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Uncertainty quantification in modeling earth surface processes: more applicable for some types of models than for others

Murray, A. Brad, Gasparini, Nicole M., Goldstein, Evan B., van der Wegen, Mick
Computers & geosciences 2016 v.90 pp. 6-16
computers, data collection, prediction, simulation models, uncertainty
In Earth-surface science, numerical models are used for a range of purposes, from making quantitatively accurate predictions for practical or scientific purposes (‘simulation’ models) to testing hypotheses about the essential causes of poorly understood phenomena (‘exploratory’ models). We argue in this contribution that whereas established methods for uncertainty quantification (UQ) are appropriate (and crucial) for simulation models, their application to exploratory models are less straightforward, and in some contexts not relevant. Because most models fall between the end members of simulation and exploratory models, examining the model contexts under which UQ is most and least appropriate is needed. Challenges to applying state-of-the-art UQ to Earth-surface science models center on quantifying ‘model-form’ uncertainty—the uncertainty in model predictions related to model imperfections. These challenges include: 1) the difficulty in deterministically comparing model predictions to observations when positive feedbacks and associated autogenic dynamics (a.k.a. ‘free’ morphodynamics) determine system behavior over the timescales of interest (a difficulty which could be mitigated in a UQ approach involving statistical comparisons); 2) the lack of available data sets at sufficiently large space and/or time scales; 3) the inability to disentangle uncertainties arising from model parameter values and model form in some cases; and 4) the inappropriateness of model ‘validation’ in the UQ sense for models toward the exploratory end member of the modeling spectrum.