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

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.

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

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-2-7027-2014 ◽

2014 ◽

Vol 2 (11) ◽

pp. 7027-7059 ◽

Cited By ~ 1

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.

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

Natural Hazards and Earth System Science ◽

10.5194/nhess-15-1617-2015 ◽

2015 ◽

Vol 15 (7) ◽

pp. 1617-1630 ◽

Cited By ~ 24

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.

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