Uncertainty analysis of environmental simulation models

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Our basic understanding and mathematical representation of complex environmental systems via environmental simulation models, as well as the measurement of environmental variables or quantities, are only approximations of the true state of nature. The degree of approximation varies by location and type of environmental system being studied. Sound environmental management decisions based on environmental simulation model predictions should explicitly consider the propagation of these approximations into overall model prediction uncertainty. The research questions I plan to investigate over the next five years and beyond are ultimately focused on developing robust and efficient techniques for assessing environmental simulation model prediction uncertainty for modelling studies where measured calibration data are available.   New methods for model calibration and uncertainty analysis will be developed and compared with existing approaches for a variety of environmental modelling case studies. This research will focus specifically on hydrologic or watershed simulation models that predict flow, nutrient and other pollutant transport within a watershed. All real case studies will be agricultural watersheds located mainly in Ontario. The findings of this research will be of interest for all engineers, researchers and decision-makers who are using environmental simulation models. However, the most important benefit of this research will be enhanced decision-making based on environmental modelling. This research will develop tools through which uncertainty analyses of model predictions can be conducted for more modelling studies.  With an uncertainty assessment of future environmental system behaviour, rather than a deterministic analysis, decision-makers can more effectively trade-off risks and costs and therefore improve the management of environmental systems.