Development of a possibilistic method for the evaluation of predictive uncertainty in rainfall‐runoff modeling

[1] Using a possibilistic approach, this study develops a methodology for the evaluation of predictive uncertainty in rainfall-runoff modeling. The methodology proposed herein can be regarded as a further extension of the well-known generalized likelihood uncertainty estimation (GLUE) methodology. Both methods are based on the equifinality paradigm, under which it is accepted that there may be many model structures and parameter sets (within a given model structure) that are compatible with the knowledge available about the real system. In both methodologies, uncertainty bounds of the model predictions are obtained using Monte Carlo simulations. The essential difference between them is that while in the GLUE methodology the likelihood weights of behavioral simulations are used to obtain prediction quantiles, in the possibilistic methodology the possibility distributions of the model outputs are used to derive the prediction uncertainty bounds. The methodology presented in this study is applied to a conceptual type rainfall-runoff model.

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