Journal of Disaster Research · 2020 · 15 citations · 24 references
Natural ResourcesEarth SciencesHydrologyFlood Risk ManagementHydrologic ModelingWater Resources EngineeringFlood Frequency AnalysisFlood Risk AssessmentHydrologic AnalysisRainfall MeasurementRainfall-runoff AnalysisRainfall-runoff ModelingContinuous Probability DistributionsFlood RiskFlood Risk EstimationHydrological Risk AnalysisLog-pearson Type Iii DistributionRain Gauge Data AnalysisRainfall Frequency AnalysisReturn Period Analysis
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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Best-Fit Probability Distributions and Return Periods for Maximum Monthly Rainfall in Bangladesh
Md Ashraful Alam, Kazuo Emura, Craig Farnham et al. · Climate · 2018 · 177 citations · Full text