Matthew Knowling

Dr Matthew Knowling

Postdoctoral Research Fellow

School of Civil, Environmental and Mining Engineering

Faculty of Engineering, Computer and Mathematical Sciences


My research focuses on the development and application of state-of-the-art modelling approaches to provide robust decision-making support for real-world resource management problems.

My work combines data assimilation, uncertainty assessment and optimisation under uncertainty approaches with process-based environmental and agricultural models. I have extensive experience in developing and applying hydrological models that simulate advective-dispersive-reactive contaminant transport to identify optimal land-use management strategies (e.g., by maximising farm production while satisfying ecological river health conditions). I also specialise in crop modelling for vineyard forecasting and operational irrigation and canopy management decision support.

A particular motivation of mine is improving the efficiency and scalability of the decision-support modelling approaches such that they are applicable to complex and high dimensional problems. I am also interested in model emulation and data-driven methods; however, most problems that I have encountered to date have been ill-posed (i.e., there are insufficient data to inform the many model dimensions needed to adequately characterise uncertainty).

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  • Journals

    Year Citation
    2021 White, J. T., Hemmings, B., Fienen, M. N., & Knowling, M. J. (2021). Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses. Environmental Modelling and Software, 139, 105022.
    DOI Scopus1 WoS1
    2021 Knowling, M. J., Bennett, B., Ostendorf, B., Westra, S., Walker, R. R., Pellegrino, A., . . . Grigg, D. (2021). Bridging the gap between data and decisions: A review of process-based models for viticulture. Agricultural Systems, 193, 13 pages.
    DOI Scopus2 WoS1
    2020 Knowling, M. J., White, J. T., Moore, C. R., Rakowski, P., & Hayley, K. (2020). On the assimilation of environmental tracer observations for model-based decision support. Hydrology and Earth System Sciences, 24(4), 1677-1689.
    DOI Scopus5 WoS5
    2020 Partington, D., Knowling, M. J., Simmons, C. T., Cook, P. G., Xie, Y., Iwanaga, T., & Bouchez, C. (2020). Worth of hydraulic and water chemistry observation data in terms of the reliability of surface water-groundwater exchange flux predictions under varied flow conditions. Journal of Hydrology, 590, 17 pages.
    DOI Scopus5 WoS5
    2020 Hemmings, B., Knowling, M. J., & Moore, C. R. (2020). Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example. Frontiers in Earth Science, 8, 22 pages.
    DOI Scopus3 WoS3
    2020 White, J. T., Foster, L. K., Fienen, M. N., Knowling, M. J., Hemmings, B., & Winterle, J. R. (2020). Toward Reproducible Environmental Modeling for Decision Support: A Worked Example. Frontiers in Earth Science, 8, 11 pages.
    DOI Scopus6 WoS5
    2020 Knowling, M. J., White, J. T., McDonald, G. W., Kim, J. H., Moore, C. R., & Hemmings, B. (2020). Disentangling environmental and economic contributions to hydro-economic model output uncertainty: An example in the context of land-use change impact assessment. Environmental Modelling and Software, 127, 13 pages.
    DOI Scopus3 WoS3
    2020 White, J., Knowling, M., Fienen, M., Feinstein, D., McDonald, G., & Moore, C. (2020). A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support. Environmental Modelling and Software, 126, 104657-1-104657-11.
    DOI Scopus5 WoS5
    2020 White, J. T., Knowling, M. J., & Moore, C. R. (2020). Consequences of Groundwater-Model Vertical Discretization in Risk-Based Decision-Making. Groundwater, 58(5), 695-709.
    DOI Scopus6 WoS5
    2019 Knowling, M., White, J., & Moore, C. (2019). Role of model parameterization in risk-based decision support: An empirical exploration. Advances in Water Resources, 128, 59-73.
    DOI Scopus18 WoS18
    2017 Knowling, M., & Werner, A. (2017). Transient recharge estimability through field-scale groundwater model calibration. Groundwater, 55(6), 827-840.
    DOI Scopus4 WoS4 Europe PMC1
    2016 Knowling, M. J., & Werner, A. D. (2016). Estimability of recharge through groundwater model calibration: insights from a field-scale steady-state example. Journal of Hydrology, 540, 973-987.
    DOI Scopus18 WoS18
    2015 Knowling, M. J., Werner, A. D., & Herckenrath, D. (2015). Quantifying climate and pumping contributions to aquifer depletion using a highly parameterised groundwater model: Uley South Basin (South Australia). Journal of Hydrology, 523, 515-530.
    DOI Scopus22 WoS22
    2013 Liggett, J. E., Knowling, M. J., Werner, A. D., & Simmons, C. T. (2013). On the implementation of the surface conductance approach using a block-centred surface-subsurface hydrology model. Journal of Hydrology, 496, 1-8.
    DOI Scopus5 WoS5
  • Position: Postdoctoral Research Fellow
  • Phone: 83131231
  • Email: matthew.knowling@adelaide.edu.au
  • Campus: North Terrace
  • Building: Engineering North, floor 1
  • Room: N1 25
  • Org Unit: School of Civil, Environmental and Mining Engineering

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