Commentaries

  • 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, 1996–2000. https://doi.org/10.1002/hyp.13723 (Invited Commentary)

Position Papers

  • 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  

Editorials

Book Reviews

  • Nabavi, E., & Razavi, S. (2023). The responsibility turn: Lessons from the COVID-19 pandemic inspire a guide to recognizing the politics of modeling. Science, 382(6672), 775–775. https://doi.org/10.1126/science.adl3473

Career Commentaries

  • Razavi, S. (2020). Celebrate diversity, embrace equity and cultivate inclusion. University Affairs. 
  • Sheikholeslami, R. & Razavi, S. (2018). Avoiding the Guise of an Anonymous Review, Eos, 99.

Publications by Year

  • 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

  • Triana, J. S. A., Ajami, H., & Razavi, S. (2025). The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales. Journal of Hydrology, 650, 132482. https://doi.org/10.1016/j.jhydrol.2024.132482

  • 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
  • Wheater, H. S., Pomeroy, J. W., Pietroniro, A., Davison, B., Elshamy, M., Yassin, F., Rokaya, P., Fayad, A., Tesemma, Z., Princz, D., Loukili, Y., DeBeer, C. M., Ireson, A. M., Razavi, S., Lindenschmidt, K.-E., Elshorbagy, A., MacDonald, M., Abdelhamed, M., Haghnegahdar, A., & Bahrami, A. (2022). Advances in modelling large river basins in cold regions with Modélisation Environmentale Communautaire—Surface and Hydrology (MESH), the Canadian hydrological land surface scheme. Hydrological Processes, 36(4), e14557. https://doi.org/10.1002/hyp.14557

  • Khoshnood Motlagh, S., Sadoddin, A., Haghnegahdar, A., Razavi, S., Salmanmahiny, A., & Ghorbani, K. (2021). Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran. Land Degradation & Development, 32(10), 3092–3105. https://doi.org/10.1002/ldr.3969

  • Maier, H. R., Taghikhah, F. R., Nabavi, E., Razavi, S., Gupta, H., Wu, W., Radford, D. A. G., & Huang, J. (2024). How much X is in XAI: Responsible use of “Explainable” artificial intelligence in hydrology and water resources. Journal of Hydrology X, 25, 100185. https://doi.org/10.1016/j.hydroa.2024.100185

  • Abdelhamed, M. S., Razavi, S., Elshamy, M. E., & Wheater, H. S. (2024). Assessment of a hydrologic-land surface model to simulate thermo-hydrologic evolution of permafrost regions. Journal of Hydrology, 645, 132161. https://doi.org/10.1016/j.jhydrol.2024.132161

  • Li, K., & Razavi, S. (2024). What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach. Journal of Hydrology, 131835. https://doi.org/10.1016/j.jhydrol.2024.131835  

  • Arheimer, B., Cudennec, C., and many others including Harvey, N. & Razavi, S. (2024). The IAHS Science for Solutions decade, with Hydrology Engaging Local People IN a Global world (HELPING). Hydrological Sciences Journal, 69(11), 1417–1435. https://doi.org/10.1080/02626667.2024.2355202 
  • Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Razavi, S., & Choi, S. M. (2024). Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling. International Journal of Digital Earth, 17(1), 2311325. https://doi.org/10.1080/17538947.2024.2311325 
  • Harvey, N., Razavi, S., & Bilish, S. (2024). Review of hydrological modelling in the Australian Alps: from rainfall-runoff to physically based models. Australasian Journal of Water Resources, 28(2), 208–224. https://doi.org/10.1080/13241583.2024.2343453 
  • Meles, M. B., Goodrich, D. C., Unkrich, C. L., Gupta, H. V., Burns, I. S., Hirpa, F. A., Razavi, S., & Guertin, D. P. (2024). Rainfall distributional properties control hydrologic model parameter importance. Journal of Hydrology: Regional Studies, 51, 101662. https://doi.org/10.1016/j.ejrh.2024.101662 
  • Saed, B., Elshorbagy, A., & Razavi, S. (2024). Quantifying interactions in the water-energy-food nexus: data-driven analysis utilizing a causal inference method. Frontiers in Environmental Science, 11, 1328009. https://doi.org/10.3389/fenvs.2023.1328009

  • Nabavi, E., & Razavi, S. (2023). The responsibility turn: Lessons from the COVID-19 pandemic inspire a guide to recognizing the politics of modeling. Science, 382(6672), 775. https://doi.org/10.1126/science.adl3473
  • Elrashidy, M. T., Ireson, A., & Razavi, S. (2023). On the optimal level of complexity for the representation of wetland systems in land surface models. Hydrology and Earth System Sciences, 27(24), 4595-4608. https://doi.org/10.5194/hess-27-4595-2023
  • Maier, H. R., Zheng, F., Gupta, H., Chen, J., Mai, J., Savic, D., Loritz, R., Wu, W., Guo, D., Bennett, A., Jakeman, A., Razavi, S., and Zhao, J. (2023). On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization. Environmental Modelling & Software, 167, 105779. 
  • Khalili, P., Razavi, S., Davies, E. G., Alessi, D. S., & Faramarzi, M. (2023). Assessment of blue water-green water interchange under extreme warm and dry events across different ecohydrological regions of western Canada. Journal of Hydrology, 130105. 
  • Maier, H. R., Galelli, S., Razavi, S., Castelletti, A., Rizzoli, A., Athanasiadis, I. N., ... & Humphrey, G. B. (2023). Exploding the myths: An introduction to artificial neural networks for prediction and forecasting. Environmental Modelling & Software, 105776. 
  • Wu, W., Eamen, L., Dandy, G., Razavi, S., Kuczera, G., & Maier, H. R. (2023). Beyond engineering: A review of reservoir management through the lens of wickedness, competing objectives and uncertainty. Environmental Modelling & Software, 105777. 
  • Ahmadianfar, I., Samadi-Koucheksaraee, A., & Razavi, S. (2023). Design of optimal operating rule curves for hydropower multi-reservoir systems by an influential optimization method. Renewable Energy. (211) 508-521. 
  • Kreibich, H., Schröter, K., Di Baldassarre, G., and many co-authors including Razavi S. (2023). Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts. Earth System Science Data, 15(5), 2009-2023. 
  • Eamen, L., Brouwer, R., & Razavi, S. (2023). Testing the Performance of Hydro-Economic Supply-side Input-Output Models under Different Water Availability and Economic Conditions in a Transboundary River Basin, Water Resources and Economics, 9(1).
  • Abdelhamed, M. S., Elshamy, M. E., Razavi, S., & Wheater, H. S. (2023). Challenges in Hydrologic‐Land Surface Modeling of Permafrost Signatures—A Canadian Perspective. Journal of Advances in Modeling Earth Systems, 15(3), e2022MS003013. 
  • Ghoreishi, M., Elshorbagy, A., Razavi, S., Blöschl, G., Sivapalan, M., & Abdelkader, A. (2023). Cooperation in a transboundary river basin: a large-scale socio-hydrological model of the Eastern Nile. Hydrology and Earth System Sciences, 27(5), 1201-1219. 
  • Sedighkia, M., Datta, B., & Razavi, S. (2023). Optimizing agricultural cropping patterns under irrigation water use restrictions due to environmental flow requirements and climate change. Water Resources and Economics, (41)100216.

  • 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)

 

  • Sedighkia, M., Fathi, Z., Razavi, S., & Abdoli, A. (2022). Optimal agricultural plan for minimizing ecological impacts on river ecosystems. Irrigation Science, 41 (1), 93-106. https://doi.org/10.1007/s00271-022-00834-7 

 

  • Sedighkia, M., Datta, B., & Razavi, S. (2022). A simulation-optimization framework for reducing thermal pollution downstream of reservoirs. Water Quality Research Journal, 57 (4), 291-303. https://doi.org/10.2166/wqrj.2022.018 

 

  • Li, K., Huang, G., Wang, S., & Razavi, S. (2022). Development of A Physics-Informed Data-Driven Model for Gaining Insights into Hydrological Processes in Irrigated Watersheds. Journal of Hydrology, (613), 128323. https://doi.org/10.1016/j.jhydrol.2022.128323 

 

  • Kreibich, H., Van Loon, A. F., and many co-authors including Razavi, S. (2022). The challenge of unprecedented floods and droughts in risk management. Nature, 608, 80–86. https://doi.org/10.1038/s41586-022-04917-5 
  • Li, K., Huang, G., Wang, S., Razavi, S., & Zhang, X. (2022). Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions. Water Resources Research, 58(6), e2021WR031557. https://doi.org/10.1029/2021WR031557 
  • Dezfuli, A., Razavi, S., & Zaitchik, B. F. (2022). Compound effects of climate change on future transboundary water issues in the Middle East. Earth's Future, 10(4). https://doi.org/10.1029/2022EF002683 
  • Mohammadlou, M., Bahremand, A., Princz, D., Kinar, N., Haghnegahdar, A., & Razavi, S. (2022). Objective evaluation of the Global Environmental Multiscale Model (GEM) with precipitation and temperature for Iran. Natural Resource Modeling, 35(3), e12343. https://doi.org/10.1111/nrm.12343 
  • Eamen, L., Brouwer, R., & Razavi, S. (2022). Comparing the applicability of hydro-economic modelling approaches for large-scale decision-making in multi-sectoral and multi-regional river basins. Environmental Modelling & Software, 152, 105385. https://doi.org/10.1016/j.envsoft.2022.105385 
  • Wheater, H., Pomeroy, J., Pietroniro, A., … Razavi, S., …, & Bahrami, A. (2022). Advances in modelling large river basins in cold regions with Modélisation Environmentale Communautaire – Surface and Hydrology (MESH), the Canadian hydrological land surface scheme. Hydrological Processes, 36(4), e14557. https://doi.org/10.1002/hyp.14557 
  • Abdelhamed, M., Elshamy, M., Wheater, H., & Razavi, S. (2022). Hydrologic-land surface modelling of the Canadian sporadic-discontinuous permafrost: initialization and uncertainty propagation. Hydrological Processes, 36(3). https://doi.org/10.1002/hyp.14509 

  • 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). Breaking through language barriers. Science, 371(6525), 206. https://doi.org/10.1126/science.371.6525.206

  • 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 
  • Eamen, L., Brouwer, R., & Razavi, S. (2021). Integrated modelling to assess the impacts of water stress in a transboundary river basin: Bridging local-scale water resource operations to a river basin economy. Science of The Total Environment, 800, 149543. https://doi.org/10.1016/j.scitotenv.2021.149543
  • Saltelli, A., Jakeman, A., Razavi, S., & Wu, Q. (2021). Sensitivity analysis: A discipline coming of age. Environmental Modelling & Software, 146, 105226. https://doi.org/10.1016/j.envsoft.2021.105226
  • Ghoreishi, M., Sheikholeslami, R., Elshorbagy, A., Razavi, S., & Belcher, K. (2021). Peering into agricultural rebound phenomenon using a global sensitivity analysis approach. Journal of Hydrology, 602, 126739. https://doi.org/10.1016/j.jhydrol.2021.126739
  • Khoshnood Motlagh, S., Sadoddin, A., Haghnegahdar, A., Razavi, S., Salmanmahiny, A., & Ghorbani, K. (2021). Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran. Land Degradation & Development, 32(10), 3092–3105. https://doi.org/10.1002/ldr.3969
  • Meles, M. B., Goodrich, D. C., Gupta, H. V., Shea Burns, I., Unkrich, C. L., Razavi, S., & Guertin, D. P. (2021). Multi-criteria, time dependent sensitivity analysis of an event-oriented, physically-based, distributed sediment and runoff model. Journal of Hydrology, 598, 126268. https://doi.org/10.1016/j.jhydrol.2021.126268
  • DeBeer, C. M., Wheater, H. S., Pomeroy, J. W., Barr, A. G., Baltzer, J. L., Johnstone, J. F., Turetsky, M. R., Stewart, R. E., Hayashi, M., van der Kamp, G., Marshall, S., Campbell, E., Marsh, P., Carey, S. K., Quinton, W. L., Li, Y., Razavi, S., Berg, A., McDonnell, J. J., … Pietroniro, A. (2021). Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology. Hydrology and Earth System Sciences, 25(4), 1849–1882. https://doi.org/10.5194/hess-25-1849-2021
  • Mai, J., Tolson, B. A., Shen, H., Gaborit, É., Fortin, V., Gasset, N., Awoye, H., Stadnyk, T. A., Fry, L. M., Bradley, E. A., Seglenieks, F., Temgoua, A. G. T., Princz, D. G., Gharari, S., Haghnegahdar, A., Elshamy, M. E., Razavi, S., Gauch, M., Lin, J., … Pietroniro, A. (2021). Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E). Journal of Hydrologic Engineering, 26(9), 05021020. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002097
  • Ghoreishi, M., Razavi ,Saman, & and Elshorbagy, A. (2021). Understanding human adaptation to drought: Agent-based agricultural water demand modeling in the Bow River Basin, Canada. Hydrological Sciences Journal, 66(3), 389–407. https://doi.org/10.1080/02626667.2021.1873344
  • Bahrami, A., Goïta, K., Magagi, R., Davison, B., Razavi, S., Elshamy, M., & Princz, D. (2021). Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model. Journal of Hydrology, 597, 125744. https://doi.org/10.1016/j.jhydrol.2020.125744
  • Vali, M., Zare, M., & Razavi, S. (2021). Automatic clustering-based surrogate-assisted genetic algorithm for groundwater remediation system design. Journal of Hydrology, 598, 125752. https://doi.org/10.1016/j.jhydrol.2020.125752
  • Zaremehrjardy, M., Razavi, S., & Faramarzi, M. (2021). Assessment of the cascade of uncertainty in future snow depth projections across watersheds of mountainous, foothill, and plain areas in northern latitudes. Journal of Hydrology, 598, 125735. https://doi.org/10.1016/j.jhydrol.2020.125735

  • Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., Razavi, S., Tang, G., & Pomeroy, J. W. (2020). Assessment of extremes in global precipitation products: How reliable are they? Journal of Hydrometeorology, 21(12), 2855-2873. https://doi.org/10.1175/JHM-D-20-0040.1 
  • Sheikholeslami, R., & Razavi, S. (2020). A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data. Geophysical Research Letters, 47(20), e2020GL089829. https://doi.org/10.1029/2020GL089829 
  • Iwanaga, T., Wang, H. H., Hamilton, S. H., Grimm, V., Koralewski, T. E., Salado, A., Elsawah, S., Razavi, S., Yang, J., Glynn, P., Badham, J, Voinov, A., Chen, M., Grant, W. E., Peterson, T., Frank, K., Shenk, G., Barton, C. M., Jakeman, A. J., & Little, J. C. (2020). Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach. Environmental Modelling & Software, 135, 104885. https://doi.org/10.1016/j.envsoft.2020.104885 
  • Do, N., & Razavi, S. (2020). Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis. Water Resources Research, 56, e2019WR025436. https://doi.org/10.1029/2019WR025436  
  • Elshamy, M., Princz, D., Sapriza-Azuri, G., Pietroniro, A., Wheater, H., & Razavi, S. (2020). On the Configuration and Initialization of a Large Scale Hydrological Land Surface Model to Represent Permafrost. Hydrology and Earth System Sciences, 24, 349–379. https://doi.org/10.5194/hess-24-349-2020  
  • Slaughter, A., & Razavi, S. (2020). Paleo-hydrologic reconstruction of 400 years of past flows at a weekly time step for major rivers of Western Canada. Earth System Science Data, 12, 231–243. https://doi.org/10.5194/essd-12-231-2020  
  • Eamen, L., Brouwer, R., & Razavi, S. (2020). The economic impacts of water supply restrictions due to climate and policy change: A transboundary river basin supply-side input-output analysis. Ecological Economics, 172, 106532. https://doi.org/10.1016/j.ecolecon.2019.106532 

 

  • Razavi, S., Gober, P., Maier, H. R., 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)

  • Sheikholeslami, R., Razavi, S., Haghnegahdar, A., (2019), What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models, Geoscientific Model Development, 12, 4275–4296, https://doi.org/10.5194/gmd-12-4275-2019. 
  • Yassin, F., Razavi, S., Elshamy, M., Davison, B., and Wheater, H., (2019), Representation and improved parameterization of reservoir operation in hydrological and land-surface models, Hydrology and Earth System Sciences. 23, 3735–3764, https://doi.org/10.5194/hess-23-3735-2019. 
  • Guillaume, J., Jakeman, J., Marsili-Libelli, S., Asher, M., Brunner, P., Croke, B., Hill, M., Jakeman, A., Keesman, K., Razavi, S., and Stigter, J., (2019), Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose, Environmental Modelling & Software, Volume 119, Pages 418-432. https://doi.org/10.1016/j.envsoft.2019.07.007. 
  • Razavi, S., Gupta, H. V., (2019), A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models, Environmental Modelling & Software. 
  • Maier H.R., Razavi S., Kapelan, Z., Matott L.S., Kasprzyk J., and Tolson, B.A., (2019), Introductory Overview: Optimization using Evolutionary Algorithms and other Metaheuristics, Environmental Modelling & Software, Volume 114, Pages 1-11, https://doi.org/10.1016/j.envsoft.2018.12.002. 
  • Razavi, S., Sheikholeslami, R., Gupta, H. V., Haghnegahdar, A., (2019), VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis, Environmental Modelling & Software. Volume 112, Pages 95-107, https://doi.org/10.1016/j.envsoft.2018.10.005. 
  • Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., and Haghnegahdar, A., (2019), Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost, Environmental Modelling & Software, Volume 111, Pages 282-299, https://doi.org/10.1016/j.envsoft.2018.09.002.

  • Gupta, H.V., and Razavi, S., (2018), Revisiting the basis of sensitivity analysis for Dynamical Earth System Models, Water Resources Research, 54. Pages 8692-8717, https://doi.org/10.1029/2018WR022668. 
  • Gharari, S., and Razavi, S. (2018), A review and synthesis of hysteresis in hydrology and hydrological modeling: Memory, path-dependency, or missing physics?, Journal of Hydrology, Volume 566, Pages 500-519, https://doi.org/10.1016/j.jhydrol.2018.06.037. 
  • Sapriza-Azuri, G., Gamazo, P., Razavi, S., and Wheater, H. (2018), On the appropriate definition of soil profile configuration and initial conditions for land surface–hydrology models in cold regions, Hydrology and Earth System Sciences, 22, 3295-3309, https://doi.org/10.5194/hess-22-3295-2018. 
  • Asong, Z. E., Wheater, H. S., Bonsal, B., Razavi, S., & Kurkute, S., (2018), Historical drought patterns over Canada and their teleconnections with large-scale climate signals, Hydrology and Earth System Sciences, 22(6), 3105-3124, https://doi.org/10.5194/hess-22-3105-2018. 
  • Razavi, S., and Vogel, R., (2018), Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales. Journal of Hydrology, Volume 557, Pages 109-115, https://doi.org/10.1016/j.jhydrol.2017.11.053.

  • Farjad, B., Gupta, A., Razavi, S., Faramarzi, M., & Marceau, D. J. (2017). An Integrated Modelling System to Predict Hydrological Processes under Climate and Land-Use/Cover Change Scenarios. Water, 9(10), 767. 
  • Haghnegahdar, A., Razavi, S., Yassin, F., & Wheater, H. (2017). Multicriteria sensitivity analysis as a diagnostic tool for understanding model behaviour and characterizing model uncertainty. Hydrological Processes, 31(25), 4462-4476. 
  • Yassin, F., Razavi, S., Wheater, H., Sapriza-Azuri, G., Davison, B., and Pietroniro, A., (2017) Enhanced Identification of a Hydrologic Model using Streamflow and Satellite Water Storage Data: A Multi-criteria Sensitivity Analysis and Optimization Approach, Hydrological Processes. 31:3320–3333. 
  • Sheikholeslami, R., Yassin, F., Lindenschmidt, K., and Razavi, S., (2017), Improved Understanding of River Ice Processes Using Global Sensitivity Analysis Approaches, ASCE Journal of Hydrologic Engineering. 
  • Haghnegahdar, A., and Razavi, S., (2017) Insights into Sensitivity Analysis of Earth and Environmental Systems Models: On the Impacts of Parameter Perturbation Scale, Environmental Modelling and Software. 
  • Wong, J, Razavi, S., Bonsal, B., Wheater, H., and Asong E., (2017), Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada, Hydrology and Earth System Sciences (HESS). 
  • Sheikholeslami, R., and Razavi, S., (2017), Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models, Environmental Modelling & Software, 93: 109–126 doi: 10.1016/j.envsoft.2017.03.010. 
  • Asong, Z. E., Razavi, S., Wheater, H. S., and Wong, J. S., (2017), Evaluation of integrated multisatellite retrievals for GPM (IMERG) over southern Canada against ground precipitation observations: A preliminary assessment, Journal of Hydrometeorology, 18(4), 1033-1050.

  • Elshorbagy, A., Wagener, T., Razavi, S., and Sauchyn, D., (2016), Correlation and causation in tree-ring-based reconstruction of paleohydrology in cold semiarid regions, Water Resources Research, 52 doi:10.1002/2016WR018985. 
  • Razavi, S., and Gupta, H. V., (2016), A new framework for comprehensive, robust, and efficient global sensitivity analysis: I. Theory, Water Resources Research, 51, doi:10.1002/2015WR017558. 
  • Razavi, S., and Gupta, H. V., (2016), A new framework for comprehensive, robust, and efficient global sensitivity analysis: II. Application, Water Resources Research, 51, doi:10.1002/2015WR017559. 
  • Razavi, S., Elshorbagy, A., Wheater, H. and Sauchyn, D., (2016). Time scale effect and uncertainty in reconstruction of Paleo‐hydrology. Hydrological Processes, doi: 10.1002/hyp.10754.

  • Razavi, S., and Gupta, H. V., (2015), What do we mean by sensitivity analysis? The need for comprehensive characterization of ‘‘global’’ sensitivity in Earth and Environmental systems models, Water Resources Research, 51(5): pp.3070–3092., doi:10.1002/2014WR016527. 
  • Razavi, S., Elshorbagy, A., Wheater, H., and Sauchyn, D., (2015), Toward understanding nonstationarity in climate and hydrology through tree ring proxy records, Water Resources Research, 51(3): pp.1813–1830. 
  • Asadzadeh, Razavi, S., Tolson, B. A., and Fay, D., (2014), Pre-emption strategies for efficient multi-objective optimization: application to the development of Lake Superior regulation plan, Environmental Modelling and Software, 54: pp. 128–141. 
  • Razavi, S., Asadzadeh, M., Tolson, B. A., Fay, D., Moin, S., Bruxer, J., and Fan, Y., (2014), Evaluation of new control structures for regulating the Great Lakes system: a multi-scenario, multi-reservoir optimization approach, Journal of Water Resources Planning and Management. 140(8), 14 pages. 
  • Razavi, S., and Tolson, B. A., (2013), An efficient framework for hydrologic model calibration on long data periods, Water Resources Research, 49(12): pp. 8418–8431. 
  • Razavi, S., Tolson, B. A., and Burn D. H., (2012), Review of surrogate modelling in water resources, Water Resources Research,48, W07401, doi:10.1029/2011WR011527. 32 pages. (Received WRR Editors’ Choice Award, Selected as an AGU Research Spotlight). 
  • Razavi, S., Tolson, B. A., and Burn, D. H., (2012), Numerical assessment of metamodelling strategies in computationally intensive optimization, Environmental Modelling & Software, 34, pp. 67-86. 
  • Razavi, S., and Tolson, B. A., (2011), A new formulation for feedforward neural networks, IEEE Transactions on Neural Networks, 22(10), pp. 1588-1598. 
  • Razavi, S., Tolson, B. A., Matott, L. S., Thomson, N. R., MacLean, A., and Seglenieks, F. R., (2010), Reducing the computational cost of automatic calibration through model preemption, Water Resources Research. 46(11):17 pages. 
  • Razavi, S., and Araghinejad, S., (2009), Reservoir inflow modeling using temporal neural networks with forgetting factor approach, Water Resources Management, 23(1): pp. 39-55. 
  • Karamouz, M., Razavi, S., and Araghinejad, S., (2008), Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study. Hydrological Processes, 22(2): pp. 229–241. 
  • Razavi, S., and Karamouz, M., (2007), Adaptive neural networks for flood routing in river systems, Water International, 32(3), pp. 360-375.