A case study examining Bayesian techniques for assessing parameter and predictive uncertainty of semiarid flash flood events is presented here. The focus is on testing a fully distributed rainfall-runoff model (i.e., AFFDEF) linked with Markov chain Monte Carlo (MCMC) samplers to simulate four semiarid flash flood events with varying rainfall durations (<24 h) and amounts (>20 mm). MCMC samplers showed consistent behaviors with the a priori assumption and successfully improved performances on complex and multivariate search problems of semiarid flood simulation over the Abol-Abbas watershed, Iran. Analysis suggests that parameters associated with infiltration and interception capacity along with the contributing area threshold for the digital river network were the key model parameters and were more influential on the shape and volume of the flood hydrograph. Model predictive uncertainty was heavily dominated by error and bias in the soil water storage capacity, which reflects inadequate representation of the upper soil zone processes in the AFFDEF distributed model. Overall, the modeling results revealed that a fat-tailed Gaussian distribution using the standard least-squares (SLS) error assumption yielded improved estimates of parameter and predictive uncertainty for the semiarid flood events. This case study emphasizes the importance of proper statistical representation of the residual error distribution as a basis to improve parameter and predictive uncertainty. DOI: 10.1061/(ASCE)HE.1943-5584.0001482.

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