Journal of the Japan Society of Erosion Control Engineering
Online ISSN : 2187-4654
Print ISSN : 0286-8385
ISSN-L : 0286-8385
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Displaying 1-8 of 8 articles from this issue
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Technical Papers
  • Yoshito OGAWAUCHI, Osamu YOKOYAMA, Megumi KOSUGI, Gaku KITAMOTO
    2024 Volume 76 Issue 5 Pages 3-14
    Published: January 15, 2024
    Released on J-STAGE: January 15, 2025
    JOURNAL OPEN ACCESS

    In 2011, Typhoon No.12 caused the occurrence of many deep-seated landslides that formed landslide dams in the Kii Peninsula. Since then, many research publications about deep-seated landslides have been published, and methods to extract slopes that are at risk have been proposed. However, the occurrence mechanism of the deep-seated landslides in 2011 is not entirely clear. Field surveys and observation results in each area have accumulated since the disaster. In this study, these results were focused with topography, geology, and hydrology related to deep-seated landslides, the primary causes in each area were organized, and the deep-seated landslide occurrence mechanism was investigated from the phenomenon with many similarities in each area. The survey areas are 8 districts, Hiyamizu district, Shimizu district, Kitamata district, Akadani district, Nagatono district, Kuridaira district, Mikoshi district and Iya district, where many survey results have been accumulated in the Kii Peninsula. The phenomena with many similarities about primary causes and triggers were summarized as follows. The primary causes of these deep-seated landslides included cliff slope resulting from gravitational slope deformation, slope failure caused by the erosion of fragile geology at the base, and loosening bedrock at deep depths because of prolonged bedrock creep. And trigger was that groundwater was supplied from a wide area even after the peak of heavy rainfall and high groundwater pressure remained for a long period. Considering these primary causes and triggers, we proposed the mechanism of the deep-seated landslide that occurred in the Kii Mountains during Typhoon No.12 in 2011.

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  • Toshiyuki SAKAI, Yoshiharu UCHIDA, Natsumi MUNECHIKA, Ichiki KAWAMO ...
    2024 Volume 76 Issue 5 Pages 15-24
    Published: January 15, 2024
    Released on J-STAGE: January 15, 2025
    JOURNAL OPEN ACCESS

    In September 2011, Typhoon No.12 (Typhoon Ta las) caused many deep-seated landslides in southern Nara and Wakayama prefectures due to rainfall far exceeding the total precipitation of 1,000 mm, forming landslides dams. Since landslide dam can cause extensive flood damage downstream when overflow erosion occurs, at the Kii Mountain District Sabo Office MLIT, as a risk management, the predicted precipitation and water level have been forecasted using a combination of very short range forecast of precipitation by the JMA, MSM, and GSM, and the storage function method. For the accuracy improvement, this study examined a water level prediction method that combines rainfall prediction by downscaling the MSM guidance and GSM guidance, which are currently the most accurate, with AI and a three-stage tank model that can respond to various hydrological characteristics. This predicted precipitation is based on the analyzed precipitation for the past 10 years with a 1 km grid and 1 hour interval, and a dataset with the same rough timeinterval and spatial resolution as MSM-G and GSM-G. Based on this coarse-resolution guidance rainfall, a convolutional neural network (CNN), a type of deep learning, is used to calculate the predicted precipitation on the same time and spatial scales as the analytical precipitation. The parameters in the three-stage tank model were determined to match the inflow into the landslide dam, which was determined by a water balance analysis based on hydrological observations of the landslide dam. The parameters in the three-stage tank model were determined to match the inflow into the landslide dam as determined by a water balance analysis based on hydrological observations of the landslide dam. Although a tank model can be easily adapted to rainfall with various sizes, the parameter adjustment is complicated. Therefore, the SCE-UA method, one of the optimization methods, was used to obtain the parameters for efficiency. The total precipitation in the AI prediction tends to be underestimated, but the ratio in the AI predcitiction and the observation was close to 1.0, and the three-stage tank model improved the accuracy of both the peak water level difference and the peak time difference in predicting water levels.

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Research Note
  • Koji KOSHIMIZU, Yosuke YAMAKAWA, Taro UCHIDA
    2024 Volume 76 Issue 5 Pages 25-32
    Published: January 15, 2024
    Released on J-STAGE: January 15, 2025
    JOURNAL OPEN ACCESS

    In this study, we examined the specific flow rate and electrical conductivity during various periods without rainfall in 16 watersheds of the Oigawa River system in Shizuoka Prefecture, Japan. The aim was to explore the connection between the geological structure and the spatial variability of rainfall-runoff in a mountainous region characterized by high relief and composed of accretionary sedimentary rock from the Paleozoic and Mesozoic Eras. We assessed three different geological structural conditions within these watersheds: dip slope, anti-dip slope, and strike-direction slope. The findings revealed that there was no evident correlation between the specific flow amount and the specified geological conditions of the watershed. However, in watersheds dominated by dip slopes, it is implied that the storage capacity is reduced, leading to a quicker response in rainfall-runoff. Moreover, these areas exhibit a higher contribution of rainwater runoff through the deeper bedrock layers.

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