scholarly journals Inundation Analysis of the Oda River Basin in Japan during the Flood Event of 6–7 July 2018 Utilizing Local and Global Hydrographic Data

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1005 ◽  
Author(s):  
Shakti P. C. ◽  
Hideyuki Kamimera ◽  
Ryohei Misumi

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.

2021 ◽  
pp. 1-26
Author(s):  
Bikash Ranjan Parida ◽  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Amit Kumar

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 896
Author(s):  
Thanh Thu Nguyen ◽  
Makoto Nakatsugawa ◽  
Tomohito J. Yamada ◽  
Tsuyoshi Hoshino

This study aims to evaluate the change in flood inundation in the Chitose River basin (CRB), a tributary of the Ishikari River, considering the extreme rainfall impacts and topographic vulnerability. The changing impacts were assessed using a large-ensemble rainfall dataset with a high resolution of 5 km (d4PDF) as input data for the rainfall–runoff–inundation (RRI) model. Additionally, the prediction of time differences between the peak discharge in the Chitose River and peak water levels at the confluence point intersecting the Ishikari River were improved compared to the previous study. Results indicate that due to climatic changes, extreme river floods are expected to increase by 21–24% in the Ishikari River basin (IRB), while flood inundation is expected to be severe and higher in the CRB, with increases of 24.5, 46.5, and 13.8% for the inundation area, inundation volume, and peak inundation depth, respectively. Flood inundation is likely to occur in the CRB downstream area with a frequency of 90–100%. Additionally, the inundation duration is expected to increase by 5–10 h here. Moreover, the short time difference (0–10 h) is predicted to increase significantly in the CRB. This study provides useful information for policymakers to mitigate flood damage in vulnerable areas.


2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Gebdang Biangbalbe Ruben ◽  
Zhenxin Bao ◽  
...  

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.


2021 ◽  
Vol 14 (18) ◽  
Author(s):  
Mohammad Ilyas Abro ◽  
Dehua Zhu ◽  
Ehsan Elahi ◽  
Asghar Ali Majidano ◽  
Bhai Khan Solangi

2016 ◽  
Vol 59 ◽  
pp. 1-9 ◽  
Author(s):  
Thomas Prime ◽  
Jennifer M. Brown ◽  
Andrew J. Plater

2013 ◽  
Vol 52 (4) ◽  
pp. 802-818 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Deg-Hyo Bae

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.


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