Identification of dominant mechanisms for representing hydrological processes in catchment scale models

The identification of model mechanisms for representing hydrological (physical) process is a major scientific and practical problem in catchment scale hydrological modelling. We present a multiple hypothesis-testing approach to identify dominant hydrological mechanisms. The method combines: (i) Bayesian estimation of posterior probabilities of individual hydrological mechanisms given an ensemble of hydrological model structures; (ii) a test statistic that defines a “dominant” mechanism as the mechanism with (substantially) higher posterior probability than the sum of the alternative ones given observed data; (iii) a flexible modelling framework to generate hydrological models from combinations of available mechanisms. The uncertainty in the test statistic is approximated using a model bootstrap approach. The performance of the proposed framework is evaluated using synthetic and real data. We use 624 model structures from the Framework for Understanding Structural Errors (FUSE) and data from the Leizarán catchment in Basque Country (northern Spain). The synthetic experiments indicate that the mechanism identification method is reliable; as expected its statistical power (identifiability) declines as data/model errors increase. The "most identifiable" processes are those in the saturated zone and routing, and the "least identifiable" processes are interflow and percolation. The real data experiment yields results that are broadly consistent with the synthetic experiments, with dominant mechanisms identified for 4 of 7 processes. We expect that the proposed mechanism identification method will contribute to hydrological community efforts on improving process representation and model development.