Articles | Volume 20, issue 1
Hydrol. Earth Syst. Sci., 20, 375–392, 2016
https://doi.org/10.5194/hess-20-375-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue: Modeling hydrological processes and changes
Research article
21 Jan 2016
Research article
| 21 Jan 2016
Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization
Y. Chen et al.
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42 citations as recorded by crossref.
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- Calibration of Concentration Parameters Based on Frequency Decomposition of Fourier Series Q. Li et al. 10.1061/(ASCE)HE.1943-5584.0002008
- Improved vegetation parameterization for hydrological model and assessment of land cover change impacts on flow regime of the Upper Bhima basin, India M. Mohaideen & K. Varija 10.1007/s11600-018-0161-y
- A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions X. Wan et al. 10.1007/s11269-019-02351-3
- An improved calibration and uncertainty analysis approach using a multicriteria sequential algorithm for hydrological modeling H. Wu et al. 10.1038/s41598-021-96250-6
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- Discussion on the Key Problems of Yellow River Water Right Transfer between Two Cities W. Zhang et al. 10.4028/www.scientific.net/AMR.524-527.2717
- Improving the Xin'anjiang hydrological model based on mass–energy balance Y. Fang et al. 10.5194/hess-21-3359-2017
- Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China J. Yin et al. 10.5194/hess-21-183-2017
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- Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model S. Xu et al. 10.3390/w13030272
- Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India P. Yeditha et al. 10.2166/hydro.2021.067
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- Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion F. Ejaz et al. 10.1016/j.jhydrol.2021.127347
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- Evaluation of short-term streamflow prediction methods in Urban river basins X. Huang et al. 10.1016/j.pce.2021.103027
- Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting K. Liu et al. 10.3390/w13243483
- Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling J. Li et al. 10.1175/JHM-D-21-0088.1
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- Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model J. Li et al. 10.5194/hess-21-1279-2017
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Saved (preprint)
Latest update: 09 Aug 2022
Short summary
Parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. A method for parameter optimization with particle swam optimization (PSO) algorithm has been proposed for physically based distributed hydrological model in catchment flood forecasting and validated in southern China. It has found that the appropriate particle number and maximum evolution number of PSO algorithm are 20 and 30 respectively.
Parameter optimization is necessary to improve the flood forecasting capability of physically...
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