Recycling of clastics in coastal areas inferred from quantitative analysis of reworked radiocarbon samples

Studies of the evolution of coastal lowlands since the Last Glacial Maximum (LGM) typically ignore radiocarbon data from sediment samples that have undergone reworking. However, these samples contain information on their sediment sources and the timing of their redeposition. We analyzed 738 radiocarbon dates obtained from shell and plant material in samples of post-LGM coastal sediment from north of Tokyo Bay, Japan. Of these samples, 245 (33%) were reworked. Furthermore, the percentage of reworked samples and their average age offsets increased with the depth of the water environment (terrestrial, 15% and 360 ± 250 years, respectively; intertidal, 26% and 470 ± 620 years; subtidal, 39% and 550 ± 630 years). Taking these radiocarbon samples as a proxy for clastic material, our results imply that channel erosion accounted for relatively little clastic removal in the terrestrial and intertidal environments over short timescales, whereas ~ 40% of clastics were removed by storm winnowing and transported in stepwise fashion to deeper water over longer timescales and ~ 60% in the subtidal environment were transported by floods directly from river mouths. These findings imply that radiocarbon ages from reworked samples can be used to quantify clastic recycling processes and their history in coastal areas.

Periodic Sea-Level Oscillation in Tokyo Bay Detected with the Tokyo-Bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD)

Meteorological-tsunami-like (or meteotsunami-like) periodic oscillation was muographically detected with the Tokyo-Bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD) deployed in the underwater highway called the Trans-Tokyo Bay Expressway or Tokyo Bay Aqua-Line (TBAL). It was detected right after the arrival of the 2021 Typhoon-16 that passed through the region 400 km south of the bay. The measured oscillation period and decay time were respectively 3 hours and 10 hours. These measurements were found to be consistent with previous tide gauge measurements. Meteotsunamis are known to take place in bays and lakes, and the temporal and spatial characteristics of meteotsunamis are similar to seismic tsunamis. However, their generation and propagation mechanisms are not well understood. The current result indicates that a combination of muography and trans-bay or trans-lake underwater tunnels will offer an additional tool to measure meteotsunamis at locations where tide gauges are unavailable.

Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020.

Marine Traffic Data Clustering for Ships Route Planning

This paper is about maritime safety. The system of vessel traffic schemas is one of the key elements of sea traffic control at the arias with heavy traffic. Such system based on a set of rules and guidelines defined by traffic schemas for certain water areas. From the classic approach, vessels that are not following the guidelines do not necessarily create alarming situations at the moment, however, could lead to complex danger navigation situations with the time passed. The problem of ship route planning through the area with highly intensive traffic is considered in this paper. The importance of the problem becomes more significant these days when taking in account development of self-navigating autonomous vessels. It is expected to respect area navigation limitations while planning vessel path through the areas with identified traffic schema. One of the ways to identify navigation limitations could be trajectory pattern recognition at certain sea areas based on retrospective traffic analysis. Model representation for such task could be based on vessel moving parameters clustering. The presented model is based on solving the shortest path problem on weighted graph. There are several ways to create such weighted graphs are suggested in the paper: regular grid of vertices and edges, layer grid of vertices and edges, random grid of vertices and edges, vertices and edges identified based on retrospective data. All edges are defined as a weighted function of "desirability" of one or another vessel course for each location of sea area with consideration of identified trajectory patterns. For that the area is divided into sub areas where courses and velocity clustering is evaluated. Possible ways of clustering are discussed in the paper and the choice made in favor of subtractive clustering that does not require predefining of cluster count. Automatic Identification Systems (AIS) could be used as data source for the traffic at certain sea areas. The possibility of using AIS data available on specialized public Internet resources is shown in the paper. Although such data typically has low density, they still could well represent vessel traffic features at the certain sea area. In this paper are presenting samples of route panning for Tsugaru Straight ang Tokyo Bay.

First results of undersea muography with the Tokyo-Bay Seafloor Hyper-Kilometric Submarine Deep Detector

Tidal measurements are of great significance since they may provide us with essential data to apply towards protection of coastal communities and sea traffic. Currently, tide gauge stations and laser altimetry are commonly used for these measurements. On the other hand, muography sensors can be located underneath the seafloor inside an undersea tunnel where electric and telecommunication infrastructures are more readily available. In this work, the world’s first under-seafloor particle detector array called the Tokyo-bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD) was deployed underneath the Tokyo-Bay seafloor for conducting submarine muography. The resultant 80-day consecutive time-sequential muographic data were converted to the tidal levels based on the parameters determined from the first-day astronomical tide height (ATH) data. The standard deviation between ATH and muographic results for the rest of a 79-day measurement period was 12.85 cm. We anticipate that if the length of the TS-HKMSDD is extended from 100 m to a full-scale as large as 9.6 km to provide continuous tidal information along the tunnel, this muography application will become an established standard, demonstrating its effectiveness as practical tide monitor for this heavy traffic waterway in Tokyo and in other important sea traffic areas worldwide.

Numerical Assessment of Coastal Multi-hazard Vulnerability in Tokyo Bay

Many bays in the world are threatened by coastal hazards such as storm surge, river flood and tsunami. Since most of the existing studies have been focused on one or two of them, in this study, the assessment of coastal vulnerability caused by the three hazards was the research target. Inundation simulation is a widely used and straightforward way in coastal vulnerability assessments; however, it is computationally expensive, and considering an increase in the number of cases in multi-hazard analysis, an efficient method was proposed using an estimated overflow volume without computing inundation, which was validated by comparing with inundation simulation. It shows that when free overflow is dominant, this method is consistent with inundation simulation approach. Using Tokyo Bay as a study area, the efficient method was then applied to multi-hazard vulnerability assessment. By comparing the overflow volume maps and maximum anomaly distribution along the coasts for four types of hazards (worst storm surge; worst concurrent storm surge and river flood; worst concurrent storm surge, river flood and Tokai-Tonankai earthquake tsunami; worst concurrent storm surge, river flood and Tokyo inland earthquake tsunami), we investigated the characteristics of different types of hazards and identified the difference between single hazard and multi-hazards. The characteristic of overflow volume along the coasts is similar to that of maximum anomaly distribution, especially for only storm surge case, the multi-hazard case combining storm surge and river flood, and the multi-hazard case combining storm surge, Tokyo inland earthquake tsunami and river flood. However, for multi-hazard case combining storm surge, Tokai-Tonankai earthquake tsunami and river flood, only by the maximum anomaly distribution, it cannot reflect the real overflow volume condition. For only storm surge case and multi-hazard case combining storm surge and river flood, the head of the bay suffers the highest vulnerability while for multi-hazard cases combining storms surge, tsunami and river flood, the difference of vulnerability in the north and south of the bay is not significant. The difference of superposing method and concurrent method for computing multi-hazards was also compared. It was found that the linear superposing method tends to overestimate the total water elevation in coastal region; however, in the coasts where superposing method underestimates the multi-hazard anomalies, upgrading dikes needs to be considered by policymakers.