Ph.D., Civil Engineering, ​​University of Texas at Austin, 2005M.S., Civil Engineering, ​University of Texas at Austin, 2003B.S., Civil Engineering, University of Virginia, 2001

Jon Goodall is a Professor in the Department of Civil and Environmental Engineering at the University of Virginia (UVA) and Director of the UVA Engineering Link Lab. He is a water resources engineer working to advance the use of data and computational sciences to improve the understanding, forecasting, and management of water systems. Much of his current work focuses on adapting techniques from cyber-physical systems for real-time flood mitigation in coastal urban communities experiencing sea level rise impacts. Professor Goodall leads the Hydroinformatics Research Group housed in the Link Lab, co-directs the UVA Public Interest Technology University Network (PIT-UN), and is a member of UVA's Environmental Resilience Institute and Center for Transportation Studies. He is a registered Professional Engineer (PE), a Fellow of the American Society of Civil Engineers, and an elected member of the Virginia Academy of Science, Engineering, and Medicine. He earned his Ph.D. and M.S. in Civil Engineering from the University of Texas at Austin and his B.S. in Civil Engineering from the University of Virginia.


  • Elected Member, Virginia Academy of Science, Engineering, and Medicine 2021
  • Elected Fellow, American Society of Civil Engineering 2018
  • Early Career Research Excellence Award, International Environmental Modelling & Software Society (iEMSs) 2012
  • CAREER Award, National Science Foundation 2009

Research Interests

  • Water Resources Engineering
  • Hydroinformatics
  • Stormwater
  • Flooding
  • Smart Cities
  • GIS

Selected Publications

  • pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms ABS Rimer, S.P., Mullapudi, A., Troutman, S.C., Ewing, G., Bowes, B.D., Akin, A.A., Sadler, J., Kertesz, R., McDonnell, B., Montestruque, L. and Hathaway, J., 2023. Environmental Modelling & Software, p.105635.
  • Predicting combined tidal and pluvial flood inundation using a machine learning surrogate model ABS Zahura, F.T. and Goodall, J.L., 2022. Journal of Hydrology: Regional Studies, 41, p.101087.
  • Flood resilience through crowdsourced rainfall data collection: Growing engagement faces non-uniform spatial adoption ABS Chen, A.B., Goodall, J.L., Chen, T.D. and Zhang, Z., 2022. Journal of Hydrology, 609, p.127724.
  • Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality. ABS Bowes, B.D., Wang, C., Ercan, M.B., Culver, T.B., Beling, P.A. and Goodall, J.L., 2022. Environmental Science: Water Research & Technology, 8(10), pp.2065-2086.
  • Data-Driven Model Predictive Control For Real-Time Stormwater Management. ABS Ning, J., Bowes, B.D., Goodall, J.L. and Behl, M., 2022, June. In 2022 American Control Conference (ACC) (pp. 1438-1443). IEEE.

Courses Taught

  • CE 6230 Hydrology
  • CE 3220 Water Resources Engineering
  • CE 6500 Hydroinformatics