Search Results (6)

This study marks a significant step toward the future development of river discharges forecasted in real time for flash flood early warning system (EWS) disaster prevention frameworks in the Chugoku region of Japan, and presumably worldwide. To reduce the disaster impacts with EWSs, accurate integrated hydrometeorological real-time models for predicting extreme river water levels and discharges are needed, but they are not satisfactorily accurate due to large uncertainties. This study evaluates two calibration methods with 7 and 5 parameters using the hydrological Cell Distributed Runoff Model version 3.1.1 (CDRM), calibrated by the University of Arizona’s Shuffled Complex Evolution optimization method (SCE-UA). We hypothesize that the proposed ensemble hydrological parameter calibration approach can forecast similar future events in real time. This approach was applied to seven major rivers in the region to obtain hindcasts of the river discharges during the Heavy Rainfall Event of July 2018 (HRE18). This study introduces a new historical extreme rainfall event classification selection methodology that enables ensemble-averaged validation results of all river discharges. The reproducibility metrics obtained for all rivers cumulatively are extremely high, with Nash–Sutcliffe efficiency values of 0.98. This shows that the proposed approach enables accurate predictions of the river discharges for the HRE18 and, similarly, real-time forecasts for future extreme rainfall-induced events in the Japanese region. Although our methodology can be directly reapplied only in regions where observed rainfall data are readily available, we suggest that our approach can analogously be applied worldwide, which indicates a broad scientific contribution and multidisciplinary applications. Full article

Natural hazards have a significant impact on the sustainable development of human society. This paper reports on the catastrophic floods in western Japan in 2018. Continuous rainfall resulted in catastrophic floods, leading to 212 deaths, damage to more than 2000 houses and 619 geological disasters in 31 prefectures. The causes and contributing factors of these catastrophic floods are analyzed. The analysis of the causes of typical natural hazards provides an important lesson for hazard prevention and management. To adapt to climate change and prevent natural hazards in the future, the preliminary investigation and sustainable perspective analysis in this paper suggest the importance of the construction of a spongy city and the establishment of an early warning system with the help of information science and artificial intelligence technologies (ISAIT); we also highlight the urgent need to improve and strengthen the management of infrastructure. Full article

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time. Full article

During the first week of July 2018, widespread flooding caused extensive damage across several river basins in western Japan. Among the affected basins were the Mabicho district of Kurashiki city in the lower part of the Oda river basin of the Okayama prefecture. An analysis of such a historical flood event can provide useful input for proper water resources management. Therefore, to improve our understanding of the flood inundation profile over the Oda river basin during the period of intense rainfall from 5–8 July 2018, the Rainfall-Runoff-Inundation (RRI) model was used, with radar rainfall data from the Japan Meteorological Agency (JMA) as the input. River geometries—width, depth, and embankments—of the Oda river were generated and applied in the simulation. Our results show that the Mabicho district flooding was due to a backwater effect and bursting embankments along the Oda River. The model setup was then redesigned, taking into account these factors. The simulated maximum flood-affected areas were then compared with data from the Japanese Geospatial Information Authority (GSI), which showed that the maximum flood inundation areas estimated by the RRI model and the GSI flood-affected area matched closely. River geometries were extracted from a high-resolution digital elevation model (DEM), combined with coarser resolution DEM data (global data), and then utilized to perform a hydrological simulation of the Oda river basin under the scenarios of backwater effect and embankment failure. While this approach produced a successful outcome in this study, this is a case study for a single river basin in Japan. However, the fact that these results yielded valid information on the extent of flood inundation over the flood-affected area suggests that such an approach could be applicable to any river basin. Full article

Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named “conditional coherence”, that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well. Full article

Excessive rainfall was observed over Southwest China in July 2018, leading to floods in several major tributaries of the Yangtze River and landslide and debris flow in the neighboring provinces. The rainfall during 7–11 July was unusually heavy and broke the record that can be traced back to 1961. The occurrence of the excessive rain can be attributed to the anomalous convection over the western North Pacific and the presence of a mid-latitude Rossby wave train. On one hand, the convection over the western North Pacific was anomalously strong in July 2018, and it could have excited the negative phase of the Pacific–Japan pattern and led to a northwestward shift of the western Pacific subtropical high. Hence, the water vapor transport toward inland China including Southwest China was enhanced, providing a favorable moisture environment for precipitation. On the other hand, a mid-latitude Rossby wave train was observed to propagate from Northern Europe towards East Asia, which was conducive to anomalous ascending motion over Southwest China via warm advection and differential vorticity advection, creating a favorable dynamical condition for precipitation. As a result, the combination of the two effects mentioned above led to the occurrence of the flood over Southwest China in July 2018. Full article