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Information - TM1 Is modelling more than a fashionable indoor sport? Representativity, predictive capacity and uncertainty of environmental models
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Modelling is a widespread practice in geosciences, with applications ranging from global climate models to microscale flow processes. Models are used to understand the integration of different processes, to test hypotheses, and to predict unknown states of a system. However, despite advances in understanding physical processes, improved data availability and processing power, models are necessarily a simplification of reality. Additionally, input and calibration data are prone to measurement errors, or may not contain sufficient information to properly calibrate the model. Therefore, parameterisation of models is prone to problems with identifiability, uniqueness and stability.
As a result, model predictions may be highly uncertain, up to the point where the predictive capacity of a model is not sufficient to distinguish between different hypotheses, or to generate predictions that are of any practical use. In this session, we explore how scientists from different scientific disciplines solve problems related to uncertainty and predictive capacity of environmental models. Additionally, we examine how benchmarking can be used to quantify advances in environmental modelling and prediction due to improved data availability, computing power and scientific understanding of processes.
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