Uncertainty and its propagation in computer models has relevance in many disciplines, including hydrology, environmental engineering, ecology and climate change. Error propagation in a model results in uncertainty in prediction due to uncertainties in model inputs and parameters. Common methods for quantifying error propagation are reviewed, namely Differential Error Analysis and Monte Carlo Simulation, including underlying principles, together with a discussion on their differences, advantages and disadvantages. The separate case of uncertainty in the model calibration process is different to error propagation in a fixed model in that it is associated with a dynamic process of iterative parameter adjustment, and is compared in the context of non-linear regression and Bayesian approaches, such as Markov Chain Monte Carlo Simulation. Error propagation is investigated for a soil model representing the organic carbon depth profile and also a streamflow model using probabilistic simulation. Different sources of error are compared, including uncertainty in inputs, parameters and geometry. The results provided insights into error propagation and its computation in systems and models in general.

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geometry the organic carbon depth profile nonlinear regression dynamic process of iterative parameter adjustment hydrology environmental engineering ecology markov chain monte carlo inputs parameters differential error analysis bayesian approaches uncertainty prediction climate model calibration process probabilistic simulation computer models

S. Norng,L. R. Benke,T. J. Peterson,N. J. Robinson,K. K. Benke,.Error propagation in computer models: analytic approaches, advantages, disadvantages and constraints. 32 (10),2971–2985.

S. Norng, et al."Error propagation in computer models: analytic approaches, advantages, disadvantages and constraints" 32

S. Norng,L. R. Benke,T. J. Peterson,N. J. Robinson,K. K. Benke,"Error propagation in computer models: analytic approaches, advantages, disadvantages and constraints"