Investigation of Unsteady Flow Dynamics in Rivers under the Influence of Cross-Sectional Uncertainty

Document Type : Research Paper

Authors

Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

10.22059/jwim.2026.405429.1273

Abstract

Rivers serve as a fundamental component of the hydrological cycle, exhibiting continuous and stable flow. However, river flow modeling is inherently associated with uncertainties in input data and geometric parameters, which make sensitivity analysis and the assessment of error sources a complex and challenging task. Among the most significant sources of uncertainty are measurement errors and inaccuracies in defining river cross-sections, which can directly influence the outcomes of hydrodynamic models. In this study, a Monte Carlo simulation framework was employed to investigate the propagation of uncertainty in hydraulic flow modeling. The uncertainties were analyzed through three case studies, including two real rivers and one hypothetical river. Simulation scenarios were constructed based on normal and uniform probability distributions, incorporating random errors of 10% and 20%, as well as systematic errors of 0% and ±3% in the cross-sectional data. The results demonstrated that random errors following a uniform distribution introduced the greatest variability in flow predictions, and an increase in the magnitude of geometric data errors directly led to higher variance in the model outputs. In contrast, the influence of systematic errors on the results was comparatively smaller, indicating the model’s greater sensitivity to stochastic variations in geometric input data. The findings of this study can contribute to enhancing the accuracy of hydrodynamic models and improving the interpretation of their results. Moreover, the outcomes provide practical implications for water resources management, hydraulic structure design, and risk-informed decision-making in the field of river engineering.

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References

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Water and Irrigation Management
Volume 16, Issue 1
July 2026
Pages 193-213

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