Error propagation in computer models: analytic approaches, advantages, disadvantages and constraints

Abstract

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 par..