Water resource vulnerability due to uneven precipitation and water allocations is a significant issue in many regions of the world, including the Mekong Delta. Although numerous studies have already evaluated drought tendencies in many parts of the Mekong Delta, some areas have been excluded. This study targeted these excluded areas, including one inland, one coastal area, and two islands, which are defined as “understudied regions.” The meteorological drought intensity, frequency, and duration in the study areas in the Mekong Delta were evaluated using different time scales of the Standardized Precipitation Index (SPI) between 1994–2020. Unique contrasts in drought features were found between the study areas, indicating that severe drought events occupied the highest percentages inland from 2011 onward, while the most extreme drought events occurred in the coastal areas. Furthermore, trends in drought intensity, tendency, frequency, and duration were identified within the same delta region, demonstrating that combining SPI with other indicators can detect drought patterns in the Mekong Delta. These findings emphasize the importance of comprehensive evaluation of drought tendencies, including in understudied regions, for a better understanding of the features and future of water resources management.

In the renowned Kushiro wetland, Japan, the government has been undertaking a nature restoration project to counter the impact of post-WW2 restoration land development. The recent typhoon landfalls in Hokkaido has triggered concerns about the potential impact of climate change. In response, the government plans to incorporate nature restoration as an adaptation strategy to mitigate climate risks. To support this initiative, which involves an assessment of climate change impacts, we improved the high-resolution climate change projections and hydrological model to predict future impacts on wetlands in terms of river discharge and sediment runoff. The results show that although there is no significant increase in the risk of drought, intense rainfall and flooding are likely. In addition, simulated sediment runoff increased by mean of 2–3 folds due to increased summer flooding, and the frequency of intense sediment runoff events increased by 10 fold. These results show that countermeasures against sediments are important for protecting wetland ecosystems from climate change.

Historical climatological upper percentile rainfall over Japan from the Database for Policy Decision-making for Future climate changes (d4PDF) was compared with that from 121 rain-gauges for the 1952–2010 period. Percentiles calculated excluding or including values below 1 mm, “wet-extremes” and “all-extremes” were compared. The 5 km d4PDF hourly wet (all) extremes matched rain-gauges at 72% (45%) of the locations, but for daily extremes this was 39% (33%). Further comparison with radar-AMeDAS (Automated Meteorological Data Acquisition System) was made for hourly extremes in the 2006–2021 period. Wet (all) extremes matched for 84% (79%) of rain-gauges, and 80% (51%) also matched radar-AMeDAS at the same locations. Exceedance probabilities were calculated for historical and 4 K warmer climates using thresholds based on a non-warming climate. Historically, hourly (daily) extremes were more frequent over most of (eastern) Japan. In a 4 K warmer climate, daily extremes would become more frequent over most of Japan. Rainfall from 5 km d4PDF at hourly and daily resolutions are provided in prefecture and river basin subsets, with compressed sizes of each below 1 Gigabyte.

Localized heavy rainfall events have caused an increasing number of water-related disasters in recent years, particularly in urban areas where drainage capacity is limited. Accurate prediction of such events requires high-resolution, high-frequency three-dimensional (3D) observation data to capture rapidly evolving atmospheric structures. The Multi-Parameter Phased Array Weather Radar (MP-PAWR) provides rapid, high-density observations, offering significant advantages over conventional parabolic radars. However, rainfall prediction methods such as Vertically Integrated Liquid water content (VIL) Nowcast (VIL Nowcast; VILNC), which traditionally rely on limited-resolution radar data, often overestimate rainfall intensities, especially during the dissipating phase of cumulonimbus clouds. In this study, we propose an enhanced VILNC model that incorporates 3D wind field estimations derived from MP-PAWR to better represent the development and decay of rainfall areas. By integrating vertical wind information, the modified VILNC aims to mitigate overestimations associated with downdrafts. Comparative analysis of several localized heavy rainfall events demonstrates that the proposed method improves prediction accuracy, particularly at shorter lead times. These results suggest that the utilization of high-resolution 3D wind fields from MP-PAWR contributes to more reliable short-term rainfall predictions.