Bayesian inference and prediction in hydrology and environmental modelling. Development of novel Bayesian and Monte Carlo techniques for parameter estimation, uncertainty analysis and predictive applications. Areas of application have included hydrological models and river system models.
Mathematical modelling in hydrology and environmental engineering. Selection of governing equations and process representations in hydrological and snow models, including scientifically defensible hypothesis testing. Model development, optimisation and testing.
Applied numerical and statistical analysis in environmental engineering. Design and implementation of accurate, robust and computationally efficient numerical algorithms and software. Including solution of nonlinear differential equations, numerical integration, nonlinear optimisation problems, and others. Areas of application have included Richards equation for groundwater simulations, rainfall-runoff models, CO2 geosequestration models, and others
