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School of Environment and Sustainability

Research Area(s)

  • Developing integrated frameworks and digital tools to assess and manage climate-related risks, with a focus on enhancing the resilience of water systems and critical infrastructure.
  • Investigating weather- and water-related hazards—floods, droughts, wildfires, heatwaves—and their interactions with communities and the built environment, including their compound and cascading effects across multiple scales.
  • Advancing convergent research that unites engineering, hydrology, social sciences, economics, and Indigenous knowledge to address complex human-environment systems.
  • Improving physically based models with novel parameterizations and leveraging remote sensing and high-resolution data to improve fine-scale prediction of hydroclimatic variables and extremes.
  • Applying advanced sensitivity analysis (e.g., VARS), uncertainty quantification, and optimization to support infrastructure design, risk mitigation, policy development, and long-term planning.
  • Exploring responsible, interpretable, and robust AI/ML methods for predictive modelling, digital twins, and decision-making in infrastructure and environmental systems.
  • Collaborating with Indigenous and local communities to braid academic science with place-based knowledge for inclusive resilience planning and disaster risk reduction.

Selected Awards and Honours

2024  GN Alexander Medal for Water Engineering, Engineers Australia

2024  Walter L. Huber Civil Engineering Research Prize, American Society of Civil Engineers (ASCE)

2021  Certificate of Excellent Editorial Board Member, Elsevier - Environmental Modelling & Software (EMS)

2020  Early Career Research Excellence (ECRE) Award (biennial), International Environmental Modelling and Software Society

2020  IAS Vanguard Fellowship, University of Birmingham, UK

2020  Bristol Benjamin Meaker Distinguished Visiting Professor, University of Bristol, UK

2019  Young Scientist Award, Canadian Geophysical Union (CGU). 

Academic Credentials

  • Doctor of Philosophy, Civil and Environmental Engineering, University of Waterloo
  • Master of Science, Civil and Environmental Engineering, Amirkabir University, Iran
  • Bachelor of Science,Civil Engineering, Iran University of Science and Technology, Iran

Selected Publications

Razavi, S., Duffy, A., Eamen, L., Jakeman, A. J., Jardine, T. D., Wheater, H., Hunt, R. J., Maier, H. R., Abdelhamed, M. S., Ghoreishi, M., Gupta, H., Döll, P., Moallemi, E. A., Yassin, F., Strickert, G., Nabavi, E., Mai, J., Li, Y., Thériault, J. M., Wu, W., Pomeroy, J., Clark, M. P., Ferguson, G., Gober, P., Cai, X., Reed, M. G., Saltelli, A., Elshorbagy, A., Sedighkia, M., Terry, J., Lindenschmidt, K.-E., Hannah, D. M., Li, K., Asadzadeh, M., Harvey, N., Moradkhani, H., & Grimm, V. (2025). Convergent and transdisciplinary integration: On the future of integrated modeling of human-water systems. Water Resources Research, 61, e2024WR038088. https://doi.org/10.1029/2024WR038088 

Razavi, S., Hannah, D., Elshorbagy, A., Kumar, S., Marshall, L., Solomatine, D., Dezfuli, A., Sadegh, M., & Famiglietti, J. (2022). Coevolution of Machine Learning and Process-based Modelling to Revolutionize Earth and Environmental Sciences: A Perspective. Hydrological Processes, 36(6), e14596. https://doi.org/10.1002/hyp.14596 (Invited Commentary)

Razavi, S., Gober, P., Maier, H., Brouwer, R., & Wheater, H. (2020). Anthropocene Flooding: Challenges for Science and Society. Hydrological Processes, 34(8), 1996–2000. https://doi.org/10.1002/hyp.13723  (Invited Commentary)

Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S., & Maier, H. R. (2021). The Future of Sensitivity Analysis: An Essential Discipline for Systems Modeling and Policy Support. Environmental Modelling and Software, 137, 104954. https://doi.org/10.1016/j.envsoft.2020.104954

Razavi, S. (2021). Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environmental Modelling and Software, 144, 105159. https://doi.org/10.1016/j.envsoft.2021.105159  

Wu, W., Eamen, L., Dandy, G., Maier, H. R., Razavi, S., Kwakkel, J., Huang, J., & Kuczera, G. (2025). Beyond the traditional paradigm of water resources management: Scenario thinking to address deep uncertainty. Journal of Hydrology, 661, 133547. https://doi.org/10.1016/j.jhydrol.2025.133547

Panigrahi, B., Razavi, S., Doig, L. E., Cordell, B., Gupta, H. V., & Liber, K. (2025). On robustness of the explanatory power of machine learning models: Insights from a new explainable AI approach using sensitivity Analysis. Water Resources Research, 61(3), e2024WR037398. https://doi.org/10.1029/2024WR037398

Razavi, S., & Gupta, H. V. (2016). A new framework for comprehensive, robust, and efficient global sensitivity analysis: I. Theory. Water Resources Research, 52(1), 423–439. https://doi.org/10.1002/2015WR017558

Razavi, S., Tolson, B. A., & Burn, D. H. (2012). Review of surrogate modeling in water resources. Water Resources Research, 48(7). https://doi.org/10.1029/2011WR011527 (Received the 2012 Water Resources Research Journal’s Editors’ Choice Award, Selected as an AGU Research Spotlight)