Ph.D. 2005, M.EngSc. 2002, B.E. 2001, University of New South Wales. Quantitative analysis of hydrological data and processes. Emphases on the study of uncertainties arising from data errors and imperfect model structures; rainfall runoff model calibration; Bayesian statistical inference in modeling natural systems; probabilistic modeling; risk analysis in water resources planning.

Recent Publications

  • Smith, T.J. and L.A. Marshall. 2010. Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework. Environmental Modeling and Software. doi:10.1016/j.envsoft.2009.11.010.
  • Smith, T.J. and L.A. Marshall. 2009. A conceptual precipitation-runoff modeling suite: Model selection, calibration and predictive uncertainty assessment. In Anderssen, R.S., R.D. Braddock and L.T.H. Newham (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, pp. 3556-3562. ISBN: 978-0-9758400-7-8.
  • Jencso, K., B. McGlynn, M. Gooseff, S. Wondzell, K. Bencala, L. Marshall. 2009. Hydrologic Connectivity Between Landscapes and Streams: Transferring Reach and Plot Scale Understanding to the Catchment Scale, Water Resources Research, 45, W04428, doi:10.1029/2008WR007225.
  • Smith, T. J., and L. A. Marshall. 2008. Bayesian methods in hydrologic modeling: A study of recent advancements in Markov chain Monte Carlo techniques, Water Resources Research, 44, W00B05, doi:10.1029/2007WR006705.
  • Marshall, L., A. Sharma, D. Nott. 2007. A single model ensemble versus a dynamic modeling platform: Semi-distributed rainfall runoff modeling in a Hierarchical Mixtures of Experts framework. Geophysical Research Letters, 34, L01404, doi:10.1029/2006GL028054.
  • Marshall, L., A. Sharma, and D. Nott. 2007. Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework. Hydrological Processes 21(7), 847-861, DOI: 10.1002/hyp.6294.
  • Wagener, T., M. Weiler, B. McGlynn, L. Marshall, M. McHale, T. Meixner and K. McGuire. 2007. Taking the pulse of hydrology education. Hydrological Processes, 21, 1789–1792, DOI: 10.1002/hyp.6766.
  • Marshall, L., A. Sharma, and D. Nott. 2006. Modeling the catchment via mixtures: Issues of model specification and validation. Water Resources Research 42(11):1-14.
  • Sharma, A., L. Marshall, and D. Nott. 2006. A Bayesian view of rainfall-runoff modeling: Alternatives for parameter estimation, model comparison and hierarchical model development. in Prediction in Ungauged Basins, edited by M. Sivapalan, et al., IAHS Press, Wallingford, U.K .
  • Marshall, L., D. Nott, and A. Sharma. 2005. Hydrological model selection: A Bayesian alternative. Water Resources Research 41(10):1-11.
  • Marshall, L., D. Nott, and A. Sharma. 2004. A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling. Water Resour. Res. 40(2):1-11.
  • Roser, D., J. Skinner, C. LeMaitre, L. Marshall, J. Baldwin, K. Billington., S. Kotz., K. Clarkson and N. Ashbolt. 2002. Automated event sampling for microbiological and related analytes in remote sites: a comprehensive system. Water Science and Technology: Water Supply. 2(3):123-130.