Dr David McInerney
ARC Grant-Funded Researcher (B)
School of Civil, Environmental and Mining Engineering
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
My research interests involve the use of mathematical and statistical models to improve the understanding and management of physical systems. Areas which my research has focussed on include
- Quantifying uncertainty in hydrological predictions
- Developing statistical models for emulating complex climate models
- Robust decision making with respect to uncertain future climate change
- Hydrodynamic modelling of lakes and flood plains
- Numerical and statistical modelling of geological processes
Check out some of my recent research on the Intelligent Water Decisions Blog and my talk at the 2016 DEWNR NRM Science Conference
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Appointments
Date Position Institution name 2016 - 2016 Principal Hydrologist Government of South Australia, Adelaide 2012 - ongoing Senior Research Associate University of Adelaide, Adelaide 2007 - 2012 Research Associate University of Chicago, Chicago 2005 - 2007 Postdoctoral Research Scholar Pennsylvania State University, University Park -
Education
Date Institution name Country Title University of Adelaide, Adelaide Australia PhD University of Adelaide, Adelaide Australia BSc, Mathematics and Computer Sciences (Honours)
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Journals
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Conference Papers
Year Citation 2021 Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H., . . . Kavetski, D. (2021). Modelling hydrological change due to wildfires. In Proceedings of the 24th International Congress on Modelling and Simulation (MODSIM2021) (pp. 582-587). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand.
2021 Kavetski, D., McInerney, D., Hunter, J., & Thyer, M. (2021). Recent learnings towards achieving high quality probabilistic predictions in practical applications of hydrological models. In R. W. Vervoort, A. A. Voinov, J. P. Evans, & L. Marshall (Eds.), MODSIM2021, 24th International Congress on Modelling and Simulation. (pp. 526-532). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand.
2021 McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemsekel, F., Tuteja, N., & Kuczera, G. (2021). Improving sub-seasonal streamflow forecasts across flow regimes. In MODSIM2021, 24th International Congress on Modelling and Simulation. (pp. 616-622). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand.
2021 Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., & Kuczera, G. (2021). Advances in subseasonal streamflow forecasting: An overview. In R. W. Vervoort, A. A. Voinov, J. P. Evans, & L. Marshall (Eds.), MODSIM2021, 24th International Congress on Modelling and Simulation. (pp. 623-629). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand.
2021 Lerat, J., Kavetski, D., McInerney, D., & Thyer, M. (2021). A method to calibrate daily rainfall-runoff models to monthly streamflow data. In MODSIM2021, 24th International Congress on Modelling and Simulation. (pp. 533-539). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand.
2021 Thyer, M., McInerney, D., Kavetski, D., & Hunter, J. (2021). Practical approaches to produce high-quality probabilistic predictions and improve risk-based design making. In To be advised. Virtual Symposium. 2021 McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Tuteja, N., & Kuczera, G. (2021). The MuTHRE Model for High Quality Sub-seasonal Streamflow Forecasts. In Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 1-9). Online: Engineers Australia. 2021 Kavetski, D., McInerney, D., Thyer, M., Lerat, J., & Kuczera, G. (2021). Probabilistic streamflow prediction and uncertainty estimation in ephemeral catchments. In To be advised. Virtual Symposium. 2021 Kavetski, D., Lerat, J., McInerney, D., & Thyer, M. (2021). Can a daily rainfall-runoff model be successfully calibrated to monthly streamflow data?. In To be advised. Virtual Symposium. 2014 McInerney, D., Thyer, M., Kavetski, D., & Kuczera, G. (2014). Evaluating different approaches for using the Box-Cox transformation to model heteroscedasticity in residual errors of hydrological models. In Hydrology and Water Resources Symposium 2014, HWRS 2014 - Conference Proceedings (pp. 937-944). online: Engineers Australia. Thyer, M., Hunter, J., McInerney, D., & Kavetski, D. (n.d.). High-quality probabilistic predictions for existing hydrological models with common objective functions    . Copernicus GmbH.
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Report for External Bodies
Year Citation 2021 McInerney, D., Thyer, M., Kavetski, D., & Kuczera, G. (2021). Technical note on producing spatially correlated daily streamflow forecasts at semi-gauged sites. 2020 McInerney, D. J., Thyer, M., Kavetski, D., & Kuczera, G. (2020). Technical note on producing spatially correlated streamflow forecasts at multiple gauged sites. 2019 McInerney, D. J., Thyer, M. A., & Kavetski, D. (2019). Technical Note on Improving Seamless Streamflow Forecasting using Multi-time scale Hydrological Residual Error Modelling. Australian Bureau of Meteorology. 2018 McInerney, D., Thyer, M. A., & Kavetski, D. (2018). Assessment of DEW Surface Water Team Modelling Approaches. South Australian Department of Environment and Water (DEW). -
Datasets
Year Citation McInerney, D., Thyer, M., & Kavetski, D. (n.d.). Supporting data for "Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model" by McInerney et al. (2021).
McInerney, D., Thyer, M., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M., & Kuczera, G. (n.d.). The importance of Spatiotemporal Variability in irrigation inputs for hydrological modelling of irrigated catchments - Datasets.
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Software
Year Citation 2017 McInerney, D., Bennett, B. S., Thyer, M., & Kavetski, D. (2017). Interactive Probabilistic Predictions [Computer Software]. http://www.probabilisticpredictions.org/.
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Filesets
Year Citation McInerney, D., Thyer, M., & Kavetski, D. (n.d.). Supporting data for "Benefits of explicit treatment of zero flows in probabilistic hydrological modelling of ephemeral catchments" by McInerney et al. (2019).
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Presentations
Year Citation McInerney, D. (n.d.). Improving Probabilistic Streamflow Predictions.
Thyer, M., McInerney, D., Kavetski, D., Kuczera, G., & Lerat, J. (n.d.). Improving probabilistic prediction of daily streamflow.
Mcinerney, D., Thyer, M., Kavetski, D., & Kuzera, G. (n.d.). Practical guidance on representing uncertainty in hydrological predictions.
Lecturing
- Engineering Hydrology III (2015-2017)
- Engineering Modelling and Analysis II (2015-2017)
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2019 Co-Supervisor Improving the value of subseasonal streamflow forecasts for water resource management decisions Doctor of Philosophy Doctorate Part Time Mr Richard Mark Laugesen 2018 Co-Supervisor Influence of Catchment-Scale Characteristics on Residual Model Selection and Parameters Doctor of Philosophy Doctorate Part Time Mr Jason Hunter
Connect With Me
External Profiles