Assessing Flood Risk of the Chao Phraya River Basin Based on Statistical Rainfall Analysis
Shakti P. C.*1,, Mamoru Miyamoto*2, Ryohei Misumi*1, Yousuke Nakamura*3, Anurak Sriariyawat*4, Supattra Visessri*4,*5, and Daiki Kakinuma*2
*1National Research Institute for Earth Science and Disaster Resilience (NIED)
3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan
*2International Centre for Water Hazard and Risk Management under the auspices of UNESCO (ICHARM),
Public Works Research Institute (PWRI), Ibaraki, Japan
*3Mitsui Consultants Co., Ltd., Tokyo, Japan
*4Department of Water Resources Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
*5Disaster and Risk Management Information Systems Research Group, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
The Chao Phraya River Basin is one of the largest in Asia and is highly vulnerable to water-related disasters. Based on rainfall gauge data over 36 years (1981–2016), a frequency analysis was performed for this basin to understand and evaluate its overall flood risk; daily rainfall measurements of 119 rain gauge stations within the basin were considered. Four common probability distributions, i.e., Log-Normal (LOG), Gumbel type-I (GUM), Pearson type-III (PE3), and Log-Pearson type-III (LP3) distributions, were used to calculate the return period of rainfall at each station and at the basin-scale level. Results of each distribution were compared with the graphical Gringorten method to analyze their performance; GUM was found to be the best-fitted distribution among the four. Thereafter, design hyetographs were developed by integrating the return period of rainfall based on three adopted methods at basin and subbasin scales; each method had its pros and cons for hydrological applications. Finally, utilizing a Rainfall-Runoff-Inundation (RRI) model, we estimated the possible flood inundation extent and depth, which was outlined over the Chao Phraya River Basin using the design hyetographs with different return periods. This study can help enhance disaster resilience at industrial complexes in Thailand for sustainable growth.
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