Bayesian characterization of uncertainties surrounding fluvial flood hazard estimates

Sanjib Sharma, Ganesh Raj Ghimire, Rocky Talchabhadel, Jeeban Panthi, Benjamin Seiyon Lee, Fengyun Sun, Rupesh Baniya, Tirtha Raj Adhikari

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Fluvial floods drive severe risk to riverine communities. There is strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters, and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on the Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in extreme flood stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.

Original languageEnglish (US)
Pages (from-to)277-286
Number of pages10
JournalHydrological Sciences Journal
Volume67
Issue number2
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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