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Recordable heavy rainfall hit and caused severe floods from the Kuma River in Hitoyoshi and Kuma regions in July, 2020 due to an active frontal rain system. This paper aims to show the flood impacts and mitigation effects of dams including proposing Kawabegawa dam. Additionally, we clarify challenges and issues in measures taken by core medical institutions against large-scale floods based on the results of interview with the core medical institution in Hitoyoshi and Kuma regions. The paper then discusses directions for effective flood countermeasures of core medical institutions in order to have more robust Business Continuity Plan (BCP) for large-scale flood disasters.

The objective of this study is to evaluate the risk of floods by modeling rainfall patterns during recent disaster events and applying them to various river basins with different basin geography using a distributed hydrological model. As the first attempt, we tried to change the incoming rainfall patterns by rotate its directions from 90 degrees to 270 degrees from the original rainfall direction. Then, we detected the worst cases of flood hazards considering rainfall patterns and basin geography in several major river basins in Japan. As the second attempt, the relationship between specific river flow and basin shape, gradient, and channel density of the 109 class-A river systems in Japan was investigated. As a result, a weak correlation was found between the specific peak flow and the designed high-water flow with the basin shape, but no clear relationship was found with the annual average flow. Furthermore, it was found that the larger the slope of the basin, the larger the flow rate. However, no clear relationship was found between the water density and the specific flow rate.

This paper reports the results of a broad survey on expected depth of inundation by large-scale floods at designated hospitals for infectious diseases in Japan based on open flood hazard maps. The results showed that inundation was expected at approximately a quarter of all target hospitals by floods of the designed level for river planning, while it was expected at about one-third of them by floods of the probable maximum level. Flood inundation deeper than 10 meters was expected at some hospitals, where self-protection measures such as evacuation to the upper stories or installation of the emergency power system to the upper level may no longer be effective. Close coordination with river management, crisis management and public health authorities is therefore considered to be important for those hospitals to enlarge their capability of crisis management under a complex disaster that consists of the epidemic of severe infectious diseases and large-scale floods.

In July in 2018, a strong frontal rain system caused prolonged and torrential rainfall in the broad areas in the western Japan. While many reservoirs contributed to mitigate flood impacts by controlling flood water in the rivers, some reservoirs lost their flood control volumes to regulate river flow in the downstream by the overwhelming amount of inflow in the middle of the flood event. Those reservoirs therefore had to release the same amount of water as inflow after that, and resulted in severe inundation in the downstream in some river basins. Considering lessons learnt from this flood event, a robust reservoir operation method is proposed for flood control under extreme flood events in this paper.

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.

In the torrential rain disaster in July 2018, heavy rain over a long period occurred in a wide area mainly in western Japan which caused severe flooding in various places. There were many catchment basins greatly contributing to reduce the flood water level in the downstream river by the flood control operation of the dam reservoir. Meanwhile, due to prolonged rainfall, the flood control capacity of the dam is expected to be full, and some dams carried out the emergency spillway gate operation at the time of extreme flooding. In this paper, we outline the implementation status of the dam flood control operation in the heavy rain disaster, analyze the effect and problem, and describe the direction of countermeasures for these problems for large-scale flooding in the future.

Heavy rainfall caused severe flood disasters across the western Japan in July 2018. In order to decrease frequent inundation in the downstream reaches where river improvement works have never been completed, some reservoir dams are often operated to control small and/or medium floods by releasing a less discharge than originally designed. However, this operation can increase a risk of severe flood inundation in case of large floods, because the water level of the reservoir increases faster to the full storage level and eliminates the flood control function of the reservoir. This study aims at analyzing impacts of different flood control operations for floods of various scales including large flood events using rainfall runoff inundation analysis in order to identi fy an effective operation rule for flood risk reduction.Heavy rainfall caused severe flood disasters across the western Japan in July 2018. In order to decrease frequent inundation in the downstream reaches where river improvement works have never been completed, some reservoir dams are often operated to control small and/or medium floods by releasing a less discharge than originally designed. However, this operation can increase a risk of severe flood inundation in case of large floods, because the water level of the reservoir increases faster to the full storage level and eliminates the flood control function of the reservoir. This study aims at analyzing impacts of different flood control operations for floods of various scales including large flood events using rainfall runoff inundation analysis in order to identi fy an effective operation rule for flood risk reduction.

In order to clarify the requirements for DSSs for IWRM, key functions of DSSs for water resources planning and management are discussed in this paper. Changes in roles and functions of DSSs are reviewed by tracing the past studies on DSS for water resources planning and management. The paper then discusses important points to be considered in the development of effective DSSs for IWRM in the future.

Characteristics of ensemble inflow predictions synthetically generated using error characteristics of the operational hydrological prediction is analyzed to investigate the way to effectively use the simulated generation method of hydrological predictions. As a result of the analysis, it was shown that the conventional generation method assuming stationary error parameters did not well simulate operational ensemble inflow prediction. It was also shown that the modified generation method assuming non-stationary error parameters could reproduce operational ensemble prediction. It is therefore considered to be important to include a model of non-stationary error parameter in the generation model for more realistic generation of ensemble inflow predictions.