Articles | Volume 20, issue 1
https://doi.org/10.5194/hess-20-375-2016
https://doi.org/10.5194/hess-20-375-2016
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, J. Li, and H. Xu

Related authors

Parameter dynamics of distributed hydrological model in simulating or forecasting flood processes of urbanizing watersheds
Yangbo Chen, Jun Liu, and Liming Dong
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-233,https://doi.org/10.5194/hess-2023-233, 2023
Manuscript not accepted for further review
Short summary
Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model
Ji Li, Daoxian Yuan, Jiao Liu, Yongjun Jiang, Yangbo Chen, Kuo Lin Hsu, and Soroosh Sorooshian
Hydrol. Earth Syst. Sci., 23, 1505–1532, https://doi.org/10.5194/hess-23-1505-2019,https://doi.org/10.5194/hess-23-1505-2019, 2019
Short summary
Impact of urbanization on flood of Shigu creek in Dongguan city
Luying Pan, Yangbo Chen, and Tao Zhang
Proc. IAHS, 379, 55–60, https://doi.org/10.5194/piahs-379-55-2018,https://doi.org/10.5194/piahs-379-55-2018, 2018
Short summary
Preface: Innovative Water Resources Management in a Changing Environment – Understanding and Balancing Interactions between Humankind and Nature
Zongxue Xu, Dingzhi Peng, Wenchao Sun, Bo Pang, Depeng Zuo, Andreas Schumann, and Yangbo Chen
Proc. IAHS, 379, 463–464, https://doi.org/10.5194/piahs-379-463-2018,https://doi.org/10.5194/piahs-379-463-2018, 2018
Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model
Ji Li, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao
Hydrol. Earth Syst. Sci., 21, 1279–1294, https://doi.org/10.5194/hess-21-1279-2017,https://doi.org/10.5194/hess-21-1279-2017, 2017
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Mathematical applications
A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, and Raphael J. M. Schneider
Hydrol. Earth Syst. Sci., 28, 2871–2893, https://doi.org/10.5194/hess-28-2871-2024,https://doi.org/10.5194/hess-28-2871-2024, 2024
Short summary
Inferring heavy tails of flood distributions through hydrograph recession analysis
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso
Hydrol. Earth Syst. Sci., 27, 4369–4384, https://doi.org/10.5194/hess-27-4369-2023,https://doi.org/10.5194/hess-27-4369-2023, 2023
Short summary
Landscape structures regulate the contrasting response of recession along rainfall amounts
Jun-Yi Lee, Ci-Jian Yang, Tsung-Ren Peng, Tsung-Yu Lee, and Jr-Chuan Huang
Hydrol. Earth Syst. Sci., 27, 4279–4294, https://doi.org/10.5194/hess-27-4279-2023,https://doi.org/10.5194/hess-27-4279-2023, 2023
Short summary
Hydrological objective functions and ensemble averaging with the Wasserstein distance
Jared C. Magyar and Malcolm Sambridge
Hydrol. Earth Syst. Sci., 27, 991–1010, https://doi.org/10.5194/hess-27-991-2023,https://doi.org/10.5194/hess-27-991-2023, 2023
Short summary
Spatial variability in Alpine reservoir regulation: deriving reservoir operations from streamflow using generalized additive models
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023,https://doi.org/10.5194/hess-27-673-2023, 2023
Short summary

Cited articles

Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J.: An Introduction to the European Hydrologic System-System Hydrologue Europeen, `SHE', a: History and Philosophy of a Physically-based, Distributed Modelling System, J. Hydrol., 87, 45–59, 1986a.
Abbott, M. B.,Bathurst, J. C.,Cunge, J. A.,O'Connell, P. E., and Rasmussen, J.: An Introduction to the European Hydrologic System-System Hydrologue Europeen, `SHE', b: Structure of a Physically based, distributed modeling System, J. Hydrol., 87, 61–77, 1986b.
Acharjee, P. and Goswami, S. K.: Chaotic particle swarm optimization based robust load flow, Int. J. Electr. Power Energ. Syst., 32, 141–146, 2010.
Ajami, N. K., Gupta, H., Wagener, T., and Sorooshian, S.: Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system, J. Hydrol., 298, 112–135, 2004.
Ambroise, B., Beven, K., and Freer, J.: Toward a generalization of the TOPMODEL concepts: Topographic indices of hydrologic similarity, Water Resour. Res., 32, 2135–2145, 1996.
Download
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.