The estimation of extreme wind speeds, their directional variation, and potential future changes is essential for wind-resistant design and is possible using climate models. Accurate evaluations of local topographic winds such as downslope windstorms and gap winds require high-resolution calculation and many ensemble years. However, few climate databases satisfy both requirements and none have been validated for extreme wind speeds.

We assessed directional extreme wind speeds using a massive high-resolution ensemble climate dataset (d4PDF-5km-DS) for Hokkaido Island, Japan. Despite some issues due to limited reproducibility of spring extratropical cyclones, downslope windstorms caused by large mountains are reproduced well, indicating decreasing wind speed under predicted future climate. The findings suggest future climatic conditions may influence extreme topographic wind speeds.

Satellite products are expected to play important roles in water-related management and public welfare, particularly in developing countries. Higher-resolution precipitation products are required to cope with increasingly severe water-related disasters. In this study, we propose a new satellite precipitation estimation algorithm based on deep learning that uses data from multiple satellite infrared (IR) bands and geographic information (e.g. elevation, latitude, and longitude) as input. For the deep learning model component, we use various image segmentation models, including U-Net, PSPNet, and DeepLabv3+. Cosine similarity and correlation coefficients for precipitation rate were used to determine the IR bands of the input data; five bands were used as IR. Four input datasets were constructed: IR alone; IR and elevation data; IR and latitude/longitude; and IR, elevation data, and latitude/longitude. When PSPNet or DeepLabv3+ was used as the deep learning model, and elevation and latitude/longitude were added to IR as input data, the mean square error and fraction skill score showed improved accuracy over GSMaP_MVKv7 and PERSIANN-CCS; precipitation overestimation was eliminated. These results indicate that deep learning models can be used to estimate precipitation from satellite IR observations with high resolution and accuracy.

In Asian megacities undergoing rapid urbanization such as Bangkok, heavy rainfall exacerbates traffic congestion owing to inadequate drainage systems. This study statistically analyzed the extent to which rainfall affects urban traffic speed and how this impact varies depending on regional environmental factors and traffic demand trends, utilizing probe vehicles and rainfall data from 2018 to 2020 in Bangkok. The results clearly indicate that both the intensity of rainfall during driving and previous cumulative rainfall significantly reduce traffic speed. This impact is particularly pronounced during morning and evening rush hours, and in areas with a high proportion of narrow roads or in low-lying areas. On the other hand, areas with rich urban green space, which naturally absorb and retain water, tend to mitigate the speed reduction due to rainfall. This study highlights the fact that the impact of rainfall on traffic varies with time and location, suggesting that the exacerbation of rain-induced congestion can be more effectively mitigated by coordinated improvements in drainage facilities, traffic management and land use.

Toward comprehensive sediment management in Japan, the present study estimated suspended sediment discharge in the downstream areas of 109 first-class river watersheds based on the relationship between suspended sediment (SS) discharge L and water discharge Q as well as the volumetric changes of the watersheds. Firstly, we collected literature that reported L – Q relationships and we identified L – Q equations for the monitoring stations of all 109 first-class rivers. Secondly, we calculated annual average SS discharge rate L for all stations using the L – Q equations. Finally, we calculated the annual average vertical movement rate and volume change. As a result, we found that: (1) SS discharge was high in eastern Hokkaido, northern Kanto, Chubu, Shikoku regions and parts of Chugoku region, (2) average vertical movement rates were high in areas around 40° north and in the central parts of Japan, (3) the volume increase was greater in Hokkaido, areas around 40° north and central parts of Japan, (4) net increases were greater in western Hokkaido, areas around 40° north, and in Kinki region, and (5) volume increase rates were high in areas around 40° north. There were also large volume decreases in the Shinano River watershed.