Professor Martin Lambert

• Discharge reduction in straight compound channels caused by momentum interaction effects between the main channel and the floodplain.
• Development of techniques for discharge assessment in meandering river channels.
• Development of point rainfall simulation model for hydrological risk assessment.
• Characterization and modelling of long term persistence effects in Australian rainfall and augmentation using climate indices to determine hidden states.
• Determining the effects of catchment moisture conditions on flood frequency distributions.
• Modelling the dynamic nature of tidal inlets and the effect on flooding and water quality of coastal areas.
• Developing links between River Engineering and River/Floodplain ecology.

Professor Dmitri Kavetski

Dmitri Kavetski received his PhD in Environmental Engineering in 2005 at the University of Newcastle (Australia). One of the main contributions of Dmitri's thesis and subsequent research work has been the development of Bayesian Total Error Analysis (BATEA) - a comprehensive framework for parameter estimation and probabilistic prediction accounting for data and model uncertainties. BATEA is currently applied in hydrological modelling, with additional appplications developing in river system modelling, irrigation modelling and other areas of environmental engineering.

Dmitri's broader interests include mathematical modelling of surface and subsurface hydrological systems, uncertainty estimation, the development of numerically robust rainfall-runoff and snow models, design of accurate and computationally efficient numerical algorithms for nonlinear differential equations and nonlinear optimization. During his postdoctoral research at Princeton University in 2004-2007, Dmitri also made contributions to risk assessment of geosequestration scenarios.

Dmitri's international collaborations include the National Center for Atmospheric Research (NCAR, Boulder, USA), Centre Recherche Public Gabriel Lippmann (CRP-GL, Luxembourg), Swiss Federal Institute for Aquatic Science and Technology (EAWAG, Switzerland), CEMAGREF (Paris and Lyon, France), and other institutions worldwide.

Dr Mark Thyer

Mark's research interests are in the application of techniques in stochastic modelling, model calibration and uncertainty analysis and Bayesian technique to problems in:

• streamflow preditions using hydrological models
• water use predictions for urban water systems
• hydrological extremes (flood and droughts).

Dr Seth Westra

Seth has over eight years of industry and research experience in fields of flood hydrology, hydroclimatology, and hydrological statistics.

Seth's primary research areas currently include

• statistical downscaling to estimate future rainfall extremes at very short (sub-daily) timescales to use as the basis for flood estimation;
• generation of synthetic rainfall sequences at ungauged locations;
• evaluation of the capabilities of global climate models to simulate hydrological time series; and
•  the development of seasonal rainfall and streamflow forecasts at lead times of up to 12 months.

Before joining Adelaide University, Seth worked as a senior research associate in the hydrology group at the University of New South Wales (2009-2011) and as a hydrologist at Sinclair Knight Merz (2007-2008).

• coastal morphology and the River Murray mouth and Coorong
• long-term modelling of nearshore coastal phenomena
• environmental impact of man-made changes to coastal features
• climate change and its effect on the coast.

• prediction, forecasting and data analysis using statistically-based techniques
• optimisation using evolutionary algorithms and other heuristics derived from nature
• sensitivity, risk and uncertainty analysis
• sustainability assessment and decision-making
• engineering education

Professor Graeme Dandy
Graeme has had over 35 years of experience in the engineering of water systems. His main areas of research interest include:

• genetic algorithm techniques applied to the optimum design and operations of water distribution systems
• monitoring, modelling and optimising water quality in water distribution systems
• use of artificial neural networks techniques for forecasting hydrologic and water resources variables.

Associate Researchers