Assessment of uncertainty in estimating future flood return levels under climate change

In the context of climate change, it is essential to quantify the uncertainty for effective design and risk management practices. In the present study, we have accessed the climate model and flood return level uncertainties over a river basin. Six high-resolution global climate models (GCMs) with two Representative Concentration Pathways (RCPs) are used to project the future climate change impact on streamflow of Wainganga River basin. Uncertainty associated with the use of high-resolution multiple GCM is treated with reliability ensemble average (REA) followed by bias correction. The bias-corrected weighted outputs are used as input to variable infiltration capacity (VIC) model, a physically based hydrological model. Calibration and validation are carried out for the hydrological model, and the parameters of VIC are fixed through trial-and-error method. The uncertainty in flood return level associated with the future projected flows is dealt with the Bayesian analysis and modelled through Markov Chain Monte Carlo (MCMC) simulation technique using Metropolis-Hastings algorithm with the non-informative prior distribution. The study provides a robust framework, which will help in effective decision-making and adaptation strategies over the river basin.