 1 m higher than the present sea level because the tide level can change by a maximum of 1 m depending on the time of a tsunami occurrence. The TSVs and thickness distribution patterns are indicated in the area shown in Fig. 1c and 1b, respectively. The TSVs of Sand KS1 and Sand KS2 are calculated by cubic interpolation of the observed thickness distribution (Nakanishi et al. 2024 ), and the interpolated TSVs of the KS1 and KS2 layer are 293 m 3 and 21 m 3 , respectively, and the TSV ratio (KS1/KS2) was 14.2. 6 Result and Discussion Figure 1e shows the tsunami waveforms off Kabari for the wave source candidates. Since both volume models for the Komagatake tsunami show similar tsunami waveforms, we use the 1.3 km 3 model for STM hereafter. The active fault model, the Satake ( 1989 ) model of the 1968 North Sanriku earthquake, and the 1611 Keicho model show that the wave height is less than 2 m. The maximum wave height of the T10N5S25 model is slightly higher than that of the T10w70N5S25 model. Therefore, the Komagatake (Kanno et al. 2023 ), Kuril Trench (T10N5S25: Ioki and Tanioka 2016 ), and North Sanriku models (Annaka et al. 1999 ) are used for STM. We evaluate which changes in these parameters affect TSV and thickness distribution patterns (Figs. 2 and 3 ). The TSVs depending on roughness coefficient, ridge height, and sea level were evaluated using the Komagatake (1.3 km 3 ) and the North Sanriku models (Annaka et al. 1999 ) (Fig. 2 a). Under the same ridge height and roughness coefficient conditions, the TSVs of both wave source models are roughly linear (R 2 is 0.71 for all 8 scenarios). The coefficients of determination are 0.97 and 0.87 for sea level 0 m and sea level 1m, respectively, indicating that TSV varies with changes in bottom conditions between wave source models. While only the layer thickness with change in sea level and roughness coefficient, the ridge height change also affects the thickness distribution patterns (Fig. 3 ), which is a result of suppression of sand transport over the beach ridge. From the above sensitive tests, we search for scenarios that reproduce the observed TSVs and compare the TSV ratios between models with the same roughness coefficient and topographic condition (sea level varies). The Komagatake and the North Sanriku model (sea level > 0.5 m) only exceed the KS1 TSV (Fig. 2 b and Table 1 ). The Komagatake model produced the largest TSV even the other models under the high sea level conditions. Since the Komagatake tsunami is a documented event, we compare TSV ratios using the Komagatake model as the KS1 source rather than the Northern Sanriku model. The scenarios comparable to the KS1’s TSV were the low ridge and 0.04 m − 1/3 s condition, which is used to compare each wave source model as the base scenario. The North Sanriku (0 m sea level) and Kuril Trench model (0.5 m sea level) show comparable KS1/KS2 ratios (Fig. 2 b). The Komagatake and Kuril models and the Komagatake and North Sanriku models are possible combinations as the wave sources of the KS1 and KS2 layers. The thickness distribution in the Komagatake model with the base scenario is similar to that of the KS1 layer on Line A and Line B in Fig. 1b (Fig. 3 ). The North Sanriku model shows convex upward patterns (30–80 m in Line B), while the Kuril Trench model shows a homogeneous thin layer and thinning inland. In addition to TSV and thickness distribution patterns, the estimated grain size composition and sedimentary structures at a given site can be compared to the observed sand layer. Figure 4 shows the hydraulic conditions and grain size composition at the intersection of Line A and Line B. The Komagatake model displays that the runup flow by the first flow directly inundated the ridge in the northeast direction. Although 0.2- and 0.3-mm components are dominated by suspension, the 1.5-mm component transported by bedload is also included, indicating a poor sorting layer. The North Sanriku model shows inundation from the river mouth as indicating the westward flow. Only the 0.2-mm component is transported by two flows in two halves (the first and third one). No significant changes in grain size components were observed for different sea levels, roughness coefficients, or topography in this model. The Kuril Trench model shows the leading northwestward inflow from the river mouth and the following northeastward inflow over the beach ridge at ~ 150 min. The 0.2- and 0.3-mm components are transported by the northwest flow, while the 0.2 mm component is immediately eroded by the following northeastward flow; finally, the dominant 0.3 mm and a small amount of 1.5 mm components are deposited. The Komagatake model showed high agreement with the KS1 layer in TSV and thickness distribution pattern (Figs. 2 and 3 ). The KS1 layer shows the inverse to normal grading structure and is induced by the inundation over the beach ridge because it contains coarse-grained sand that is constated of the beach ridge and beach sand (Nakanishi et al., 2024 ). The Komagatake model also reproduces the features of the KS1 layer in the grading structure by bedload and suspension and poor sorting (Fig. 4 a). For the KS2 layer, the Kuril Trench and the North Sanriku models are candidates based on the TSV ratio compared to the Komagatake model (Fig. 2 ). The sand layer estimated by the Kuril Trench model was dominated by the 0.3-mm component and it was similar with the that of the KS2 layer (Fig. 4 ) and generally agrees with the KS2 distribution pattern as homogeneous thin layers and the thinning trend behind the beach ridge (Fig. 3 ). The North Sanriku model consists with the TSV of KS1 and KS2; however, the inundation from the river mouth cannot transport coarse sand. The estimated layers are different from the KS1 and KS2 layers in terms of the distribution patterns and grain size distributions. The wavelength is longer than in other models due to the deep focal depth (Fig. 1e) and may result in the low velocity was not sufficient to transport > 0.3-mm components (Fig. 4 b). Therefore, if the sand source consisted of coarse grains, the tsunami could have existed without leaving tsunami deposits, which should be noted for disaster prevention. The T10N5S25 model (or T10w70N5S25 model) is the most reasonable wave source for the KS2 layer because it is a model based on the 17th-century tsunami deposits in eastern Hokkaido and can comprehensively reproduce the tsunami deposit distributions in the Hidaka region including the Kabari as well as Erimo and Shizunai areas. 7 Conclusion To constrain the maximum magnitude of a giant earthquake in the Kuril Trench, sediment transport modeling was used to identify the wave source of two layers of 15th–17th-century tsunami deposits observed in Kabari, far from the Kuril Trench. The Komagatake, Kuril Trench, and North Sanriku models show inundations covering the observed sand layers. The Komagatake and Kuril models show good agreement with the observed KS1 and KS2 layers, respectively on not only the transported sediment volumes and thickness distribution patterns but also grain size compositions. The North Sanriku model shows similar wave heights to the Kuril Trench model; however, the flow velocities were not enough to transport coarse grains over the beach ridge. The sediment transport modeling provides useful information to identify wave sources of tsunami deposits by comparing sediment features and thickness distribution patterns rather than only an inundation extent. The Komagatake and Kuril Trench models can reproduce the tsunami deposits in Kabari without modifying the inflow debris volume and fault parameters and explain the distribution of tsunami deposits along the western Pacific coast of Hokkaido without Mw > 9 giant earthquakes (e.g., Cabinet Office 2022 ). The Kuril Trench tsunami in Kabari is smaller than the Komagatake tsunami in the 17th century. This information is a significant constraint for identifying the maximum magnitude of earthquakes in the Kuril Trench and contributes to the investigation of the tsunami sources in the Kuril and Japan Trenches. Declarations Funding This work was supported by JSPS KAKENHI Grant Number JP23K13177. Availability of data and materials This work relies on open-source code, namely Delft3D-Flow version 3.15 (Deltares, 2020) for sediment transport modeling (https://svn.oss.deltares.nl/repos/delft3d/tags/delft3d4/7545/). This code requires registration to download. Competing interests The authors declare that they have no competing interests. Authors' contributions R.N. processed the numerical simulation and drafted the manuscript. T.Kimura provided the tsunami wave data of the Komagatake tsunamis and reviewed the manuscript. T.Kanno reviewed and edited the manuscript. All authors approved the final version of this manuscript. References Abe T, Goto K, Sugawara D (2012) Relationship between the maximum extent of tsunami sand and the inundation limit of the 2011 Tohoku-oki tsunami on the Sendai Plain, Japan. Sediment Geol 282:142–150. doi.org/10.1016/j.sedgeo.2012.05.004. 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Okamura Y, Namegaya Y (2011) Reconsideration of the 17th century Kuril multi-segment earthquake. Ann Rep Active Fault Paleoearthq Res 11:15–20 (in Japanese, with English abstract). Takashimizu Y, Sagayama T, Nishina K, Oka T, Nakamura Y, Nishimura Y (2007) A 17th-century tsunami deposit discovered on the eastern Iburi coast, Hokkaido, northern Japan. Quat Res 46:119–130 (in Japanese, with English abstract). Takashimizu Y, Nishina K, Kawakami G, Sato Y, Okamura S, Nakanishi R, et al. (2017) Identification of a 17th-century tsunami deposit on the northern Hidaka coast, Hokkaido, northern Japan. Quat Res 56:1–9 (in Japanese, with English abstract). Yamanaka Y, Tanioka Y (2022) Short-wave run-ups of the 1611 Keicho tsunami along the Sanriku Coast. Prog Earth Planet Sci 9:37. doi.org/10.1186/s40645-022-00496-1 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4379459","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314924604,"identity":"96ff8a78-be3c-47f7-affc-2b550831eb61","order_by":0,"name":"Ryo Nakanishi","email":"data:image/png;base64,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","orcid":"","institution":"Kyoto University","correspondingAuthor":true,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Nakanishi","suffix":""},{"id":314924605,"identity":"aa41df64-ea2d-41ef-8600-d78df6275f02","order_by":1,"name":"Tatsuto Kimura","email":"","orcid":"","institution":"Tokyo Electric Power Services Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Tatsuto","middleName":"","lastName":"Kimura","suffix":""},{"id":314924606,"identity":"36e05dae-c440-41d1-ac32-49a360f12e71","order_by":2,"name":"Takeshi Kanno","email":"","orcid":"","institution":"Tohoku Electric Power Co., Inc","correspondingAuthor":false,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Kanno","suffix":""}],"badges":[],"createdAt":"2024-05-07 01:24:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4379459/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4379459/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59462498,"identity":"24c9033c-2104-43d3-8c0e-5c80f28f65e0","added_by":"auto","created_at":"2024-07-02 05:37:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":549671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy ares maps with topography and wave cources. (a) Map for northern Japan and wave source models. Initial water levels due to source fault displacement of wave source candidates are shown. Green circles indicate locations where 17th-century tsunami deposits have been reported. Contour lines indicate the depth of the plate boundary. (b) Current DEM data. Survey lines measuring thickness distribution patterns are shown. (c) 17th-century topography (low ridge) reconstructed from DEM. Dashed lines indicate the zone where transported sediment volumes were calculated. (d) 17th-century topography with beach ridge (high ridge). Shaded zones display source sand areas.\u003c/strong\u003e \u003cstrong\u003e(e) Tsunami amplitude off Kabari of each model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4379459/v1/2adbef3cadc7fecd88445955.png"},{"id":59462926,"identity":"a8234bac-8c3a-4a22-a605-2e7569f4c809","added_by":"auto","created_at":"2024-07-02 05:45:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTransported sediment volume obtained by sediment transport modelings. (a) Biplot of transported sediment volume for Komagatake and Northern Sanriku models at the same roughness coefficient and topographic conditions. Regression lines and coefficients of determination are presented for each sea-level scenario. (b) Transported sediment volume under each condition. Solid lines indicate sediment volumes of the KS1 and KS2 layers. Black bars indicate the results of the base scenario. Diamonds indicate the ratio of the Komagatake to the Northern Sanriku or T10N5S25 models under the same conditions. The dashed line is KS1/KS2 ratio.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4379459/v1/142b50cb7d3756c53f1b019a.png"},{"id":59463451,"identity":"76b44010-2df5-4235-8744-82838f8e6c10","added_by":"auto","created_at":"2024-07-02 05:53:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated and observed sediment thickness on survey lines.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4379459/v1/70781a46c449c8c90c5e4cbb.png"},{"id":59462502,"identity":"d864b597-1e6c-447d-a108-3e0c433dfdbe","added_by":"auto","created_at":"2024-07-02 05:37:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":626801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHydraulic conditions, sediment thickness, and grain size composition at a given location (a-c); tsunami inundation, distribution of sedimentation or erosion (d-f) estimated by sediment transport modeling on the Komagatake, Kuril Trench, and North Sanriku models with the base scenario. The arrows in d-f indicate the point at which the data in a-c was obtained. Gray areas indicate no inundation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4379459/v1/f1a89b535fb5167be8f58399.png"},{"id":63377831,"identity":"6eda4c29-d85c-4cf6-9d4c-a3d7f54a4d2a","added_by":"auto","created_at":"2024-08-27 13:11:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2396979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4379459/v1/43322df9-9cf4-4e9a-b2d8-3ba66385efe1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wave sources of the 17th-century tsunami deposits in western Hokkaido, Japan using sediment transport modeling","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTsunami deposits are an important geological record of past tsunami inundation. The tsunami inundation and transported sediment distribution are not fully coincident (e.g. Abe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), relating to erosion and transport of sediments. Since this discrepancy varies depending not only on the hydraulic conditions but also on the bottom sediment, past tsunamis require calculating sediment transport. Sediment transport modeling (STM) considering multiple grain sizes allows comparison of observed tsunami deposits on sedimentary structures and grain size composition as well as thickness distribution (Nakanishi and Ashi \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, STM can provide new constraints in estimating wave sources, even for multiple event layers with similar sediment distribution or inundation extent. However, the applications of STM to past tsunamis are limited (Sugawara et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dourado et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nakanishi and Ashi \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and case studies need to be accumulated.\u003c/p\u003e \u003cp\u003eAlthough a giant earthquake in the southern Kuril Trench is not known from historical records, the geological records in the 17th century suggest the existence of the giant earthquake of Mw\u0026thinsp;~\u0026thinsp;8.8 (Ioki and Tanioka \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) (T10N5S25 model in Fig.\u0026nbsp;1a). However, it is not clear to which area these deposits are correlated due to several wave source candidates. The 17th-century tsunami deposits are widely distributed along the Pacific coast of western Hokkaido (Nishimura and Miyaji \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nakanishi and Okamura \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Takashimizu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and if all of them were to be induced by a megathrust earthquake, the magnitude is estimated over M9 (Okamura and Namegaya \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hokkaido Government \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Headquarters for Earthquake Research Promotion \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cabinet Office \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, in western Hokkaido, the collapse of Mt. Komagatake in 1640 caused debris flow into the sea and generated a large tsunami (Nishimura and Miyaji \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Another possibility is that the 1611 CE Keicho tsunami, which caused a large tsunami along the Sanriku coast, also reached Hokkaido (Yamanaka and Tanioka \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These multiple wave sources may have generated tsunamis in the 17th century and tsunami deposits in the coastal areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Study ares maps with topography and wave cources. (a) Map for northern Japan and wave source models. Initial water levels due to source fault displacement of wave source candidates are shown. Green circles indicate locations where 17th-century tsunami deposits have been reported. Contour lines indicate the depth of the plate boundary. (b) Current DEM data. Survey lines measuring thickness distribution patterns are shown. (c) 17th-century topography (low ridge) reconstructed from DEM. Dashed lines indicate the zone where transported sediment volumes were calculated. (d) 17th-century topography with beach ridge (high ridge). Shaded zones display source sand areas. (e) Tsunami amplitude off Kabari of each model.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNumerical tsunami simulations indicate that the 1640 Komagatake tsunami and the Kuril Trench tsunami reached the eastern Iburi to northern Hidaka coastal areas with wave heights of \u0026gt;\u0026thinsp;2 m (Kanno et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the single layer of 17th-century tsunami deposits has been reported (Takashimizu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), Nakanishi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported two layers of tsunami deposits during the 15th\u0026ndash;17th centuries in Kabari, northern Hidaka (Fig.\u0026nbsp;1a). Identifying the wave sources of these two layers can constrain the maximum magnitude of the giant earthquake in the Kuril Trench. In this study, we reproduce these 17th-century tsunami deposits with STM using possible wave source models. This study provides a good example of refining wave sources by comparing the sediment distribution and detailed sediment features.\u003c/p\u003e"},{"header":"2 Tsunami deposit data","content":"\u003cp\u003eIn Kabari, the 17th-century tsunami deposits were reported by Takashimizu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and reexamined by Nakanishi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They found a total of five sand layers in the peat layer and reported that the two sand layers were different event layers that occurred in the 15th\u0026ndash;17th century. The upper sand layer (KS1) is more widely distributed than the lower sand layer (KS2). The KS1 layer is less than 10 cm thick and consists of very coarse to fine sand. The KS2 layer is less than 1.5 cm thick and consists of coarse to fine sand.\u003c/p\u003e"},{"header":"3 Wave source model","content":"\u003cp\u003eThe 1640 Komagatake collapse and the 1611 Keicho tsunami based on historical and geological records, and the Kuril Trench earthquake in the 17th century based on geological records are the wave source candidates of the past tsunamis in Kabari. For the 17th-century Kuril Trench earthquake, the T10N5S25 model and its minor change model (T10w70N5S25 model) have been proposed based on the tsunami inundation modeling reproduced the tsunami deposit distribution in eastern Hokkaido (Ioki and Tanioka \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nakanishi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). For the 1640 Komagatake collapse, Kanno et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) proposed the models considering the debris inflow process based on the document records and tsunami deposit distributions, which are the inflow volumes of 1.3 and 1.5 km\u003csup\u003e3\u003c/sup\u003e and the estimated inundation can cover the tsunami deposit distribution from the northern Hidaka coast to the Uchiura Bay. The 1611 Keicho tsunami is known to have generated tsunami wave heights of 4\u0026ndash;30 m along the Sanriku coast based on document records (Ebina and Imai \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); however, the epicenter is unclear due to the lack of records related to ground motions. Yamanaka and Tanioka (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that local anomalous wave heights on the Sanriku coast require a short-period component in the Japan Trench. The simple fault model that constructed the two segments off Sanriku roughly reproduces the tsunami wave heights along the Sanriku coast based on the document records.\u003c/p\u003e \u003cp\u003eThe North Sanriku-oki earthquakes and the Ishikari Lowland eastern edge faults are other possible wave sources of tsunami in the Kabari area although there are no known events in the 17th century. The 1968 North Sanriku-oki Earthquake caused a tsunami of ~\u0026thinsp;2 m around the Kabari area (Hatori \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). The fault models based on the run-up height are larger than one based on tide records, which are inverted to Mw 8.4 and 8.1 (Satake \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Annaka et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Regarding active faults, the 2018 eastern Iburi earthquake (Mw 6.8) was caused in the northwest area of Kabari. The 1982 Urakawa-oki earthquake (Mw 6.9) occurred on the seaward extension of thrust faults of the Ishikari. We used a hypothetical large fault model with twice the amount of slip off-Kabari based on the fault parameters of the 2018 Iburi earthquake obtained by the SAR analysis by the Geospatial Information Authority (GSI) of Japan (2018). The wave source models used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTsunami and sediment transport modeling calculation conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWave Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMagnitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eManning's Roughness\u003c/p\u003e \u003cp\u003ecoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSea level\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTSV\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTsunami modeling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKomagatake-small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKomagatake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVolume 1.3 km\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKanno et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKomagatake-large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKomagatake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVolume 1.5 km\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKanno et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu-T10N5S25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKuril Trench\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIoki and Tanioka (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu-T10w70N5S25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKuril Trench\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNakanishi et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSa-1611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOff Sanriku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYamanaka and Tanioka (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-1968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOff North Sanriku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnnaka et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSs-1968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOff North Sanriku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSatake (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive fault\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOff Kabari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emodified GSI (2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSediment transport modeling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-ThM30S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKomagatake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVolume 1.3 km\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKanno et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-ThM30S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-ThM40S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-ThM40S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-TlM30S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-TlM30S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Ko-TlM40S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo-TlM40S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-ThM30S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOff North Sanriku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnnaka et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-ThM30S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-ThM40S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-ThM40S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-TlM30S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-TlM30S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*NSa-TlM40S0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-TlM40S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-TlM40S05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSa-ThM30S05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKuril Trench\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIoki and Tanioka (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Ku-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKe-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOff Sanriku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMw 8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYamanaka and Tanioka (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKe-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKe-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow ridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e* Base scenario\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4 Method","content":"\u003cp\u003eWave propagations and STMs are used Delft-3D (Delfters 2020). The basic tsunami and STM methods are followed by Nakanishi and Ashi (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Topography and bathymetry data are nested at 405, 135, 45, 15, and 5-m mesh sizes. The bathymetry and topography data are from the Global Tsunami Terrain Model (Chikasada \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e: doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17598/NIED.0021\u003c/span\u003e\u003cspan address=\"10.17598/NIED.0021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the 5-m mesh digital elevation model (DEM; from GSI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fgd.gsi.go.jp/download/mapGis.php?tab=dem\u003c/span\u003e\u003cspan address=\"https://fgd.gsi.go.jp/download/mapGis.php?tab=dem\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The time step for the 5-m mesh domain is set to \u0026lt;\u0026thinsp;0.1 s to stabilize these calculations.\u003c/p\u003e"},{"header":"5 Parameter setting","content":"\u003cp\u003eThe topography around the 17th century is reconstructed based on coastal 5-m DEM data and core samples (Fig.\u0026nbsp;1b). Artificial structures (roads, bridges, and wharf) were removed from the DEM data, and the coastline was established based on aerial photograph data in 1944 (GSI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mapps.gsi.go.jp/maplibSearch.do#1\u003c/span\u003e\u003cspan address=\"https://mapps.gsi.go.jp/maplibSearch.do#1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) before starting the significant coastal erosion in the 1950s (Mizogami et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) (Fig.\u0026nbsp;1cd). The elevation is reconstructed based on the depth of the 17th-century volcanic ash layers from core samples (Nakanishi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The sand sources are defined from 1944 aerial photographs and field surveys (Fig.\u0026nbsp;1d). The grain size compositions of the sand source are divided into two areas based on the grain sizes of modern sand samples, which are three-grain sizes (D50 are 0.2, 0.3, and 1.5 mm) with a normal distribution (D10\u0026thinsp;=\u0026thinsp;0.75 \u0026times; D50 and D90\u0026thinsp;=\u0026thinsp;1.5 \u0026times; D50). The very coarse and medium sands are distributed on the beaches and ridges in the west area, while fine sand is distributed near the river mouth and the beach in the east area (Nakanishi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA transported sediment volume (TSV) is used to compare observed and estimated sediments because many factors can cause differences between both layer thicknesses (Sugawara et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dourado et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Masuda et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sugawara et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also found that layer thickness is largely affected by parameters, while patterns of layer thickness distribution are less sensitive. Among the past topographic conditions that are difficult to reconstruct, ridge height, roughness of sand source area, and sea level (tidal and global change) are parameters that have a significant impact on the results of STM (Nakanishi and Ashi \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The manning\u0026rsquo;s roughness coefficients of the sand area are 0.03 and 0.04 m\u003csup\u003e\u0026minus;\u0026thinsp;1/3\u003c/sup\u003e/s. Ridge heights were unchanged from the DEM data in the low ridge scenario (3\u0026ndash;4 m height: Fig.\u0026nbsp;1c) and are increased by 2 m in the high ridge scenario (5\u0026ndash;6 m height: Fig.\u0026nbsp;1d). Based on the sea level index points reconstructed by Nakanishi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the sea level in the 17th century was as close to the present sea level. The difference between mean higher high water and mean lower low water is ~\u0026thinsp;0.9 m. Therefore, we use the condition where the sea level is 0.5 m and 1 m higher than the present sea level because the tide level can change by a maximum of 1 m depending on the time of a tsunami occurrence. The TSVs and thickness distribution patterns are indicated in the area shown in Fig.\u0026nbsp;1c and 1b, respectively. The TSVs of Sand KS1 and Sand KS2 are calculated by cubic interpolation of the observed thickness distribution (Nakanishi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the interpolated TSVs of the KS1 and KS2 layer are 293 m\u003csup\u003e3\u003c/sup\u003e and 21 m\u003csup\u003e3\u003c/sup\u003e, respectively, and the TSV ratio (KS1/KS2) was 14.2.\u003c/p\u003e"},{"header":"6 Result and Discussion","content":"\u003cp\u003eFigure 1e shows the tsunami waveforms off Kabari for the wave source candidates. Since both volume models for the Komagatake tsunami show similar tsunami waveforms, we use the 1.3 km\u003csup\u003e3\u003c/sup\u003e model for STM hereafter. The active fault model, the Satake (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) model of the 1968 North Sanriku earthquake, and the 1611 Keicho model show that the wave height is less than 2 m. The maximum wave height of the T10N5S25 model is slightly higher than that of the T10w70N5S25 model. Therefore, the Komagatake (Kanno et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Kuril Trench (T10N5S25: Ioki and Tanioka \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and North Sanriku models (Annaka et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) are used for STM.\u003c/p\u003e \u003cp\u003eWe evaluate which changes in these parameters affect TSV and thickness distribution patterns (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The TSVs depending on roughness coefficient, ridge height, and sea level were evaluated using the Komagatake (1.3 km\u003csup\u003e3\u003c/sup\u003e) and the North Sanriku models (Annaka et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Under the same ridge height and roughness coefficient conditions, the TSVs of both wave source models are roughly linear (R\u003csup\u003e2\u003c/sup\u003e is 0.71 for all 8 scenarios). The coefficients of determination are 0.97 and 0.87 for sea level 0 m and sea level 1m, respectively, indicating that TSV varies with changes in bottom conditions between wave source models. While only the layer thickness with change in sea level and roughness coefficient, the ridge height change also affects the thickness distribution patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which is a result of suppression of sand transport over the beach ridge. From the above sensitive tests, we search for scenarios that reproduce the observed TSVs and compare the TSV ratios between models with the same roughness coefficient and topographic condition (sea level varies).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Komagatake and the North Sanriku model (sea level\u0026thinsp;\u0026gt;\u0026thinsp;0.5 m) only exceed the KS1 TSV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Komagatake model produced the largest TSV even the other models under the high sea level conditions. Since the Komagatake tsunami is a documented event, we compare TSV ratios using the Komagatake model as the KS1 source rather than the Northern Sanriku model. The scenarios comparable to the KS1\u0026rsquo;s TSV were the low ridge and 0.04 m\u003csup\u003e\u0026minus;\u0026thinsp;1/3\u003c/sup\u003e s condition, which is used to compare each wave source model as the base scenario. The North Sanriku (0 m sea level) and Kuril Trench model (0.5 m sea level) show comparable KS1/KS2 ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The Komagatake and Kuril models and the Komagatake and North Sanriku models are possible combinations as the wave sources of the KS1 and KS2 layers.\u003c/p\u003e \u003cp\u003eThe thickness distribution in the Komagatake model with the base scenario is similar to that of the KS1 layer on Line A and Line B in Fig.\u0026nbsp;1b (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The North Sanriku model shows convex upward patterns (30\u0026ndash;80 m in Line B), while the Kuril Trench model shows a homogeneous thin layer and thinning inland.\u003c/p\u003e \u003cp\u003eIn addition to TSV and thickness distribution patterns, the estimated grain size composition and sedimentary structures at a given site can be compared to the observed sand layer. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the hydraulic conditions and grain size composition at the intersection of Line A and Line B. The Komagatake model displays that the runup flow by the first flow directly inundated the ridge in the northeast direction. Although 0.2- and 0.3-mm components are dominated by suspension, the 1.5-mm component transported by bedload is also included, indicating a poor sorting layer. The North Sanriku model shows inundation from the river mouth as indicating the westward flow. Only the 0.2-mm component is transported by two flows in two halves (the first and third one). No significant changes in grain size components were observed for different sea levels, roughness coefficients, or topography in this model. The Kuril Trench model shows the leading northwestward inflow from the river mouth and the following northeastward inflow over the beach ridge at ~\u0026thinsp;150 min. The 0.2- and 0.3-mm components are transported by the northwest flow, while the 0.2 mm component is immediately eroded by the following northeastward flow; finally, the dominant 0.3 mm and a small amount of 1.5 mm components are deposited.\u003c/p\u003e \u003cp\u003eThe Komagatake model showed high agreement with the KS1 layer in TSV and thickness distribution pattern (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The KS1 layer shows the inverse to normal grading structure and is induced by the inundation over the beach ridge because it contains coarse-grained sand that is constated of the beach ridge and beach sand (Nakanishi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Komagatake model also reproduces the features of the KS1 layer in the grading structure by bedload and suspension and poor sorting (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the KS2 layer, the Kuril Trench and the North Sanriku models are candidates based on the TSV ratio compared to the Komagatake model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The sand layer estimated by the Kuril Trench model was dominated by the 0.3-mm component and it was similar with the that of the KS2 layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and generally agrees with the KS2 distribution pattern as homogeneous thin layers and the thinning trend behind the beach ridge (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The North Sanriku model consists with the TSV of KS1 and KS2; however, the inundation from the river mouth cannot transport coarse sand. The estimated layers are different from the KS1 and KS2 layers in terms of the distribution patterns and grain size distributions. The wavelength is longer than in other models due to the deep focal depth (Fig.\u0026nbsp;1e) and may result in the low velocity was not sufficient to transport\u0026thinsp;\u0026gt;\u0026thinsp;0.3-mm components (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Therefore, if the sand source consisted of coarse grains, the tsunami could have existed without leaving tsunami deposits, which should be noted for disaster prevention. The T10N5S25 model (or T10w70N5S25 model) is the most reasonable wave source for the KS2 layer because it is a model based on the 17th-century tsunami deposits in eastern Hokkaido and can comprehensively reproduce the tsunami deposit distributions in the Hidaka region including the Kabari as well as Erimo and Shizunai areas.\u003c/p\u003e"},{"header":"7 Conclusion","content":"\u003cp\u003eTo constrain the maximum magnitude of a giant earthquake in the Kuril Trench, sediment transport modeling was used to identify the wave source of two layers of 15th\u0026ndash;17th-century tsunami deposits observed in Kabari, far from the Kuril Trench. The Komagatake, Kuril Trench, and North Sanriku models show inundations covering the observed sand layers. The Komagatake and Kuril models show good agreement with the observed KS1 and KS2 layers, respectively on not only the transported sediment volumes and thickness distribution patterns but also grain size compositions. The North Sanriku model shows similar wave heights to the Kuril Trench model; however, the flow velocities were not enough to transport coarse grains over the beach ridge. The sediment transport modeling provides useful information to identify wave sources of tsunami deposits by comparing sediment features and thickness distribution patterns rather than only an inundation extent. The Komagatake and Kuril Trench models can reproduce the tsunami deposits in Kabari without modifying the inflow debris volume and fault parameters and explain the distribution of tsunami deposits along the western Pacific coast of Hokkaido without Mw\u0026thinsp;\u0026gt;\u0026thinsp;9 giant earthquakes (e.g., Cabinet Office \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Kuril Trench tsunami in Kabari is smaller than the Komagatake tsunami in the 17th century. This information is a significant constraint for identifying the maximum magnitude of earthquakes in the Kuril Trench and contributes to the investigation of the tsunami sources in the Kuril and Japan Trenches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number JP23K13177.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work relies on open-source code, namely Delft3D-Flow version 3.15 (Deltares, 2020) for sediment transport modeling (https://svn.oss.deltares.nl/repos/delft3d/tags/delft3d4/7545/). This code requires registration to download.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eR.N. processed the numerical simulation and drafted the manuscript. T.Kimura provided the tsunami wave data of the Komagatake tsunamis and reviewed the manuscript. T.Kanno reviewed and edited the manuscript. All authors approved the final version of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbe T, Goto K, Sugawara D (2012) Relationship between the maximum extent of tsunami sand and the inundation limit of the 2011 Tohoku-oki tsunami on the Sendai Plain, Japan. Sediment Geol 282:142\u0026ndash;150. doi.org/10.1016/j.sedgeo.2012.05.004.\u003c/li\u003e\n\u003cli\u003eAnnaka T, Ohota K, Mogi H, Yoshida I, Takao M, Soraoka H (1999) Study on tsunami inversion method considering shallow water deformation effects (The title was translated into English by the author). Proc Coast Eng 46:341\u0026ndash;345.\u003c/li\u003e\n\u003cli\u003eCabinet Office (2022) Report on Source Fault Models, Seismic Intensity Distribution and Tsunami Height by Giant Earthquakes along the Japan Trench and the Kuril Trench (The title was translated into English by the author). https://www.bousai.go.jp/jishin/nihonkaiko_chishima/model/pdf/honbun.pdf\u003c/li\u003e\n\u003cli\u003eChikasada N (2020) Global tsunami Terrain Model. NIED doi:10.17598/NIED.0021\u003c/li\u003e\n\u003cli\u003eDeltares (2020) Delft3D-FLOW: Simulation of multi-dimensional hydrodynamic flows and transports phenomena, including sediments. User Manual, Version 3.15, Revision 66766 [Software]. Delta. Retrieved from https://oss.deltares.nl/web/delft3d/get-started\u003c/li\u003e\n\u003cli\u003eDourado F, Costa P J M, La Selle S, Andrade C, Silva A N, Bosnic I, Gelfenbaum G (2021) Can Modeling the Geologic Record Contribute to Constraining the Tectonic Source of the 1755 CE Great Lisbon Earthquake? Earth Space Sci 8:e2020EA001109. doi.org/10.1029/2020EA001109.\u003c/li\u003e\n\u003cli\u003eEbina Y, Imai K (2014) Tsunami traces survey of the 1611 Keicho Ohsyu earthquake tsunami based on historical documents and traditions. Rep Tsunami Eng 31:139\u0026ndash;148.\u003c/li\u003e\n\u003cli\u003eGeospatial Information Authority of Japan (2018) Information on the 2018 Hokkaido Eastern Iburi Earthquake. https://www.gsi.go.jp/BOUSAI/H30-hokkaidoiburi-east-earthquake-index.html#8\u003c/li\u003e\n\u003cli\u003eHatori T (1973) Estimations of Tsunami Magnitude and Wave Source for the Hachinohe-oki Tsunami of 1856. Zishin 26:204\u0026ndash;205 (in Japanese).\u003c/li\u003e\n\u003cli\u003eHeadquarters for Earthquake Research Promotion (2017) Long-term evaluation of subduction zone earthquakes around the southern Kuril-Kamchatka Trench (3rd edition). https://www.jishin.go.jp/main/chousa/kaikou_pdf/chishima3.pdf\u003c/li\u003e\n\u003cli\u003eHokkaido Government (2012) Map Showing Areas with the Potential for Flooding from Tsunami in Pacific Coast of Hokkaido, northern Japan. http://www.pref.hokkaido.lg.jp/sm/ktk/bsb/tunami/index.htm.\u003c/li\u003e\n\u003cli\u003eIoki K, Tanioka Y (2016) Re-estimated fault model of the 17th century great earthquake off Hokkaido using tsunami deposit data. 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J Geophys Res 127:e2022JF006721. doi.org/10.1029/2022JF006721.\u003c/li\u003e\n\u003cli\u003eNakanishi R, Ashi J, Miyairi Y, Yokoyama Y (2022a) Holocene coastal evolution, past tsunamis, and extreme wave event reconstructions using sediment cores obtained from the central coast of Hidaka, Hokkaido, Japan. Mar Geol 443:106663. doi.org/10.1016/j.margeo.2021.106663.\u003c/li\u003e\n\u003cli\u003eNakanishi R, Ashi J, Miyairi Y, Yokoyama Y (2022b) Spatial Extent of Mid-To Late-Holocene Sedimentary Record of Tsunamis Along the Southern Kuril Trench, Hokkaido, Japan. Geochem Geophys Geosys 23:e2022GC010334. doi.org/10.1029/2022GC010334.\u003c/li\u003e\n\u003cli\u003eNakanishi R, Ashi J, Okamura S, Yokoyama Y, Miyairi Y (2024) Understanding paleo-earthquakes in the Kuril Trench based on Late-Holocene tsunami deposits in the distal region from wave sources, northern Hidaka, Hokkaido, Japan. PLOS ONE 19:e0298720. https://doi.org/10.1371/journal.pone.0298720 \u003c/li\u003e\n\u003cli\u003eNishimura Y, Miyaji N (1995) Tsunami deposits from the 1993 Southwest Hokkaido earthquake and the 1640 Hokkaido Komagatake eruption, northern Japan. Pure Appl Geophys 144:719\u0026ndash;733.\u003c/li\u003e\n\u003cli\u003eSatake K (1989) Inversion of tsunami waveforms for the estimation of heterogeneous fault motion of large submarine earthquakes: The 1968 Tokachi-oki and 1983 Japan Sea earthquakes. Journal Geophys Res 94:5627\u0026ndash;5636. doi.org/10.1029/JB094iB05p05627.\u003c/li\u003e\n\u003cli\u003eSugawara D, Takahashi T, Imamura F (2014) Sediment transport due to the 2011 Tohoku-oki tsunami at Sendai: Results from numerical modeling. Mar Geol 358:18\u0026ndash;37. doi.org/10.1016/j.margeo.2014.05.005.\u003c/li\u003e\n\u003cli\u003eSugawara D, Yu N T, Yen J Y (2019) Estimating a Tsunami Source by Sediment Transport Modeling: A Primary Attempt on a Historical/1867 Normal-Faulting Tsunami in Northern Taiwan. J Geophys Res 124:1675\u0026ndash;1700. doi.org/10.1029/2018JF004831.\u003c/li\u003e\n\u003cli\u003eOkamura Y, Namegaya Y (2011) Reconsideration of the 17th century Kuril multi-segment earthquake. Ann Rep Active Fault Paleoearthq Res 11:15\u0026ndash;20 (in Japanese, with English abstract).\u003c/li\u003e\n\u003cli\u003eTakashimizu Y, Sagayama T, Nishina K, Oka T, Nakamura Y, Nishimura Y (2007) A 17th-century tsunami deposit discovered on the eastern Iburi coast, Hokkaido, northern Japan. Quat Res 46:119\u0026ndash;130 (in Japanese, with English abstract). \u003c/li\u003e\n\u003cli\u003eTakashimizu Y, Nishina K, Kawakami G, Sato Y, Okamura S, Nakanishi R, et al. (2017) Identification of a 17th-century tsunami deposit on the northern Hidaka coast, Hokkaido, northern Japan. Quat Res 56:1\u0026ndash;9 (in Japanese, with English abstract).\u003c/li\u003e\n\u003cli\u003eYamanaka Y, Tanioka Y (2022) Short-wave run-ups of the 1611 Keicho tsunami along the Sanriku Coast. Prog Earth Planet Sci 9:37. doi.org/10.1186/s40645-022-00496-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sediment transport modeling, Tsunami deposit, Kuril Trench, Hokkaido, Mt. Komagatake, Volcanic tsunami","lastPublishedDoi":"10.21203/rs.3.rs-4379459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4379459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSediment transport modeling is a powerful tool for identifying wave sources of paleo-tsunami deposits because it can reproduce not only the thickness distribution but also the sediment features. The giant earthquakes in the Kuril Trench have uncertainties in the magnitude depending on the extent to which tsunami deposits widely distributed along the Pacific coast of Hokkaido can be correlated with each other. Multiple tsunami deposits have been found in Kabari, northern Hidaka, Hokkaido, and their wave sources are expected to provide a significant constraint on the tsunami magnitude. We reproduce two layers of tsunami deposits around the 17th century with sediment transport modeling using possible wave source candidate models. The Mt. Komagatake collapse and Kuril Trench earthquake models reproduce the two layers of tsunami deposits, indicating the tsunami distributions along the Pacific coast of Hokkaido are reproduced without Mw\u0026thinsp;\u0026gt;\u0026thinsp;9 earthquake models.\u003c/p\u003e","manuscriptTitle":"Wave sources of the 17th-century tsunami deposits in western Hokkaido, Japan using sediment transport modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 05:37:46","doi":"10.21203/rs.3.rs-4379459/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f91aaec-b8d8-4b48-9cef-0c58bc0cbda4","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-27T13:03:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 05:37:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4379459","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4379459","identity":"rs-4379459","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}