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.

This study proposes a method to quantitatively assess evacuation demographics during regional floods using Mobile Spatial Statistics (MSSs). It focuses on Koriyama City, affected by Typhoon Hagibis in 2019, as well as Yamagata City, which experienced torrential rains in July 2020, when COVID-19 infection risks in evacuation shelters could have affected evacuation behavior. Both events led to flooding. MSS provided by NTT Docomo were used to explore the dynamics of the population. Evacuees’ demographics, according to the changes in river levels, were presented by gender and age group. Our results show differences in evacuation dynamics between the two regions: In Koriyama, younger people were more likely to evacuate faster; in Yamagata, older people moved faster than other age groups. At the evacuation peak, the average relative evacuation ratios were 2.2 times higher for women in their 20s in Koriyama, and 2.5 times higher for women in their 30s in Yamagata. Gender differences indicate that women were more likely to evacuate than men. The effect of the COVID-19 pandemic on evacuation dynamics remains unclear; however, concerns about infection risk in shelters may have influenced people’s willingness to evacuate. MSSs provide a robust representation of evacuation dynamics in the local context and can help municipal governments develop more targeted evacuation policies, such as tailored warnings for specific age groups and enhanced support for older adults.

We investigated the merits of performing multi-scale flood simulations with nested two-dimensional flood simulation models for flood risk management applications over an extended period with infiltration and evapotranspiration. For nested calculations, (1) inter-district, (2) district, and (3) regional scales were arranged as three levels in the nested model. Because the nested method can be useful for flat land, the Gaibandha District in Bangladesh was chosen for the study area. This area is a typical flood-prone area that is inundated frequently by flood flow from the Jamuna River. First, we performed a year-round flooding simulation for the year 2007 by focusing on a monsoon flood, and validated the results via satellite images. Second, we evaluated the differences of floods in the case of 2.33- and 100-year return periods by using discharge data produced by a flood frequency analysis. We successfully simulated the flooding in each nested level for 2007, and this enabled us to evaluate the flood risks at different scales. In addition, we delineated the flood extent for different return periods and clarified the relationship between the peak water level of the river and the flood extent. The nested method proved to be advantageous because its implementation is not computationally demanding.

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.