scholarly journals NUMERICAL SIMULATION OF RAINFALL-RUNOFF AND FLOOD INUNDATION FLOWS IN THE YAMAKUNI RIVER BASIN AT KYUSHU-HOKUBU HEAVY RAIN IN 2014

2015 ◽  
Vol 71 (4) ◽  
pp. I_1405-I_1410
Author(s):  
Mirei SHIGE-EDA ◽  
Juichiro AKIYAMA ◽  
Takuma MATSUMOTO ◽  
Shunpei YAMAMOTO ◽  
Yu KAWAKAMI
Keyword(s):  
Numerical Simulation ◽  
River Basin ◽  
Heavy Rain ◽  
Flood Inundation ◽  
Rainfall Runoff

scholarly journals Rainfall–runoff, flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin

2018 ◽  
Vol 63 (13-14) ◽  
pp. 1976-1997 ◽  
Author(s):  
Muhammad Junaid Siddiqui ◽  
Sajjad Haider ◽  
H.F. Gabriel ◽  
Aamir Shahzad
Keyword(s):  
Sensitivity Analysis ◽  
River Basin ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Chenab River

scholarly journals Machine learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

2021 ◽  
Author(s):  
Habtamu Tamiru
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Daily Rainfall ◽  
Simulation Models ◽  
Spatial And Temporal Variability ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

This paper presents the integrated machine learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. A predictive rainfall-runoff and spatially distributed river simulation models were developed using Artificial Neural Networks (ANNs) and HEC-RAS respectively. Daily rainfall and temperature data of 7-yrs and Topographical Wetness Index (TWI) with a spatial resolution of 50 x 50m were used to train the ANN in R studio. The integration of the spatial and temporal variability in this paper improved the accuracy of the predictive models integrated with ANN and HEC-RAS. The predictive ANN model was tested with the observed daily discharge of the same temporal resolution and the rainfall-runoff result obtained from the tested ANN model was used as input for the HEC-RAS. The flood event of 2005 was used to verify the accuracy of flood generated in the HEC-RAS model by implementing the Normal Difference Water Index (NDWI). The comparison was made between the flood inundation map generated by HEC-RAS and flood events of different periods based on coverage percentage areas and a good agreement was reached with 96 % overlapped areas. The performance of ANN and HEC-RAS models were evaluated with 0.86 and 0.88 values at the training and testing period respectively. Finally, it was concluded that the integration of a machine learning approach with the HEC-RAS model in developing a flood inundation mapping is an appropriate tool to warn residents in this river basin.

scholarly journals Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

2014 ◽  
Vol 2 (11) ◽  
pp. 7027-7059 ◽  
Author(s):  
T. Sayama ◽  
Y. Tatebe ◽  
Y. Iwami ◽  
S. Tanaka
Keyword(s):  
River Basin ◽  
Monsoon Rainfall ◽  
Flood Hazard ◽  
Large River ◽  
Economic Damage ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Chao Phraya River ◽  
Large Floods

Abstract. Thailand floods in 2011 caused an unprecedented economic damage in the Chao Phraya River basin. To diagnose the flood hazard characteristics, this study analyzes the hydrologic sensitivity of flood runoff and inundation to rainfall. The motivation is to address why the seemingly insignificant monsoon rainfall, or 1.2 times more rainfall than past large floods including the ones in 1995 and 2006, resulted in such a devastating flooding. To quantify the hydrologic sensitivity, this study simulated a long-term rainfall-runoff and inundation for the entire river basin (160 000 km2). The simulation suggested that the flood inundation volume in 2011 was 1.6 times more than past flood events. Furthermore the elasticity index suggested that 1% increase in rainfall causes 2.3% increase in runoff and 4.2% increase in flood inundation. This study highlights the importance of sensitivity quantification for better understanding of flood hazard characteristics; and the presented approach is effective for the analysis at large river basins.

scholarly journals Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

2015 ◽  
Vol 15 (7) ◽  
pp. 1617-1630 ◽  
Author(s):  
T. Sayama ◽  
Y. Tatebe ◽  
Y. Iwami ◽  
S. Tanaka
Keyword(s):  
River Basin ◽  
Flood Hazard ◽  
Economic Damage ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
The Past ◽  
Basin Scale ◽  
Chao Phraya River ◽  
Large Floods

Abstract. The Thailand floods in 2011 caused unprecedented economic damage in the Chao Phraya River basin. To diagnose the flood hazard characteristics, this study analyses the hydrologic sensitivity of flood runoff and inundation to rainfall. The motivation is to address why the seemingly insignificant monsoon rainfall, or 1.2 times more rainfall than for past large floods, including the ones in 1995 and 2006, resulted in such devastating flooding. To quantify the hydrologic sensitivity, this study simulated long-term rainfall–runoff and inundation for the entire river basin (160 000 km2). The simulation suggested that the flood inundation volume was 1.6 times more in 2011 than for the past flood events. Furthermore, the elasticity index suggested that a 1 % increase in rainfall causes a 2.3 % increase in runoff and a 4.2 % increase in flood inundation. This study highlights the importance of sensitivity quantification for a better understanding of flood hazard characteristics; the presented basin-wide rainfall–runoff–inundation simulation was an effective approach to analyse the sensitivity of flood runoff and inundation at the river basin scale.

Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India)

2021 ◽  
pp. 1-26
Author(s):  
Bikash Ranjan Parida ◽  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Amit Kumar
Keyword(s):  
River Basin ◽  
Flood Inundation ◽  
Inundation Maps

scholarly journals Flood Inundation Assessment in the Low-Lying River Basin Considering Extreme Rainfall Impacts and Topographic Vulnerability

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 896
Author(s):  
Thanh Thu Nguyen ◽  
Makoto Nakatsugawa ◽  
Tomohito J. Yamada ◽  
Tsuyoshi Hoshino
Keyword(s):  
River Basin ◽  
Extreme Rainfall ◽  
Flood Damage ◽  
Water Levels ◽  
Flood Inundation ◽  
Inundation Area ◽  
Inundation Depth ◽  
Study Results ◽  
Inundation Duration ◽  
Short Time

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.

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