The National Research Institute for Earth Science and Disaster Resilience (NIED) is working on three tasks: predicting disasters, preventing damage, and realizing speedy reconstruction and recovery efforts in the event of natural disasters such as earthquakes, tsunamis, volcanic eruptions, landslides, torrential rains, blizzards, and ice storms.
In the last two years of the NIED’s fourth mid/long term plan period, which began in 2016, the 2016 Kumamoto earthquake (M6.5 and M7.3), the heavy rainfall in the Northern Kyushu District in July 2017, and the heavy rain event of July 2018 are listed as “named” disasters, named by Japan Meteorological Agency. In addition, there were other disasters: an avalanche accident on Nasudake in 2017, an earthquake (M6.1) with its epicenter in northern Osaka, an eruption of Kirishimayama (Shinmoedake and Ioyama) and a phreatic eruption of Kusatsu-Shiranesan in 2018.
The results of research done on the above-mentioned disasters and the latest results of ongoing projects in each research division and center were compiled as the second NIED special issue of the Journal of Disaster Research (JDR). In this special issue, we are delighted to present ten papers on three topics: climatic disasters, seismic disasters, and integrated research on disaster risk reduction. In particular, this special issue contains three papers on the above-mentioned heavy rainfall in the Northern Kyushu District in July 2017 and two papers related to the Kumamoto earthquake.
Although the achievements detailed in these papers are the results of individual research, the NIED hopes that these results as a whole will be fully utilized to promote science and technology for disaster risk reduction and resilience. The NIED hopes that this special issue awakens the readers’ interest in new research and, of course, creates an opportunity for further collaborative works with us.
The heavy rain in Northern Kyushu District on July 5, 2017 caused a sediment disaster, resulting in the loss of many lives and damage to buildings. In this study, the primary causes (topography and geology) and trigger factors (rainfall) for the sediment disaster were spatially analyzed to examine factors contributing to slope failure. As a result, it was found that the number of slope failures was highest in metamorphic rock areas and the occurrence density of the landslides was highest in plutonic rock areas. In addition, the slope angle of the slope-failure source point was sizable in volcanic rock areas and many landslides occurred in the valley-formed areas. A rainfall analysis showed that the Akatani, Shirakitani, Sozu, Kita, Naragaya, Myoken, Katsura river basins and Ono, Ohi, Sata, Inaibaru river basins are different rainfall distributions, which significantly affected the slope-failure occurrence density.
Torrential rainfall associated with linear precipitation systems occurred in Northern Kyushu, Japan, during July 5–6, 2017, causing severe damage in Fukuoka and Oita Prefectures. According to our statistical survey using ground rain gauges, the torrential rainfall was among the heaviest in recorded history for 6- and 12-h accumulated rainfall, and was unusual because heavy rain continued locally for nine hours. The predictability of precipitation associated with linear precipitation systems for this event was investigated using a cloud-resolving numerical weather prediction model with a horizontal grid interval of 1 km. The development of multiple linear precipitation systems was predicted in experiments whose initial calculation time was from several hours to immediately before the torrential rain (9:00, 10:00, 11:00, and 12:00 Japan Standard Time on July 5), although there were some displacement errors in the predicted linear precipitation systems. However, the stationary linear precipitation systems were not properly predicted. The predictions showed that the linear precipitation systems formed one after another and moved eastwards. In the relatively accurate prediction whose initial time was 12:00 on July 5, immediately before the torrential rainfall began, the forecast accuracy was evaluated using the 6-h accumulated precipitation (P6h) from 12:00 to 18:00 on July 5, the period of the heaviest rainfall. The average of the P6h in an area 100 km×40 km around the torrential rainfall area was nearly the same for the analysis and the prediction, indicating that the total precipitation amount around the torrential rainfall area was predictable. The result of evaluating the quantitative prediction accuracy using the Fractions Skill Score (FSS) indicated that a difference in location of 25 km (50 km) or greater should be allowed for in the models to produce useful predictions (those defined as having an FSS ≥0.5) for the accumulated rainfall of P6h ≥50 mm (150 mm). The quantitative prediction accuracy examined in this study can be basic information to investigate the usage of predicted precipitation data.
The heavy rainfall event that occurred on 5–6 July 2017 in Northern Kyushu, Japan, caused extensive flooding across several mountainous river basins and resulted in fatalities and extensive damage to infrastructure along those rivers. For the periods before and during the extreme event, there are no hydrological observations for many of the flooded river basins, most of which are small and located in mountainous regions. We used the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, a physically based model, to acquire more detailed information about the hydrological processes in the flood-affected ungauged mountain basins. We calibrated the GSSHA model using data from an adjacent gauged river basin, and then applied it to several small ungauged basins without changing the parameters of the model. We simulated the gridded flow and generated a map of the possible maximum flood depth across the basins. By comparing the extent of flood-affected areas from the model with data of the Japanese Geospatial Information Authority (GSI), we found that the maximum flood inundation areas of the river networks estimated by the GSSHA model are sometimes less than those estimated by the GSI, as the influence of landslides and erosion was not considered in the modeling. The model accuracy could be improved by taking these factors into account, although this task would be challenging. The results indicated that simulations of flood inundation in ungauged mountain river basins could contribute to disaster management during extreme rain events.
A new method was proposed for the probabilistic projection of future climate that introduced quantile mapping to a regression method using a multi-model ensemble (QM_RMME). Results of this method were then compared with those of the traditional regression method (RMME). Six stations in Japan where 100 year observation records were available were used to evaluate the performance of the methods. An initial 50-year period (1901–1950) was used to develop the regression models and the final period (1951–2000) was used for evaluation. Results showed that the estimation errors at the 50th and 90th percentile were smaller for QM_RMME as compared to RMME at most sites. Conversely, when the model development and evaluation periods were limited to 20 years (1901–1920 and 1951–1970, respectively), the 90th percentile error was larger for QM_RMME. This was attributed to quantile mapping resulting in over-fitting of the data during the model development period. Furthermore, the QM_RMME error increased when the difference of observations between the model development and verification periods was large. Therefore, results indicated that the RMME method was more stable for relatively short data verification periods.
In weather forecasts, the intensity of rainfall is often expressed either as a quantitative value – the amount of rainfall per hour – or using qualitative language such as “heavy rain.” To date, however, there has been no research into the extent of rainfall that is assumed by information receivers when presented with these qualitative terms. Thus, the present study examines the correspondence between rainfall evaluation and expressions using a rainfall generator. The large-scale rainfall experiment facility owned by the National Research Institute for Earth Science and Disaster Resilience was used to generate rainfall of 60, 180, and 300 mm h-1, and 21 experiment participants experienced this rainfall without knowing the rainfall amounts. Following this, the participants were asked to give feedback using a scale that correlated numerical expressions of rainfall amounts per hour with 10 language expressions such as “heavy rain” and “downpour.” The results revealed that rainfall rates of 60, 180, and 300 mm h-1 were evaluated by the participants as 135, 223, and 311 mm h-1, respectively. The 10 language expressions were felt to be stronger than the official criteria outlined by the Japan Meteorological Agency. In addition, there was no statistical significance among several expressions, suggesting that the qualitative language used to describe different rainfall amounts by information senders were not distinguished by information receivers.
Regional disaster prevention activities must be evaluated in terms of their effectiveness and suitability, and then improved on the basis of this evaluation. Those who can evaluate such activities are required to have abundant on-site experience in and extensive knowledge on disaster prevention. However, there is a shortage of such talent, and the training and nurturing thereof requires considerable resources. To address these issues, machine learning was introduced in our previous study to automate the evaluation of such activities. In the present study, we propose the automatic generation of the evaluation model of such activities using the responses of a self-evaluation questionnaire as the input variables. The output variables are the results of a review committee consisting of experts on disaster prevention. This paper describes the application of the model to the fourth Disaster Prevention Map Contest, examines the predicted results, and discusses the application conditions and issues to be resolved.
In the 2015 earthquake in Gorkha, Nepal, damaged different kinds of structures around the Kathmandu Basin. On the other hand, in mountainous areas, it was confirmed that gabion structures such as retaining walls along roads showed their high flexibility by performing their functions. In this paper, based on the results of the damage field survey on gabion retaining walls, a full-scale shake table test is conducted to evaluate the earthquake resistance of gabion retaining walls on roads, which are a common site in Nepal. The soil container used for the full-scale shake table test has the following internal dimensions: 4.0 m height, 3.1 m width, and 11.5 m depth. Earthen bank retaining walls with height of 3 m were arranged in three rows in a perpendicular direction to the cross-section, and the ground behind the retaining wall was prepared. The sinusoidal waves of 3 Hz were applied, consisting of 2 s of gradual increase, 4 s of steady part, and 2 s of gradual decrease; the input waves were provided in four stages of acceleration amplitude. Three types of gabion retaining walls were considered, i.e., vertical-type, stepwise-type and gravity-type, and 3D terrestrial laser measurement was conducted before and after shake table test of each case. Comparison of the residual deformations of the gabion retaining walls measured by 3D terrestrial laser showed that the vertical-type wall did not collapse but tilted forward after the shake teble test. A similar damage situation was confirmed by the field survey in Nepal. The other two cases suffered only slight deformation and are considered to be effective structures for application on sites. Finally, the trial wedge method was applied to the experimental results of the vertical-type of gabion retaining and useful suggestions for future earthquake-resistant design were made by comparing the active collapse angle with the positions of deformation, such as cracking which occurred in the ground behind the retaining wall after shaking. Then, the applicability of trial wedge method and its problem in the design of gabion retaining wall were shown.
During the 2016 Kumamoto earthquakes, two earthquakes of seismic intensity 7 were observed in Mashiki Town, the foreshock (MJMA 6.5) of April 14 and the main shock (MJMA 7.3) of April 16, resulting in significant damage to structures near the fault. The distribution of damage of houses and other buildings  showed a tendency in which damage was concentrated in areas near the surface earthquake fault where the main shock presumably occurred. However, there were locations with slight damage even though they were immediately above the fault and locations with a relatively significant damage even though they were far from the fault. These phenomena are highly likely to be a result of soil structure. First, we built an initial geologic model by collecting boring data in areas of the Kumamoto plain near the fault where damage was severe. Next, we observed microtremors, collected earthquake observational records, and adjusted the layer thickness and S-wave velocity of the initial geologic model. Finally, we built a shallow and deep integrated ground model, compared it to the building damage distribution, and discussed the implications.
In order to understand the damage situation immediately after the occurrence of a disaster and to support disaster response, we developed a method to classify the degree of building damage in three stages with machine-learning using road-running survey images obtained immediately after the Kumamoto earthquake. Machine-learning involves a learning phase and a discrimination phase. As training data, we used images from a camera installed in the travel direction of an automobile, in which the degree of damage was visually categorized. In the learning phase, class separation is carried out by support vector machine (SVM) on a basis of a feature calculated from training patch images for each extracted damage category. In the discrimination phase, input images are provided with raster scan so that the class separation is carried out in units of the patch image. In this manner, learning, discrimination, and parameter tuning are repeated. By doing so, we developed a damage-discrimination model for each patch image and validated the discrimination accuracy using a cross-validation method. Furthermore, we developed a method using an optical flow for preventing double counting of damaged areas in cases where an identical building is captured in multiple photos.
The West Asian region is a tectonically active area due to crustal deformation; the associated earthquakes occur on a large scale and have been recorded from the historical period to the present. Investigating the most suitable solution for this crustal movement will contribute to this region’s earthquake and tsunami disaster mitigation. The most reliable parameters were defined by researchers and applied with a non-uniform distribution in the fault plane based on Papadimitriou et al . The calculated AD 365 earthquake waveform provides an indication of maximum acceleration using the stochastic Green’s function method with the selected parameters. Using this estimation, damage to masonry structures can be calculated. The ancient Crete cities of Aptra and Chania were both hit by the AD 365 earthquake. Aptera, built on out-cropping rock, would have been 80% destroyed. In comparison, Chania, in northwest Crete, would have been completely destroyed because it was built on thick sedimentary layers. The subsurface composition at Chania would have made it a high seismic intensity area. This earthquake was followed by a tsunami that devastated the southern and eastern coasts of the Mediterranean. Based on these results, risk mitigation from seismic and tsunami events should focus on high densely populated areas with thick sedimentary layers in the Mediterranean.
The damages caused by cyber-attacks are becoming larger, broader and more serious and to include monetary losses and losses of lifeline. Some cyber-attacks are arguably suspected to be parts of national campaigns. Under such circumstances, the public sector must endeavour to enhance the national cybersecurity capacities. There are several benchmarks for national cybersecurity, i.e., a snapshot relative assessment of a nation’s cybersecurity strength at a global level. However, by considering the development of technology, attackers’ skills and capacities of other nations, we believe that it is more important to review the national strategy for cybersecurity capacity enhancement and to ensure that the national capacity advances adequately in the coming years. We propose a method of reviewing national strategies. Additionally, we performed a trial review of the Japanese cybersecurity strategy using the Cybersecurity Capacity Maturity Model for Nations (CSCMMN) developed by the Global Cyber Security Capacity Centre. This trial proved to be workable because it detected various possibly inadequate (insufficient, inappropriate or inefficient, although further investigation is needed) approaches in the Japanese strategy. Moreover, the review also discovered the shortcomings of the capacity areas in the CSCMMN. We plan to improve the reviewing method and develop the improvement process of national strategies for cybersecurity capacity enhancement.
This study investigated the vertical accuracy of satellite elevation data and its effect on flood and substance transportation analysis by using a two-dimensional flood simulation model. SRTM, AW3D, and ASTER GDEM satellite elevation data for East Dhaka, Bangladesh were used for evaluating the vertical accuracy and conducting numerical analyses. A case study in 2007 was simulated for the flooding analysis. The results showed that AW3D had the highest applicability because its vertical accuracy for low-lying areas was better than that of the other products. According to the differences in the flood extent of each satellite elevation data, the simulation results of the substance transportation analysis showed different spreading conditions. Furthermore, differences in the flood velocity and direction affected the distribution of the deposited substance.
Cities are experiencing the need to protect people, natural and man-made resources and productive capacities against the risks posed by climate change related events. This descriptive study examines the flood preparedness of hospitals at sub-district, district, provincial, and regional levels in Thailand. In-depth interviews were conducted at fourteen hospitals in five provinces, including Nonthaburi, Pathum Thani, Nakhon Pathom, Samut Prakan, and Samut Sakhon provinces. We used content analysis for this qualitative descriptive study. The highlight findings of the study revealed that lack of disaster standard operating procedures for emergency response to flood, lack of risk mapping and vulnerability assessment, and shortage of trained risk communicators. Increasing performance in disaster and emergency preparedness and response in the hospitals is essential. We suggest that the hospitals and relevant government agencies require appropriate mechanism to enhance disaster and emergency preparedness and response. Our findings demonstrate evidence of flood preparedness, which raises concerns regarding holistic approach to disaster and emergency preparedness and response for health needs at all levels that warrant further investigation.