Open AccessAssessing Flood Risk of the Chao Phraya River Basin Based on Statistical Rainfall Analysis
Author(s) -
P C Shakti,
Mamoru Miyamoto,
Ryohei Misumi,
Yousuke Nakamura,
Anurak Sriariyawat,
Supattra Visessri,
Daiki Kakinuma
Publication year - 2020
Publication title -
journal of disaster research
Language(s) - English
Resource type - Journals
eISSN - 1883-8030
pISSN - 1881-2473
DOI - 10.20965/jdr.2020.p1025
Subject(s) - gumbel distribution , return period , hydrology (agriculture) , flood myth , environmental science , drainage basin , 100 year flood , structural basin , surface runoff , rain gauge , geography , extreme value theory , meteorology , geology , statistics , mathematics , cartography , ecology , precipitation , paleontology , archaeology , geotechnical engineering , biology
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.