Journal
HYDROLOGY RESEARCH
Volume 48, Issue 1, Pages 17-27Publisher
IWA PUBLISHING
DOI: 10.2166/nh.2016.185
Keywords
flood routing; genetic algorithm; Muskingum model; parameter estimation; variable exponent
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Funding
- Hubei Support Plan of Science and Technology [2015BCA291]
- Wuhan Planning Project of Science and Technology [2014060101010062]
- Natural Science Foundation of China [51509099]
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First, a novel nonlinear Muskingum flood routing model with a variable exponent parameter and simultaneously considering the lateral flow along the river reach (named VEP-NLMM-L) was developed in this research. Then, an improved real-coded adaptive genetic algorithm (RAGA) with elite strategy was applied for precise parameter estimation of the proposed model. The problem was formulated as a mathematical optimization procedure to minimize the sum of the squared deviations (SSQ) between the observed and the estimated outflows. Finally, the VEP-NLMM-L was validated on three watersheds with different characteristics (Case 1 to 3). Comparisons of the optimal results for the three case studies by traditional Muskingum models and the VEP-NLMM-L show that the modified Muskingum model can produce the most accurate fit to outflow data. Application results in Case 3 also indicate that the VEP-NLMM-L may be suitable for solving river flood routing problems in both model calibration and prediction stages.
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