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

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.