Metamodeling uses computationally efficient models to emulate the outputs of complex models, trading off computational time against prediction accuracy and/or precision. Although potentially powerful, there is limited understanding of the uncertainty introduced by the metamodeling procedure. In particular, the errors associated with transformations of the predictions, such as aggregations during upscaling or differences between results used for impacts analysis, have not been explored in the metamodeling literature. We present an application of metamodeling that upscales physics-based model predictions to make catchment scale predictions of land management change impacts on peak flows. Two parallel sets of simulations are conducted, one with the original physics-based models and the other with metamodels. Despite good performance in emulating the local scale physics-based model simulations, once incorporated into a catchment scale model and especially once impacts of change are calculated, errors associated with the metamodeling procedure alone become significant, accounting for almost half of the prediction uncertainty in peak flows. The additional (metamodel-contributed) uncertainty is introduced both through biases in peak flows and through increases in peak flow variance. In the context of land management impacts, the results demonstrate the importance of tracking propagation of errors during upscaling, and of evaluating a model's ability to predict change, as well as independent observations. Despite these errors, the predictions of land management impacts from both physics-based models and metamodels are broadly consistent between each other, and in accordance with expectations from the literature.
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