Commentaries

  • 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 (Invited Commentary). 
  • Razavi, S., Gober, P., Maier, H., Brouwer, R., and Wheater, H., (2020), Anthropocene Flooding: Challenges for Science and Society, Hydrological Processes, 34:1996–2000 (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. 
  • Razavi, S., (2021), Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling, Environmental Modelling and Software, 144, 105159.

Editorials

  • Razavi, S., Ames, D., & Chen, M. (2024), EnviroFutures: Envisioning the next century of environmental sciences, Environmental Modelling & Software, 171, 105880. 
  • Saltelli, A., Jakeman, A., Razavi, S., & Wu, Q. (2021), Sensitivity analysis: A discipline coming of age, Environmental Modelling & Software, 146, 105226.

Book Reviews

  • Nabavi, E., and 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.

Career Commentaries

  • Razavi, S., (2021), Breaking through language barriers, Science, 371(6525) 206, selected as a top essay in 2021. 
  • Razavi, S., (2020), Celebrate diversity, embrace equity and cultivate inclusion, University Affairs. 
  • Sheikholeslami, R. and Razavi, S., (2018), Avoiding the Guise of an Anonymous Review, Eos, 99.

Published in 2024

  • 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. 
  • Arheimer, B., Cudennec, C., and many others including Harvey, N. and 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. 
  • Yousefi, H., Ahani, A., Moridi, A., & Razavi, S. (2024). The future of droughts in Iran according to CMIP6 projections. Hydrological Sciences Journal, 69(7), 951–970. 
  • 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. 
  • 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, 1-17.
  • 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. 
  • 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.

Published in 2023

  • 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. 
  • 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.

Published in 2022

  • Sedighkia, M., Fathi, Z., Razavi, S., and Abdoli, A., (2022), Optimal agricultural plan for minimizing ecological impacts on river ecosystems, Irrigation Science. 41 (1), 93-106. 
  • 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. 
  • 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. 
  • 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. 
  • 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. 
  • 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). 
  • 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, e12343. 
  • 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. 
  • 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). 
  • 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).

Published in 2021

  • 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. 
  • 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, 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 semi‐arid river basin, Iran. Land Degradation & Development. doi.org/10.1002/ldr.3969. 
  • Meles, M., B., Goodrich, D. C., Gupta, H. V., Burns, S., Unkrich, C. L., Razavi, S., Guertin, D., P., (2021) Multi-Criteria and Time Dependent Sensitivity Analysis of an Event-Oriented and Physically-Based Distributed Sediment and Runoff Model, Journal of Hydrology. 598, 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., Spence, C., Helgason, W. D., Ireson, A. M., Black, T. A., Davison, B., Howard, A., Thériault, J. M., Shook, K., and 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. 
  • Mai, J. , B. A. Tolson, H. Shen, É. Gaborit, V. Fortin, N. Gasset, H. Awoye, T. A. Stadnyk, L. M. Fry, E. A. Bradley, F. Seglenieks, A. G. Temgoua, D. G. Princz, S. Gharari, A. Haghnegahdar, M. E. Elshamy, S. Razavi, M. Gauch, J. Lin, X. Ni, Y. Yuan, M. McLeod, N. Basu, R. Kumar, O. Rakovec, L. Samaniego, S. Attinger, N. K. Shrestha, P. Daggupati, T. Roy, S. Wi, T. Hunter, and J. R. Craig (2021): The Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E), Journal of Hydrologic Engineering. 26 (9), 05021020. 
  • Ghoreishi, M., Razavi, S., 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. 
  • 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. 
  • Vali, M., Zare, M., & Razavi, S. (2021). Automatic clustering-based surrogate-assisted genetic algorithm for groundwater remediation system design. Journal of Hydrology, 598, 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.

Published in 2020

  • 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. 
  • 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. 
  • 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., and Little, J., C., (2020). Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach. Environmental Modelling & Software, 135, 104885. 
  • Do, N., and 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., and 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., and 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, 17 pages, https://doi.org/10.1016/j.ecolecon.2019.106532.

Published in 2019

  • 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.

Published in 2018

  • 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.

Published in 2017

  • 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.

Published in 2016

  • 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.

Published in 2015 and before

  • 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.