Search Results (17)

Riparian vegetation reduces the conveyance capacity and increases the likelihood of floods. Studies that consider vegetation in flow modeling rely on unmanned aerial vehicle (UAV) data, which restrict the covered area. In contrast, this study explores advances in remote sensing and machine learning techniques to obtain vegetation data for an entire river by relying solely on satellite data, superior to UAVs in terms of spatial coverage, temporal frequency, and cost effectiveness. This study proposes a machine learning method to obtain key vegetation parameters at a resolution of 10 m. The goal was to evaluate the applicability of remotely sensed vegetation data using the proposed method on a dynamic roughness distributed runoff model in the Abukuma River to assess the effect of vegetation on the typhoon Hagibis flood (12 October 2019). Two machine learning models were trained to obtain vegetation height and density using different satellite sources, and the parameters were mapped in the river floodplains with 10 m resolution based on Sentinel-2 imagery. The vegetation parameters were successfully estimated, with the vegetation height overestimated in the urban areas, particularly in the downstream part of the river, then integrated into a dynamic roughness calculation routine and patched into the RRI model. The simulations with and without vegetation were also compared. The machine learning models for density and height obtained fair results, with an R² of 0.62 and 0.55, respectively, and a slight overestimation of height. The results showed a considerable increase in water depth (up to 17.7% at the Fushiguro station) and a decrease in discharge (28.1% at the Tateyama station) when vegetation was considered. Full article

This study investigates how urban decline and intensifying flood hazards interact to threaten Japan’s urban environments, focusing on three main dimensions. First, a fine-scale analysis of spatial shrinkage was conducted using transition potential maps generated with a maximum entropy classifier. This approach enabled the identification of neighborhoods at high risk of future abandonment, revealing that peripheral districts, such as Hirakue-cho and Shimoirino-cho, are especially susceptible due to their distance from central amenities. Second, this study analyzed the 2019 Naka River flood induced by Typhoon Hagibis, evaluating water detection performance through both a U-Net-based deep learning model applied to Sentinel-1 SAR imagery in ArcGIS Pro and the DioVISTA Flood Simulator. While the SAR-based approach excelled in achieving high accuracy with a score of 0.81, the simulation-based method demonstrated higher sensitivity, emphasizing its effectiveness in flagging potential flood zones. Third, forward-looking scenarios under Representative Concentration Pathways (RCP) 2.6 and RCP 8.5 climate trajectories were modeled to capture the potential scope of future flood impacts. The primary signal is that flooding impacts 3.2 km² of buildings and leaves 11 of 82 evacuation sites vulnerable in the worst-case scenario. Japan’s proven disaster expertise can still jolt adaptation toward greater flexibility. Adaptive frameworks utilizing real-time and predictive insights powered by remote sensing, GIS, and machine intelligence form the core of proactive decision-making. By prioritizing the repositioning of decaying suburbs as disaster prevention hubs, steadily advancing hard and soft measures to deployment, supported by the reliability of DioVISTA as a flood simulator, and fueling participatory, citizen-led ties within a community, resilience shifts from a reactive shield to a living ecosystem, aiming for zero victims. Full article

There is a limited understanding on the information-seeking behavior of international tourists during disaster response scenarios due to the lack of empirical studies on crisis communication in Japan. This study clarifies the topics generated from both international tourists and official Twitter accounts by applying the embedding Bidirectional Encoder Representations from Transformers (BERT) topic model and examines the temporal sentiment changes toward transportation and tourism using the sentiment scores obtained from topic-based Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis during disasters. A case study was conducted using Twitter data on Typhoons Faxai and Hagibis, which struck Japan in 2019. This study found differences in the topics generated among international tourists and officials in response and a continuous negative sentiment toward specific transportation services. The managerial implications of these findings regarding the use of social media in crisis communication in tourism are also discussed. Full article

The FORMOSAT-7/COSMIC-2 satellites were launched in 2019, which can provide considerably larger amounts of radio occultation (RO) observations than the FORMOSAT-3/COSMIC satellites. The radio signals emitted from the global navigation satellites system (GNSS) are received by these low Earth orbit (LEO) satellites to provide the so-called bending angle accounting for bending of the rays after penetrating through the atmosphere. Deeper RO observations can be retrieved from FORMOSAT-7/COSMIC-2 for use in RO data assimilation to improve forecasts of tropical cyclones. This study used the global model FV3GFS with the finest grid resolution of about 25 km to simulate five selected typhoons over the western North Pacific, including Hagibis in 2019, Maysak and Haishen in 2020, and Kompasu and Rai in 2021. For each case, two experiments were conducted with and without assimilating FORMOSAT-7/COSMIC-2 RO bending angle. The RO data were assimilated by the GSI 4DEnVar data assimilation system for a total period of 4 days (with 6 h assimilation window) before the typhoon genesis time, followed by a forecast length of 120 h. The RO data assimilation improved the typhoon track forecasts on average of 42 runs. However, no significantly positive impacts, in general, were found on the typhoon intensity forecasts, except for Maysak. Analyses for Maysak attributed the improved intensity forecast mainly to the improved analyses for wind, temperature, and moisture in the mid-upper troposphere after data assimilation. Consequently, the RO data largely enhanced the evolving intensity of the typhoon at a more consistent movement as explained by the wavenumber-one vorticity budget analysis. On the other hand, a noted improvement on the wind analysis, but still with degraded temperature analysis above the boundary layer, also improved track forecast at some specific times for Hagibis. The predictability of typhoon track and intensity as marginally improved by use of the large RO data remains very challenging to be well explored. Full article

Recently, Japan has been hit by more frequent and severe rainstorms and floods. Typhoon Hagibis caused heavy flooding in many river basins in central and eastern Japan from 12–13 October 2019, resulting in loss of life, substantial damage, and many flood insurance claims. Considering that obtaining accurate assessments of flood situations remains a significant challenge, this study used a geographic information system (GIS)-based analytical hierarchy process (AHP) approach to develop flood susceptibility maps for the Abukuma, Naka, and Natsui River Basins during the Typhoon Hagibis event. The maps were based on population density, building density, land-use profile, distance from the river, slope, and flood inundation. A novel approach was also employed to simulate the flood inundation profiles of the river basins. In addition, a crosscheck evaluated the relationship between flood insurance claims and the developed flood risk zones within the river basins. Over 70% of insurance claims were concentrated in high to very high risk zones identified by the flood susceptibility maps. These findings demonstrate the effectiveness of this type of assessment in identifying areas that are particularly vulnerable to flood damage, which can be a useful reference for flood disaster management and related stakeholder concerns for future extreme flood events. Full article

Hydraulic models of rivers are essential for vulnerability assessment in disaster management. This study simulates the 2019 Typhoon Hagibis at the Nanakita River using a dynamic roughness model. The model estimates the roughness of the river on a pixel level from the relationship between the Manning roughness coefficient and the degree of submergence of vegetation. This degree is defined as the ratio of water depth to plant height. After validating the model, the effect of vegetation on the water level in different seasons from April 2020 to March 2021 was assessed. The vegetation area and height were obtained on a pixel level using unmanned aerial vehicle photogrammetry. The dynamic roughness model showed that the water level profile increased by 7.03% on average. The seasonal effect of vegetation was observed, revealing a strong correlation between variations in the vegetation conditions and water level profile. This approach may help mitigate flood damage by indicating the factors that can increase the risk of flooding. Full article

Various multi-source observational platforms have enabled the exploration of ocean dynamics in the Northwest Pacific Ocean (NPO). This study investigated daily oceanic variables in response to the combined effect of the 2019 super typhoon HAGIBIS and the Kuroshio current meander (KCM), which has caused economic, ecological, and climatic changes in the NPO since August 2017. During HAGIBIS, the six-hourly wind speed data estimated a wind stress power ($P_w$) which strengthened around the right and left semicircles of the typhoon, and an Ekman pumping velocity (EPV) which intensified at the center of the typhoon track. As a result, firstly, the sea temperature (ST) decreased along a boundary with a high EPV and a strong cyclonic eddy area, and the mixed layer depth (MLD) was shallow. Secondly, a low sea salinity (SS) concentration showed another area where heavy rain fell on the left side of the typhoon track. Phytoplankton bloom (PB) occurred with a large concentration of chlorophyll a (0.641 $\mathrm{mg}/{\mathrm{m}}^3$) over a wide extent (56,615 ${\mathrm{km}}^2$; above 0.5 $\mathrm{mg}/{\mathrm{m}}^3$) after one day of HAGIBIS. An analysis of a favorable environment of the PB’s growth determined the cause of the PB, and a shift of the subsurface chlorophyll maximum layer (SCML; above 0.7 $\mathrm{mg}/{\mathrm{m}}^3$) was estimated by comprehensive impact analysis. This study may contribute to understanding different individually-estimated physical and biological mechanisms and predicting the recurrence of ocean anomalies. Full article

An empirical orthogonal function (EOF) analysis was performed for the inner core of Typhoon Hagibis (2019) in the intensification phase. The Himawari–8 geostationary infrared (IR) brightness temperature (BT) collocated at the Hagibis’s center was combined with the IR BT simulated by a radiative transfer model, with 1 km ensemble simulations conducted by an atmosphere model and the coupled atmosphere–wave–ocean model. The ensemble simulations were conducted under one control atmospheric initial condition and the 26 perturbed ones with two different oceanic initial conditions. The first four EOF modes showed symmetric and asymmetric patterns such as a curved band, cloud dense overcast, and eye pattern used in the classification of the Dvorak technique. The influence of ocean coupling on the modes appeared only in the early intensification phase but was relatively small compared to the difference from the Himawari–8 observations. While ocean coupling and different oceanic initial condition quantitatively affected the IR BT, the normalized amplitude for the first EOF mode did not become close to that of the Himawari–8 observation in the late intensification phase. The intensification rate in the late intensification phase was inconsistent between the simulation results and the estimate from the Himawari–8 normalized amplitude by multiple linear regression analysis. Full article

An open levee system is a traditional flood mitigation system for reducing the expansion of inland flooding and decreasing the peak flow. However, there have been few quantitative studies on its feasibility. Furthermore, the differences in applicability depending on the topography and the construction of continuous levees have not been fully examined. We studied its feasibility based on simulations in the Kuji River area, where the vast Typhoon Hagibis occurred. Morphological models representing the past (the 1940s) and the present (2019), obtained by modifying the highly accurate digital elevation models (DEM) via the tracing of aerial photos, were applied to a 2D unsteady flow simulation model to reveal the effects of the levee system on river hydrography and overland flood behavior. The results indicated that inundation flow through an open area decreased both inundation duration and depth, while the reduction of peak discharge is relatively insignificant at approximately 10%. The sub levees are not adequate under the current conditions and floodwater volume, and their effectiveness depends on the surrounding conditions, such as the development of continuous levees. Full article

A microwave radiometer onboard a geostationary satellite can provide for the continuous atmospheric sounding of rapidly evolving convective events even in the presence of clouds, which has aroused great research interest in recent decades. To approach the problem of high-spatial resolution and large-size antennas, three promising geostationary microwave (GEO-MW) solutions—geostationary microwave radiometer (GMR) with a 5 m real aperture antenna, geostationary synthetic thinned aperture radiometer (GeoSTAR) with a Y-shaped synthetic aperture array, and geostationary interferometric microwave sounder (GIMS) with a rotating circular synthetic aperture array—have been proposed. To compare the potential impact of assimilating the three GEO-MW sounders to typhoon prediction, observing system simulation experiments (OSSEs) with the simulated 50–60 GHz observing brightness temperature data were conducted using the mesoscale numerical model Weather Research and Forecasting (WRF) and WRF Date Assimilation-Four dimensional variational (WRFDA-4Dvar) assimilation system for Typhoons Hagibis and Bualoi which occurred in 2019. The results show that the assimilation of the three GEO-MW instruments with 4 channels of data at 50–60 GHz could lead to general positive impacts in this study. Compared with the control experiment, for the two cases of Bualoi and Hagibis, GMR improves the average 72 h typhoon track forecast accuracy by 24% and 43%, GeoSTAR by 33% and 50%, and GIMS by 10% and 29%, respectively. Overall, the three GEO-MW instruments show considerable promise in atmospheric sounding and data assimilation. The difference among these positive impacts seems to depend on the observation error of the three potential instruments. GeoSTAR is slightly better than the other two GEO-MW sounders, which may be because it has the smallest observation error of the 4 assimilation channels. Generally, this study illustrates that the performance of these three GEO-MW sounders is potentially adequate to support assimilation into numerical weather prediction models for typhoon prediction. Full article

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level. Full article

This paper presents a generalization of a previously defined lexicographical dynamic flow model based on multi-objective optimization for solving the multi-commodity aid distribution problem in the aftermath of a catastrophe. The model considers distribution of the two major commodities of food and medicine, and seven different objectives, and the model can easily be changed to include more commodities in addition to other and different priorities between the objectives. The first level in the model is to maximize the amount of aid distributed under the given constraints. Keeping the optimal result from the first level, the second level can be solved considering objectives such as the cost of the operation, the time of the operation, the equity of distribution for each type of humanitarian aid, the priority of the designated nodes, the minimum arc reliability, and the global reliability of the route. The model is tested on a recent case study based on the Hagibis typhoon disaster in Japan in 2019. The paper presents a solution for the distribution problem and provides a driving schedule for vehicles for delivering the commodities from depots to the regional centers in need for humanitarian aid. Full article

Third-generation geostationary meteorological satellites (GEOs), such as Himawari-8/9 Advanced Himawari Imager (AHI), Geostationary Operational Environmental Satellites (GOES)-R Series Advanced Baseline Imager (ABI), and Meteosat Third Generation (MTG) Flexible Combined Imager (FCI), provide advanced imagery and atmospheric measurements of the Earth’s weather, oceans, and terrestrial environments at high-frequency intervals. Third-generation GEOs also significantly improve capabilities by increasing the number of observation bands suitable for environmental change detection. This review focuses on the significantly enhanced contribution of third-generation GEOs for disaster monitoring and risk mitigation, focusing on atmospheric and terrestrial environment monitoring. In addition, to demonstrate the collaboration between GEOs and Low Earth orbit satellites (LEOs) as supporting information for fine-spatial-resolution observations required in the event of a disaster, the landfall of Typhoon No. 19 Hagibis in 2019, which caused tremendous damage to Japan, is used as a case study. Full article

Typhoon Hagibis passed through Japan on October 12, 2019, bringing heavy rainfall over half of Japan. Twelve banks of seven state-managed rivers collapsed, flooding a wide area. Quick and accurate damage proximity maps are helpful for emergency responses and relief activities after such disasters. In this study, we propose a quick analysis procedure to estimate inundations due to Typhoon Hagibis using multi-temporal Sentinel-1 SAR intensity images. The study area was Ibaraki Prefecture, Japan, including two flooded state-managed rivers, Naka and Kuji. First, the completely flooded areas were detected by two traditional methods, the change detection and the thresholding methods. By comparing the results in a part of the affected area with our field survey, the change detection was adopted due to its higher recall accuracy. Then, a new index combining the average value and the standard deviation of the differences was proposed for extracting partially flooded built-up areas. Finally, inundation maps were created by merging the completely and partially flooded areas. The final inundation map was evaluated via comparison with the flooding boundary produced by the Geospatial Information Authority (GSI) and the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. As a result, 74% of the inundated areas were able to be identified successfully using the proposed quick procedure. Full article

Typhoon Hagibis, which occurred at the beginning of October 2019, was one of the largest and most powerful tropical cyclones and was considered to be the most devastating typhoon to hit Japan in recorded history. Extreme heavy rainfall caused massive impacts to Japan in general and to Marumori Town, Miyagi Prefecture in particular. In the present study, the detailed flood characteristics at Marumori Town were investigated by using field observation and numerical simulations. The obtained data immediately after the flood has clearly shown that most levee breaches were caused by the water overflow on the river embankment at the constriction areas such as the tributaries’ junction and the intersection of the river embankment. Numerical simulations were performed to investigate the mechanism of levee breaching in Marumori Town. According to the simulation results, the flooding water from the upstream levee breach locations flowed into the paddy field area and caused the levee to breach at the river embankment interaction in the downstream area. A new levee breach criterion in terms of overflow depth and its duration on the river embankment was proposed. In addition, a sensitivity analysis was also performed to understand the effect of the backwater and phase lag of water level rise between the mainstream and tributaries. Although there have been many studies on flood disasters, the typhoon’s flood-induced disasters on the river and coastal infrastructures have still remained a big challenge. The present study outcomes provide useful information not only to understand how the river embankment of tributaries is vulnerable to water level rise, but also to support the river authorities to prepare better mitigation plans for future flood disasters. Full article