Abstract
Manning’s roughness coefficient (\(n\)) is a comprehensive indicator of flow resistance, and significantly affects the accuracy of one-dimensional (1D) unsteady flow simulations. Most previous studies on roughness inversion have focused on the variation of the \(n\) values along the reach—the variations of \(n\) with the discharge or water stage have seldom been investigated. To address this issue, an optimization model based on an adaptive parallel genetic algorithm (APGA) is proposed. This model enables better estimations of \(n\) in 1D unsteady flow simulations by considering the effects of both distance and discharge on \(n\). The objective of the proposed model is to determine the optimal \(n\) values under different discharge strata for every sub-reach by minimizing the discrepancies between the simulated and measured water elevations and discharges. Moreover, a successive-approximation-based stepwise optimizing (SABSO) strategy is developed to improve the performance of the APGA-based optimization model in long natural rivers. The proposed model is evaluated through a case study on the upper reaches of the Yangtze River, China, and compared with models where the \(n\) values are considered to vary with distance or discharge. The results show that the APGA with the SABSO strategy yields better solutions than the APGA alone, and that the proposed model outperforms models that do not consider variations of \(n\) with both discharge and distance. This research provides a novel approach for the inverse estimation of roughness in long river flows.
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Acknowledgements
This work was a result of research supported in part by the National Natural Science Foundation of China (51679088), Project of National Key Research and Development Program of China (2016YFC0402308). We thank Stuart Jenkinson, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.
Funding
This work was a result of research supported in part by the National Natural Science Foundation of China (51679088), Project of National Key Research and Development Program of China (2016YFC0402308).
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All authors contributed to the study’s conception and design. Conceptualization, supervision, editing, and revision were contributed by Yang Peng. Model simulations, code implementation, and original draft writing were performed by Lishuang Yao. Programming and code testing were contributed by Xianliang Yu. Algorithms supporting and data analysis were performed by Zhihong Zhang. Data preparation and visualization were performed by Shiqi Luo. All authors commented on previous versions of the manuscript and approved the final manuscript.
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Yao, L., Peng, Y., Yu, X. et al. Optimal Inversion of Manning’s Roughness in Unsteady Open Flow Simulations Using Adaptive Parallel Genetic Algorithm. Water Resour Manage 37, 879–897 (2023). https://doi.org/10.1007/s11269-022-03411-x
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DOI: https://doi.org/10.1007/s11269-022-03411-x