Tyler J. Smith
230 Rowley Laboratories
Box 5710, Potsdam, NY 13699-5710
• Ph.D. in Ecology & Environmental Sciences (May 2012), Department of Land Resources & Environmental Sciences, Montana State University, Bozeman, MT.
• M.S. in Civil Engineering (December 2008), Department of Civil & Environmental Engineering, Montana State University, Bozeman, MT.
• B.S. in Civil Engineering, Bio-Resources Engineering Option (May 2006), Department of Civil & Environmental Engineering, Montana State University, Bozeman, MT.
• Assistant Professor, Department of Civil & Environmental Engineering, Clarkson University (July 2012 – present)
• Graduate Research Assistant, Department of Land Resources & Environmental Sciences, Montana State University (January 2007 – May 2012)
• Visiting Graduate Research Assistant, School of Civil & Environmental Engineering, University of New South Wales (June 2009 – May 2010)
• Integrative watershed studies (hydrology, water quality, etc.) driven by quantitative analysis
• Use of Bayesian statistical approaches to environmental systems modeling
• Collaboration with experimentalists to improve hydrologic model realism
• Hydrologic modeling in data-scarce regions and/or regions under change
1. Smith, T., A. Sharma, L. Marshall, R. Mehrotra, and S. Sisson (2010). Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments, Water Resources Research, 46, W12551, doi: 10.1029/2010WR009514.
2. Smith, T. J. and L. A. Marshall (2010). Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework. Environmental Modelling & Software, 25(6), 691-701.
3. 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. July 2009, pp. 3556-3562. ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/I8/smith_tj.pdf.
4. 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.
Articles in preparation/review
1. Smith, T., L. Marshall, and A. Sharma (in review). Modeling residual hydrologic errors with Bayesian inference. Submitted to Journal of Hydrology.
2. Smith, T., L. Marshall, B. McGlynn, and K. Jencso (in review). Using field data to inform and evaluate a new model of catchment hydrologic connectivity. Submitted to Water Resources Research.
3. Smith, T., L. Marshall, and A. Sharma (in review). Predicting hydrologic response through a hierarchical catchment knowledgebase - A Bayes Empirical Bayes approach. Submitted to Water Resources Research.
4. Smith, T., L. Marshall, and B. McGlynn (in prep). Improving hydrologic model calibration using a flow-corrected time transformation. To be submitted to Water Resources Research.
CE 330: Water Resources Engineering I, Fall 2012