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Palaeo-tsunami inundation distances deduced from roundness of gravel particles in tsunami deposits.

ABSTRACT: Information on palaeo-tsunami magnitude is scientifically and socially essential to mitigate tsunami risk. However, estimating palaeo-tsunami parameters (e.g., inundation distance) from sediments is not simple because tsunami deposits reflect complex transport processes. Here, we show a new approach to estimate tsunami inundation distance based on the mixture ratio of gravels from several sources in tsunami deposits. We measured the roundness of source gravels in modern beach and fluvial deposits in a coastal valley in Japan through image analysis and then calculated the mixture ratio of both sediment types in tsunami deposits. Normalising the mixture ratios by inundation distances revealed an abrupt change in the mixture ratio at a constant percentile, regardless of tsunami magnitude. This relation allowed estimation of the inundation distance of palaeo-tsunamis during the last 4000 years.

SUBMITTER: Ishimura D

PROVIDER: S-EPMC6635351 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Palaeo-tsunami inundation distances deduced from roundness of gravel particles in tsunami deposits.

Ishimura Daisuke D Yamada Keitaro K

Scientific reports 20190716 1

Information on palaeo-tsunami magnitude is scientifically and socially essential to mitigate tsunami risk. However, estimating palaeo-tsunami parameters (e.g., inundation distance) from sediments is not simple because tsunami deposits reflect complex transport processes. Here, we show a new approach to estimate tsunami inundation distance based on the mixture ratio of gravels from several sources in tsunami deposits. We measured the roundness of source gravels in modern beach and fluvial deposit ...[more]

PMID: 31312008

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Project description:Tsunamis are natural phenomena that, although occasional, can have large impacts on coastal environments and settlements, especially in terms of loss of life. An accurate, detailed and timely assessment of the hazard is essential as input for mitigation strategies both in the long term and during emergencies. This goal is compounded by the high computational cost of simulating an adequate number of scenarios to make robust assessments. To reduce this handicap, alternative methods could be used. Here, an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inundation metrics extrema. However, for the context of Tsunami Early Warning, it is equally relevant to assess whether the networks can accurately predict whether inundation would occur or not, and its time series if it does. Hence, a set of 6776 scenarios with magnitudes in the range [Formula: see text] 8.0-9.2 were used to design several 1D CNN models at two bays that have different hydrodynamic behavior, that would use as input inexpensive low-resolution numerical modeling of tsunami propagation to predict inundation time series at pinpoint locations. In addition, different configuration parameters were also analyzed to outline a methodology for model testing and design, that could be applied elsewhere. The results show that the network models are capable of reproducing inundation time series well, either for small or large flow depths, but also when no inundation was forecast, with minimal instances of false alarms or missed alarms. To further assess the performance, the model was tested with two past tsunamis and compared with actual inundation metrics. The results obtained are promising, and the proposed model could become a reliable alternative for the calculation of tsunami intensity measures in a faster than real time manner. This could complement existing early warning system, by means of an approximate and fast procedure that could allow simulating a larger number of scenarios within the always restricting time frame of tsunami emergencies.

Project description:Large lacustrine mass movements and delta collapses are increasingly being considered as potential tsunamigenic sources and therefore hazardous for the population and infrastructure along lakeshores. Although historical reports document tsunami events in several lakes in Switzerland, and although the propagation of lake tsunamis has been studied by numerical wave modeling, only little is known about on- and offshore lacustrine tsunami deposits. In Lake Sils, Switzerland, a large prehistoric mass-movement deposit originating from the Isola Delta with a minimum estimated volume of 6.5 × 106 m3 and a basinal thickness of > 6 m in the seismic record has been identified by previous studies and radiocarbon dated to around 700 Common Era. Here, we combine (i) comprehensive sedimentological investigation of sediment cores recovered from the on- and offshore settings, (ii) mineralogical fingerprinting of the inflows from key catchments to characterize sediment provenance, and (iii) numerical tsunami modeling, to test the hypothesis of a tsunamigenic delta collapse in Lake Sils. We observe a clastic event deposit consisting of coarse-grained, fining-upward sand overlying an organic-rich peat deposit in the shallow water. This layer thins and fines landward on the coastal plain. Toward the deeper water (20-40 m), the deposit transforms into a thicker and more heterogeneous sediment package with multiple sequences of fining-upward sand and a well-pronounced clay cap at the top. Radiocarbon dating of the peat underlying the event deposit yields a maximum age of 225-419 calibrated Common Era. The tsunami models, which indicate wave heights reaching up to 5 m, simulate areas of inundation that coincide with the location of event deposits. Based on our results, we propose that the historically undocumented Isola Delta collapse generated a basin-wide tsunami that inundated the lakeshore, transporting large amounts of unconsolidated sediment along the lakeshore toward the coastal plain and into the deeper lake basin.Supplementary informationThe online version of this article (10.1007/s11069-021-04533-y) contains supplementary material, which is available to authorized users.

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Palaeo-tsunami inundation distances deduced from roundness of gravel particles in tsunami deposits.

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Palaeo-tsunami inundation distances deduced from roundness of gravel particles in tsunami deposits.

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