Abstract
Physics-based modeling approaches are crucial for advancing our predictive capabilities of fluvial morphodynamics. Investigating the decadal to centennial-scale responses of river systems is essential for comprehending long-term geomorphic adjustments to climatic variations. However, the significant computational demands and prolonged processing times of the available models often restrict their application to shorter temporal scales. This paper evaluates two powerful techniques: morphological acceleration factor (morfac), which expedites bed evolution by scaling sediment transport rates, and employing condensed hydrograph inputs, which shorten flow time series by emphasizing dominant runoff events that govern geomorphic change. While both methods have historical foundations, their combined application and calibration for fluvial environments, to reduce computational demand, remain underexplored. The results highlighted the capabilities of morfac technique, with values up to 20, to enhance model efficiency, while maintaining robust performance, whereas values exceeding 20 significantly reduce performance. Furthermore, employing condensed hydrograph inputs provides additional enhancements in the model’s performance by incorporating only the most dominant runoff events in the shaping of riverbed evolution. Integrating these two techniques yields a theoretical computational efficiency that surpasses 98.8% reduction in total runtime. This study offers practical guidance for applying these methods in fluvial morphodynamic modeling, contributing to more feasible long-term simulations and advancing the operational utility of existing modeling frameworks.
Similar content being viewed by others
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Mohammadi, A., Alaghmand, S. & Mosaedi, A. Study and determination of morphological changes of Dough River in North of Iran using GIS. in Congress Int. Soc. Photogrammetry Remote Sens. 1217–1220 (2008).
Charlton, R. Fundamentals of Fluvial Geomorphology (Routledge, 2007).
Leopold, L. B., Wolman, M. G. & Miller, J. P. Fluvial Processes in Geomorphology (Dover Publications, 1992).
Church, M. & Ferguson, R. I. Morphodynamics: Rivers beyond steady state. Water Resour. Res. 51, 1883–1897 (2015).
Syvitski, J. P. M. et al. Morphodynamic models: an overview. in River, Coastal and Estuarine Morphodynamics. RCEM 3–20 (CRC Press London, Taylor & Francis Group, (2010).
Williams, R. D., Brasington, J. & Hicks, D. M. Numerical modelling of braided river morphodynamics: Review and future challenges. Geogr. Compass. 10, 102–127 (2016).
Papanicolaou, A. N., Elhakeem, M., Krallis, G., Prakash, S. & Edinger, J. Sediment transport modeling review—current and future developments. J. Hydraul. Eng. 134, 1–14 (2008).
McDonald, R. R., Nelson, J. M., Fosness, R. L. & Nelson, P. O. Field scale test of multi-dimensional flow and morphodynamic simulations used for restoration design analysis. (2016).
Sanyal, J. Predicting possible effects of dams on downstream river bed changes of a Himalayan river with morphodynamic modelling. Quat. Int. 453, 48–62 (2017).
Gelfenbaum, G. et al. Large-scale dam removal on the Elwha River, Washington, USA: Coastal geomorphic change. Geomorphology 246, 649–668 (2015).
van Oorschot, M., Kleinhans, M., Geerling, G. & Middelkoop, H. Distinct patterns of interaction between vegetation and morphodynamics. Earth Surf. Process. Landf. 41, 791–808 (2016).
Chen, J., Wang, Z., Li, M., Wei, T. & Chen, Z. Bedform characteristics during falling flood stage and morphodynamic interpretation of the middle–lower Changjiang (Yangtze) River channel, China. Geomorphology 147, 18–26 (2012).
Thuruthel, T. G. & Iida, F. Morphological computation and control complexity. in IOP Conference Series: Materials Science and Engineering vol. 1261 12011IOP Publishing, (2022).
Fathi, M. M., Liu, Z., Fernandes, A. M., Hren, M. T., Terry, D. O., Nataraj, C., & Smith, V. Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework. Environmental Modelling & Software, 185, 106307 (2025)
Hosseiny, H., Nazari, F., Smith, V. & Nataraj, C. A framework for modeling flood depth using a hybrid of hydraulics and machine learning. Sci. Rep. 10, 8222 (2020).
Lesser, G. R., Roelvink, J., van Kester, J. A. T. M. & Stelling, G. S. Development and validation of a three-dimensional morphological model. Coast. Eng. 51, 883–915 (2004).
Van der Wegen, M. & Roelvink, J. A. Long-term morphodynamic evolution of a tidal embayment using a two‐dimensional, process‐based model. J. Geophys. Res. Oceans https://doi.org/10.1029/2006JC003983 (2008).
Ranasinghe, R. et al. Morphodynamic upscaling with the MORFAC approach: Dependencies and sensitivities. Coast. Eng. 58, 806–811 (2011).
Roelvink, J. A. Coastal morphodynamic evolution techniques. Coast. Eng. 53, 277–287 (2006).
Pratama, F., Wulandari, S. & Rohmat, F. I. W. Modeling sediment accumulation in Pare Reservoir using HEC-RAS 2D: Assessing storage capacity over a 10-year period. Results Eng. 25, 104333 (2025).
Morgan, J. A. et al. The use of a morphological acceleration factor in the simulation of large-scale fluvial morphodynamics. Geomorphology 356, 107088 (2020).
Leonardi, N. & Plater, A. J. Residual flow patterns and morphological changes along a macro-and meso-tidal coastline. Adv. Water Resour. 109, 290–301 (2017).
Ranasinghe, R. et al. Morphodynamic upscaling with the MORFAC approach. (2010).
Williams, Measures, R., Hicks, D. & Brasington, J. Assessment of a numerical model to reproduce event-scale erosion and deposition distributions in a braided river. Water Resour. Res. 52, 6621–6642 (2016).
Kasprak, A., Brasington, J., Hafen, K., Williams, R. D. & Wheaton, J. M. Modelling braided river morphodynamics using a particle travel length framework. Earth Surf. Dyn. 7, 247–274 (2019).
Newell, E. & Maldonado, S. Acceleration of Morphodynamic Simulations Based on Local Trends in the Bed Evolution. J. Mar. Sci. Eng. 11, 2314 (2023).
Hoagland, S. W. H. et al. Advances in morphodynamic modeling of coastal barriers: A review. J. Waterw Port Coastal. Ocean. Eng. 149, 3123001 (2023).
Khaleghi, M. R., Gholami, V., Ghodusi, J. & Hosseini, H. Efficiency of the geomorphologic instantaneous unit hydrograph method in flood hydrograph simulation. Catena 87, 163–171 (2011).
Rowiński, P. & Czernuszenko, W. Experimental study of river turbulence under unsteady conditions. Acta Geophys. Pol. 46, 461–480 (1998).
Tabarestani, M. K. & Zarrati, A. R. Sediment transport during flood event: a review. Int. J. Environ. Sci. Technol. 12, 775–788 (2015).
Alvarez, A. Channel planform dynamics of an alluvial tropical river (Doctoral diss. (Texas A&M University, 2005).
Baker, V., Kochel, R. C. & Patton, P. C. Flood geomorphology. In Flood geomorphology (Wiley-Interscience, 1988).
Baker, V. R. Stream-channel response to floods, with examples from central Texas. Geol. Soc. Am. Bull. 88, 1057–1071 (1977).
Wolman, M. G. & Gerson, R. Relative scales of time and effectiveness of climate in watershed geomorphology. Earth Surf. Process. 3, 189–208 (1978).
Sherman, L. K. Streamflow from rainfall by the unit-graph method. Eng. News Rec. 108, 501–505 (1932).
Bayliss, A. C. & Jones, R. C. Peaks-over-Threshold Flood Database (Institute of Hydrology, 1993).
Costigan, K. H., Daniels, M. D., Perkin, J. S. & Gido, K. B. Longitudinal variability in hydraulic geometry and substrate characteristics of a Great Plains sand-bed river. Geomorphology 210, 48–58 (2014).
USGS. NED 1/3 Arc-Second N38w099 1 x 1 Degree IMG 2018 (U.S. Geological Survey, 2018).
Choné, G., Biron, P. M. & Buffin-Bélanger, T. EDP Sciences,. Flood hazard mapping techniques with LiDAR in the absence of river bathymetry data. in E3S Web of Conferences vol. 40 6005 (2018).
Wu, W., Wang, S. S. Y. & Jia, Y. Nonuniform sediment transport in alluvial rivers. J. Hydraul. Res. 38, 427–434 (2000).
Wu, W. & Lin, Q. Nonuniform sediment transport under non-breaking waves and currents. Coast. Eng. 90, 1–11 (2014).
Hunziker, R. P. & Jaeggi, M. N. R. Grain sorting processes. J. Hydraul. Eng. 128, 1060–1068 (2002).
Li, L. Delft University of Technology,. A fundamental study of the morphological acceleration factor. Civil Engineering and Geosciences. (2010). Retrieved from http://resolver.tudelft.nl/uuid: 2780f537-402b-427a-9147-b8652279a83e
Yassine, R., Cassan, L., Roux, H., Frysou, O. & Pérès, F. Numerical modelling of the evolution of a river reach with a complex morphology to help define future sustainable restoration decisions. Earth Surf. Dyn. 11, 1199–1221 (2023).
Sutherland, J., Peet, A. H. & Soulsby, R. Evaluating the performance of morphological models. Coast. Eng. 51, 917–939 (2004).
Stigler, S. M. The History of Statistics: The Measurement of Uncertainty before 1900 (Harvard University Press, 1990).
Fathi, M. M., Awadallah, A. G. & Aldahshoory, W. An improved monthly water balance GR2M model with a seasonally variable parameter. J. Hydrol. 617, 129127 (2023).
Mentaschi, L., Besio, G., Cassola, F. & Mazzino, A. Problems in RMSE-based wave model validations. Ocean Model. 72, 53–58 (2013).
Asuero, A. G., Sayago, A. & González, A. G. The correlation coefficient: An overview. Crit. Rev. Anal. Chem. 36, 41–59 (2006).
Shelley, J. E. Geomorphic Equations and Methods for Natural Channel Design, Doctoral dissertation, University of Kansas. at (2012).
Acknowledgements
This work was supported by National Science Foundation under Award No: 1844180. This work was also partially supported by the Villanova Center for Resilient Water Systems (VCRWS).
Funding
This work was supported by National Science Foundation under Award No: 1844180. This work was also partially supported by the Villanova Center for Resilient Water Systems (VCRWS).
Author information
Authors and Affiliations
Contributions
Conceptualization and Methodology: M.M.F. and V.S. Software and Writing - Original Draft: M.M.F. Validation: M.M.F., V.S., A.M.F., M.T.H., and D.O.T. Funding acquisition and Writing - Review & Editing: V.S., A.M.F., M.T.H., and D.O.T. Supervision: V.S.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Fathi, M.M., Smith, V., Fernandes, A.M. et al. Toward accelerating fluvial morphodynamic simulations through a speed accuracy trade-off assessment. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44428-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-44428-1


