TY - JOUR
T1 - Bayesian characterization of uncertainties surrounding fluvial flood hazard estimates
AU - Sharma, Sanjib
AU - Ghimire, Ganesh Raj
AU - Talchabhadel, Rocky
AU - Panthi, Jeeban
AU - Lee, Benjamin Seiyon
AU - Sun, Fengyun
AU - Baniya, Rupesh
AU - Adhikari, Tirtha Raj
N1 - Publisher Copyright:
© 2022 IAHS.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1080/02626667.2021.1999959
DO - 10.1080/02626667.2021.1999959
M3 - Article
AN - SCOPUS:85123401298
SN - 0262-6667
VL - 67
SP - 277
EP - 286
JO - Hydrological Sciences Journal
JF - Hydrological Sciences Journal
IS - 2
ER -