The 2018 Hokkaido Eastern Iburi earthquake on September 6, 2018 caused thousands of landslides, resulting in catastrophic sediment disasters which damaged lives, homes, agriculture, forestry and lifelines. To estimate the number, area and sediment volume of landslides caused by the earthquake, we conducted the elevation differential analysis with LiDAR data for 2 periods before and after the earthquake. We also estimated the fallen tree volume caused by landslides using forest register data. The number of landslides was found to be 7,142 across the four towns, Atsuma, Mukawa, Abira, and Yuni. 6,242 of them were shallow landslides on the slopes covered by highly-weathered tephra deposits and 900 of them were deep-seated landslides. Due to the limited LiDAR data available, the landslide depth was estimated for 509 shallow landslides and 35 deep-seated landslides by the elevation difference analysis, and the regression analysis with power-law function was conducted between landslide area and sediment volume. Using regression equations obtained, the sediment volume generated by each landslides were estimated. In results, the sediment volumes generated by shallow landslides and deep-seated landslides were found to be 64.7 million m³ and 80.2 million m³, respectively, resulting in the total of 144.9 million m³. The total amount of trees damaged by landslides were also estimated using forest register data before the earthquake, and found to be over 654,000 m³ in stem volume. These results can be fundamental data not only for the disaster prevention planning, sediment management and forest improvement planning in the local governments, but also for further research in sediment disasters and prevention in the future.

Sabo soil-cement utilization is a construction method that constructs and reinforces Sabo structures. A strength of Sabo soil-cement is affected by a synergism obtained from compaction of the soil and induration of Sabo soil-cement by hydration reaction in case of INSEM method. At this time, the soil indurates under conditions where a cement hydrate spread throughout the soil, and especially, the amount of cement is proportional to the compressive strength. If the cement hydrate cannot spread all over the soil, ‘the indurating space' and ‘the un-indurating space' arise in the soil. Herein, the strength properties of Sabo soil-cement gradually turn from strength properties of Sabo soil-cement to strength properties of cement by an increase of a unit cement amount. Then, we investigated the change of the strength properties, using a consolidated-drained triaxial compression test on soils. It based on a change between the adhesive force of Sabo soil-cement and the change of strength properties. The more amount of fine fraction increases, the more cement hydrates cannot spread all over the soil. The peak value of compressive strength is not clear despite fine-grained fraction content when the cement is less than 50 kg/m³. The fine-grained fraction content of soil reduced less than 20% to appear strength of the Sabo soil-cement. In addition, Sabo soil-cement mixes the cement amount fewer, and the shear resistance angle increase. On the other hand, Sabo soil-cement mixes the cement amount more, and the shear resistance angle decrease conversely. Lastly, the fracture morphology of the soil-cement test-piece gets to barrel fracture which swells the center about strength properties of soil, and comes to cleavage like lengthwise crack or slanting shear fracture like concrete.

Simulation of hydrograph is important in planning measures against sediment and floods damages caused by heavy rainfall. However, since the applicability of the parameters to disasters of different scales is not clear, the issue is whether large-scale disasters can be simulated with the parameters identified from the past data of small and mediumsized floods. Taking the sediment and flood damage in Marumori Town, Miyagi Prefecture in 2019 as an example, we identified the parameters p and k of the storage function method for small and medium-scale floods and examined the reproducibility for large-scale floods. Recent hydrological studies suggested that if the rainfall magnitude became large, the watershed should be the wettest condition and the storage-runoff function became close to catchment-specific relationship. Thus, here we hypothesized that if we used relatively large rainfall data to calibrate parameters, the calibrated parameters might be effective for prediction of hydrograph in the extremely large rainfall event, although the calibrated rainfall event on a scale was not large enough to cause a disaster. When the calibrated p was 0.2 to 0.3, the fluctuation of the observed hydrograph and the peak discharge could be reproduced successfully. However, some of calibrated parameter sets did not work well. This suggested that the wetness of catchment was not sufficient for the entire catchment area to contribute to the runoff in these rainfall events. Therefore, we conclude that calibrating the parameters with relatively large events may predict hydrographs with extremely large events, but to obtain reasonable parameter values, we still need to pay attention to the wetness contribution area.

Grain-size analysis is arguably at the base of sediment analysis, but shape analysis is often reduced to empirical equations from a few parameters and micro-variability within a single grain-size class from one grain to another is a methodology in its infancy. In this study, we investigate an image analysis technique to automatically analyze the particle shape and roughness using gravels from pyroclastic flow and debris-flow and water-borne transport deposits at Unzen Volcano. The purpose of this study is to investigate the effective shape coefficients and to evaluate the image analysis method by classifying the sediment using the shape coefficients and contour signals, in order to define debris-flow deposits from pyroclastic-flow material. The results of the Principal Component analysis suggested that the shape coefficients could be a first indicator to classify sediments with different depositional processes. From this dataset, a logistic regression analysis, which predicts the location of sediment sampling (Gully or Wall) has also shown that the shape coefficient AR (Aspect Ratio) provides a differentiation based on the sediment transport process, but it is the wavelet analysis using the particle contour signal that expressed the clearer distinction between the different samples. It showed that the amplitude is larger for the walls than for the gully in the high frequency band. This indicates that the surface of the sample collected from the walls has fine irregularities, compared to those that travelled in the channel. It was thus found that it is possible to differentiate otherwise homogeneous sediments coming from the same source, just based on their transport process, using shape and roughness information, even if the grain-size does not provide a clear distinction between the samples. The entire procedure from image processing to the calculation of shape coefficients was performed in Python, making the analysis inexpensive, simple, and flexible.