Biography
Tyler Smith joined Clarkson University in 2012 as an assistant professor in the Department of Civil and Environmental Engineering and an affiliate faculty member in the Institute for a Sustainable Environment following the completion of his PhD from Montana State University. He studies hydrology through the lens of water resources engineering and environmental science and his research focuses on improving water resources decision-making by better understanding hydrologic models and their uncertainties. Outside of work, Tyler enjoys spending time with his wife and kids and exploring the beauty of Northern New York.
Education
Montana State University
Montana State University
Montana State University
Experience
Areas of Expertise
Courses Taught
Research Interests
• Integrative watershed studies 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
Awards
Active Research Grants
Niagara and St. Lawrence River Operation Forecasting: Present and Future.
Funding Source: New York Power Authority
PIs: Tyler Smith (Clarkson), HT Shen (Clarkson)
Publications
Peer-Reviewed Articles
1. Smith, T., K. Hayes, L. Marshall, B. McGlynn, and K. Jencso (2016). Diagnostic calibration and cross-catchment transferability of a simple process-consistent hydrologic model, Hydrological Processes, doi: 10.1002/hyp.10955.
2. Tang, Y., L. Marshall, A. Sharma, and T. Smith (2016). Tools for investigating the prior distribution in Bayesian hydrology, Journal of Hydrology, doi: 10.1016/j.jhydrol.2016.04.032.
3. Smith, T., L. Marshall, and A. Sharma (2015). Modeling residual hydrologic errors with Bayesian inference, Journal of Hydrology, doi: 10.1016/j.jhydrol.2015.05.051.
4. Smith, T., L. Marshall, and A. Sharma (2014). Predicting hydrologic response through a hierarchical catchment knowledgebase: A Bayes empirical Bayes approach, Water Resources Research, 50, 1189-1204, doi: 10.1002/2013WR015079.
5. Smith, T., L. Marshall, and B. McGlynn (2014). Calibrating hydrologic models in flow-corrected time, Water Resources Research, 50, 748-753, doi: 10.1002/2013WR014635.
6. Smith, T., L. Marshall, B. McGlynn, and K. Jencso (2013). Using field data to inform and evaluate a new model of catchment hydrologic connectivity, Water Resources Research, 49, 6834-6846, doi: 10.1002/wrcr.20546.
7. 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.
8. 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.
9. 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.
10. 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. Marshall, L., K. Weber, T. Smith, M. Greenwood, and A. Sharma (in review). On the relationship between optimized models and hydrologic signatures towards improved catchment regionalization. Submitted to Journal of Hydrology.
2. Jayathilake, D. and T. Smith (in prep). Predicting the temporal transferability of model parameters through a hydrologic signature analysis. To be submitted to Journal of Hydrology.
3. Smith, T., L. Marshall, and B. McGlynn (in prep). Catchment classification, directionality, and the pursuit of universality. To be submitted to Hydrology and Earth System Sciences.
4. Perera, C., C. Corrigan, T. Smith, A. Baker, V. Peterson, and D. Ding (in prep). Calibrating coupled hydrologic models: Assessing strategies for improved performance and robustness. To be submitted to Water Resources Research.