Regionalization of rainfall-runoff model parameters using Markov Chain Monte Carlo samples

Abstract

A general approach to the regionalization of rainfall-runoff model parameters is developed that uses posterior calibration samples derived by Markov Chain Monte Carlo methods. For each watershed the posterior calibration samples are used to define the second-order properties of the posterior distribution of the model parameters. Regionalization of the model parameters is accomplished for all parameters simultaneously via a regional link function that links the posterior means to watershed characteristics. A linear model is a particular case of our general approach, and we examine its performance in some detail. We indicate nonlinear and nonparametric extensions that may also be accommodated. A case study involving a quasi-distributed, nonlinear flood event model and 39 watersheds in southwestern Australia is presented. We find that the regional model has substantial predictive ability.