Author(s): Yuta Kurihara; Mamoru Miyamoto

Linked Author(s):

Keywords: Bayesian estimation; Climate change; CMIP6; Flood risk; Philippines

Abstract: This study applies a Bayesian estimation to quantify the future probabilistic rainfall and propagate uncertainty to future flood risk in the Cagayan Valley Region, Philippines, under climate change. Downscaled and bias-corrected CMIP6 precipitation data were analyzed using Bayesian inference. Future probable rainfall is predicted by the MCMC method using the parameters of the generalized extreme value (GEV) distribution to estimate the probable extreme rainfall. The resulting probability density functions and 95% confidence intervals clearly depict the uncertainty in the return level, showing that the confidence intervals widen as the return period increases, especially for multi-day rainfall events. These probabilistic rainfall estimates were propagated through the Rainfall–Runoff–Inundation (RRI) model to generate stochastic flood simulations, representing flood hazards as probability distributions rather than deterministic outcomes. Higher-emission scenarios (SSP 2-4.5, SSP3-7.0 and SSP5-8.5) exhibited significant increases in extreme rainfall intensity. The simulated river water levels and flood risk in urban and agricultural areas are quantitatively assessed in the form of confidence intervals and probability density functions. By visualizing and propagating uncertainty from rainfall estimation to flood modeling, the framework provides a transparent and policy-relevant basis for evidence-based climate adaptation and risk-informed flood management.

DOI:

Year: 2026