Dr David McInerney

Dr David McInerney

Grant-Funded Research Fellow

School of Civil Engineering and Construction

College of Engineering and Information Technology

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

 

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

Date Institution name Country Title
University of Adelaide, Adelaide Australia PhD
University of Adelaide, Adelaide Australia BSc, Mathematics and Computer Sciences (Honours)

Year Citation
2026 Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (2026). Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages. Environmental Modelling and Software, 196, 12 pages.
DOI
2024 McInerney, D., Thyer, M., Kavetski, D., Westra, S., Maier, H. R., Shanafield, M., . . . Leonard, M. (2024). Neglecting hydrological errors can severely impact predictions of water resource system performance. Journal of Hydrology, 634, 130853-1-130853-15.
DOI Scopus7 WoS7
2024 Thyer, M., Gupta, H., Westra, S., McInerney, D., Maier, H. R., Kavetski, D., . . . Tague, C. (2024). Virtual Hydrological Laboratories: Developing the Next Generation of Conceptual Models to Support Decision Making Under Change. Water Resources Research, 60(4), 1-17.
DOI Scopus12 WoS11
2023 McInerney, D., Westra, S., Leonard, M., Bennett, B., Thyer, M., & Maier, H. R. (2023). A climate stress testing method for changes in spatially variable rainfall. Journal of Hydrology, 625, Part A, 129876.
DOI Scopus6 WoS6
2023 Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (2023). Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts. Hydrology and Earth System Sciences, 27(4), 873-893.
DOI Scopus5 WoS5
2023 Renard, B., McInerney, D., Westra, S., Leonard, M., Kavetski, D., Thyer, M., & Vidal, J. (2023). Floods and Heavy Precipitation at the Global Scale: 100‐Year Analysis and 180‐Year Reconstruction. Journal of Geophysical Research: Atmospheres, 128(9), 26 pages.
DOI Scopus1 WoS1
2022 Renard, B., Thyer, M., McInerney, D., Kavetski, D., Leonard, M., & Westra, S. (2022). A Hidden Climate Indices Modeling Framework for Multi‐Variable Space‐Time Data. Water Resources Research, 58(2), 1-27.
DOI Scopus9 WoS8
2022 Qin, Y., Kavetski, D., Kuczera, G., McInerney, D., Yang, T., & Guo, Y. (2022). Technical Note: Can Gauss‐Newton algorithms outperform stochastic optimization algorithms when calibrating a highly parameterized hydrological model? A case study using SWAT. Water Resources Research, 58(11), 14 pages.
DOI Scopus7 WoS7
2022 McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., & Kuczera, G. (2022). Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too. HYDROLOGY AND EARTH SYSTEM SCIENCES, 26(21), 5669-5683.
DOI Scopus8 WoS8
2022 Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H., . . . Kavetski, D. (2022). Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches. WIREs Water, 9(5), 1-25.
DOI Scopus30 WoS29
2021 Hunter, J., Thyer, M., McInerney, D., & Kavetski, D. (2021). Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions. Journal of Hydrology, 603, 1-22.
DOI Scopus18 WoS15
2021 McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., & Kuczera, G. (2021). Improving the reliability of sub‐seasonal forecasts of high and low flows by using a flow‐dependent non‐parametric model. Water Resources Research, 57(11), 16 pages.
DOI Scopus17 WoS15
2020 Lerat, J., Thyer, M., McInerney, D., Kavetski, D., Woldemeskel, F., Pickett-Heaps, C., . . . Feikema, P. (2020). A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data. Journal of Hydrology, 591, 1-15.
DOI Scopus22 WoS19
2020 McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Tuteja, N., & Kuczera, G. (2020). Multi‐temporal hydrological residual error modelling for seamless sub‐seasonal streamflow forecasting. Water Resources Research, 56(11), 1-33.
DOI Scopus39 WoS37
2020 Schwarzwald, K., Poppick, A., Rugenstein, M., Bloch-Johnson, J., Wang, J., McInerney, D., & Moyer, E. J. (2020). Changes in future precipitation mean and variability across scales. Journal of Climate, 34(7), 1-55.
DOI Scopus13 WoS12
2019 McInerney, D., Kavetski, D., Thyer, M., Lerat, J., & Kuczera, G. (2019). Benefits of explicit treatment of zero flows in probabilistic hydrological modelling of ephemeral catchments. Water Resources Research, 55(12), 11035-11060.
DOI Scopus20 WoS20
2019 Wright, D., Thyer, M. A., Westra, S., Renard, B., & McInerney, D. (2019). A generalised approach for identifying influential data in hydrological modelling. Environmental Modelling and Software, 111, 231-247.
DOI Scopus8 WoS8
2018 McInerney, D., Thyer, M. A., Kavetski, D., Bennett, B., Lerat, J., Gibbs, M., & Kuczera, G. (2018). A simplified approach to produce probabilistic hydrological model predictions. Environmental Modelling & Software, 109, 306-314.
DOI Scopus31 WoS29
2018 Wright, D., Thyer, M., Westra, S., & McInerney, D. (2018). A hybrid framework for quantifying the influence of data in hydrological model calibration. Journal of Hydrology, 561, 211-222.
DOI Scopus9 WoS8
2018 McInerney, D., Thyer, M. A., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M., & Kuczera, G. (2018). The importance of spatiotemporal variability in irrigation inputs for hydrological modelling of irrigated catchments. Water Resources Research, 54(9), 6792-6821.
DOI Scopus27 WoS25
2018 Woldemeskel, F., McInerney, D., Lerat, J., Thyer, M., Kavetski, D., Shin, D., . . . Kuczera, G. (2018). Evaluating post-processing approaches for monthly and seasonal streamflow forecasts. Hydrology and Earth System Sciences, 22(12), 6257-6278.
DOI Scopus45 WoS45
2018 Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., & Kavetski, D. (2018). State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application. Hydrology and Earth System Sciences, 22(1), 871-887.
DOI Scopus37 WoS33
2017 McInerney, D., Thyer, M., Kavetski, D., Lerat, J., & Kuczera, G. (2017). Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors. Water Resources Research, 53(3), 2199-2239.
DOI Scopus120 WoS114
2016 Payne, J., McInerney, D., Barovich, K., Kirkland, C., Pearson, N., & Hand, M. (2016). Strengths and limitations of zircon Lu-Hf and O isotopes in modelling crustal growth. Lithos, 248-251, 175-192.
DOI Scopus115 WoS115
2016 Poppick, A., McInerney, D., Moyer, E., & Stein, M. (2016). Temperatures in transient climates: improved methods for simulations with evolving temporal covariances. Annals of Applied Statistics, 10(1), 477-505.
DOI Scopus16 WoS16
2016 Huang, W. K., Stein, M. L., McInerney, D. J., Sun, S., & Moyer, E. J. (2016). Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions. Advances in Statistical Climatology, Meteorology and Oceanography, 2(1), 79-103.
DOI
2016 Bao, J., McInerney, D., & Stein, M. (2016). A spatial-dependent model for climate emulation. Environmetrics, 27(7), 396-408.
DOI Scopus4 WoS5
2015 Payne, J., Hand, M., Pearson, N., Barovich, K., & McInerney, D. (2015). Crustal thickening and clay: controls on O isotope variation in global magmatism and siliclastic sedimentary rocks. Earth and Planetary Science Letters, 412, 70-76.
DOI Scopus34 WoS32
2014 Glotter, M., Elliott, J., McInerney, D., Best, N., Foster, I., & Moyer, E. (2014). Evaluating the utility of dynamical downscaling in agricultural impacts projections. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8776-8781.
DOI Scopus67 WoS64 Europe PMC11
2014 Evin, G., Thyer, M., Kavetski, D., McInerney, D., & Kuczera, G. (2014). Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity. Water Resources Research, 50(3), 2350-2375.
DOI Scopus142 WoS130
2014 Castruccio, S., McInerney, D., Stein, M., Crouch, F., Jacob, R., & Moyer, E. (2014). Statistical emulation of climate model projections based on precomputed GCM runs. Journal of Climate, 27(5), 1829-1844.
DOI Scopus91 WoS88
2014 Li, D., Jacobson, A., & McInerney, D. (2014). A reactive-transport model for examining tectonic and climatic controls on chemical weathering and atmospheric CO₂ consumption in granitic regolith. Chemical Geology, 365, 30-42.
DOI Scopus53 WoS49
2013 O'Brien, J., Aherne, S., McCormack, O., Jeffers, M., & McInerney, D. (2013). MRI Features of Bilateral Amyloidosis of Breast. BREAST JOURNAL, 19(3), 338-339.
DOI WoS7
2012 McInerney, D., Lempert, R., & Keller, K. (2012). What are robust strategies in the face of uncertain climate threshold responses?. Climatic Change, 112(3-4), 547-568.
DOI Scopus111 WoS103
2012 Hall, J. W., Lempert, R. J., Keller, K., Hackbarth, A., Mijere, C., & McInerney, D. J. (2012). Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods. Risk Analysis, 32(10), 1657-1672.
DOI Scopus226 WoS216 Europe PMC23
2010 McInerney, D., Teubner, M., & Noye, B. (2010). A second-order analytic solution for oscillatory wind-induced flow in an idealized shallow lake. Computers & Fluids, 39(9), 1500-1509.
DOI Scopus2 WoS2
2008 Keller, K., McInerney, D., & Bradford, D. F. (2008). Carbon dioxide sequestration: how much and when?. Climatic Change, 88(3-4), 267-291.
DOI Scopus38 WoS36
2008 McInerney, D., & Keller, K. (2008). Economically optimal risk reduction strategies in the face of uncertain climate thresholds. Climatic Change, 91(1-2), 29-41.
DOI Scopus47 WoS37
2008 Keller, K., & McInerney, D. (2008). The dynamics of learning about a climate threshold. Climate Dynamics, 30(2-3), 321-332.
DOI Scopus33 WoS31
2008 Baehr, J., McInerney, D., Keller, K., & Marotzke, J. (2008). Optimization of an observing system design for the North Atlantic meridional overturning circulation. Journal of Atmospheric and Oceanic Technology, 25(4), 625-634.
DOI Scopus19 WoS15
2008 McInerney, D., Lynch, M., Donegan, J. F., & Weldon, V. (2008). Mode referencing of an external cavity diode laser for continuous frequency stabilization. OPTICAL ENGINEERING, 47(2), 5 pages.
DOI WoS2
2000 McInerney, D., & Noye, B. (2000). A triangular coastal element developed for use in finite difference tidal models. The ANZIAM Journal, 42, C936-C953.
2000 Noye, B., & McInerney, D. (2000). Numerical study of the stability of some explicit finite-difference methods for oscillatory advection. The ANZIAM Journal, 42(1), C1076-C1096.

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.
DOI
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.
DOI
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.
DOI Scopus1
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.
DOI
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.
DOI
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 Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 612-625). virtual online: Engineers Australia.
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. 444-452). 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 Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 482-496). Adelaide, Australia: Engineers Australia.
2021 Hunter, J., Thyer, M., McInerney, D., & Kavetski, D. (2021). High-quality probabilistic predictions for existing hydrological models with common objective functions. In Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 626-639). Adelaide, Australia: Engineers Australia.
DOI
2021 Kavetski, D., Lerat, J., McInerney, D., & Thyer, M. (2021). Can a daily rainfall-runoff model be successfully calibrated to monthly streamflow data?. In Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 806-817). virtual online: Engineers Australia.
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.
2006 Weldon, V., McInerney, D., Phelan, R., Lynch, M., & Donegan, J. (2006). Characteristics of several NIR tuneable diode lasers for spectroscopic based gas sensing: A comparison. In SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY Vol. 63 (pp. 1013-1020). ITALY, Florence: PERGAMON-ELSEVIER SCIENCE LTD.
DOI WoS22
1988 MILLER, F. R., MCINERNEY, D., & MILLER, B. E. (1988). GROWTH INTERACTIONS BETWEEN CELLS FROM SEQUENTIAL STAGES OF MOUSE MAMMARY-GLAND FROM NORMAL THROUGH PRENEOPLASIA TO HETEROGENEOUS TUMOR. In PROCEEDINGS OF THE AMERICAN ASSOCIATION FOR CANCER RESEARCH Vol. 29 (pp. 59). AMER ASSOC CANCER RESEARCH.
1986 MILLER, F. R., MCINERNEY, D., & MILLER, B. E. (1986). DIFFERENTIAL ABILITY OF QUABAIN TO UNCOUPLE NORMAL, PRENEOPLASTIC, AND NEOPLASTIC MOUSE MAMMARY CELLS. In PROCEEDINGS OF THE AMERICAN ASSOCIATION FOR CANCER RESEARCH Vol. 27 (pp. 149). AMER ASSOC CANCER RESEARCH.

Year Citation
2023 McInerney, D., Wu, T. T., Weinhouse, G., Wallace, B., & Devlin, J. (2023). NEXT-NIGHT DISRUPTED SLEEP PREDICTION FROM CLINICIAN NOTES USING WEAKLY SUPERVISED LEARNING MODELS. Poster session presented at the meeting of CRITICAL CARE MEDICINE. CA, San Francisco: LIPPINCOTT WILLIAMS & WILKINS.
2023 Wu, T. T., McInerney, D., Weinhouse, G., Wallace, B., & Devlin, J. (2023). PREDICTORS OF ICU CLINICIAN DOCUMENTATION OF DISRUPTED SLEEP IN CRITICALLY ILL MEDICAL ADULTS. Poster session presented at the meeting of CRITICAL CARE MEDICINE. CA, San Francisco: LIPPINCOTT WILLIAMS & WILKINS.
2022 Thyer, M., Hunter, J., McInerney, D., & Kavetski, D. (2022). High-quality probabilistic predictions for existing hydrological models with common objective functions. Poster session presented at the meeting of Abstracts of the EGU General Assembly 2022. Vienna, Austria & Online: Copernicus GmbH.
DOI
2021 Leigh, R., Knowling, M., Westra, S., Bennett, B., Zecchin, A., Maier, H., . . . Devanand, A. (2021). A multi-modelling framework to stress-test water resource systems under change. Poster session presented at the meeting of 24th International Congress on Modelling and Simulation (MODSIM2021) Book of Abstracts. Sydney, Australia: Modelling and Simulation Society of Australia and New Zealand Inc..
- Thyer, M., Gupta, H., Westra, S., McInerney, D., Maier, H., Kavetski, D., . . . Tague, C. (n.d.). Virtual Hydrological Laboratories: Developing the next generation of conceptual models to support decision-making under change. Poster session presented at the meeting of Unknown Conference.
DOI

Year Citation
2022 Westra, S., Leigh, R., Knowling, M., Beh, E., Devanand, A., Thyer, M., . . . McInerney, D. (2022). Assessment of current and future water security in the Barossa and Eden Valleys.
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).

Year Citation
- Thyer, M., McInerney, D., Kavetski, D., Kuczera, G., Lerat, J., Tuteja, N., . . . Shin, D. (n.d.). Seasonal Streamflow Forecasting for Water Management: Advances and Opportunities.
DOI

Year Citation
- Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (n.d.). Supporting data for "Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages" by Laugesen et al. (2025).
DOI
- 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.
DOI
- 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).
DOI
- Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (n.d.). Supporting data for "Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts" by Laugesen et.al. (2023).
DOI

Year Citation
2017 McInerney, D., Bennett, B. S., Thyer, M., & Kavetski, D. (2017). Interactive Probabilistic Predictions [Computer Software]. http://www.probabilisticpredictions.org/.
DOI
- McInerney, D., Westra, S., Leonard, M., Kavetski, D., Thyer, M., & Maier, H. (n.d.). Code and data for 'Tailored Calibration of Stochastic Weather Generators for Enhanced Hydrological System Evaluation' by McInerney et al [Computer Software].
DOI

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).
DOI

Year Citation
- Thyer, M., Kavetski, D., & McInerney, D. (n.d.). Introduction to Evaluating Probabilistic Predictions.
DOI
- Thyer, M., Kavetski, D., McInerney, D., & Renard, B. (n.d.). Introduction to Residual Diagnostics.
DOI
- Thyer, M., Kavetski, D., McInerney, D., & Hunter, J. (n.d.). Guidance on Enhancing Probabilistic Predictions.
DOI
- Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., & Tuteja, N. (n.d.). Do you want high quality subseasonal streamflow forecasts? Ask MuTHRE! vEGU 2021 Conference Presentation.
DOI
- Thyer, M., Hunter, J., McInerney, D., & Kavetski, D. (n.d.). High-quality probabilistic predictions for existing hydrological models with common objective functions: EGU2022 Conference Presentation.
DOI
- Thyer, M., McInerney, D., Kavetski, D., Westra, S., Maier, H., Shanafield, M., . . . Leonard, M. (n.d.). Neglecting hydrological errors can severely impact predictions of water resource system performance.
DOI

Year Citation
- Mcinerney, D., Thyer, M., Kavetski, D., & Kuzera, G. (n.d.). Practical guidance on representing uncertainty in hydrological predictions.
DOI
- McInerney, D. (n.d.). Improving Probabilistic Streamflow Predictions.
DOI
- Thyer, M., McInerney, D., Kavetski, D., Kuczera, G., & Lerat, J. (n.d.). Improving probabilistic prediction of daily streamflow.
DOI
- McInerney, D. (n.d.). Post-processing hydrological predictions: Quantifying impacts and enhancing subseasonal forecasts.
DOI

Lecturing

  • Engineering Hydrology III (2015-2017)
  • Engineering Modelling and Analysis II (2015-2017)

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
2019 Co-Supervisor Improving the value of subseasonal streamflow forecasts for water resource management decisions Doctor of Philosophy Doctorate Full Time Mr Richard Mark Laugesen

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