Articles | Volume 24, issue 3
https://doi.org/10.5194/hess-24-1347-2020
https://doi.org/10.5194/hess-24-1347-2020
Research article
 | 
23 Mar 2020
Research article |  | 23 Mar 2020

Dynamics of hydrological-model parameters: mechanisms, problems and solutions

Tian Lan, Kairong Lin, Chong-Yu Xu, Xuezhi Tan, and Xiaohong Chen

Related authors

A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics
Tian Lan, Kairong Lin, Chong-Yu Xu, Zhiyong Liu, and Huayang Cai
Hydrol. Earth Syst. Sci., 24, 5859–5874, https://doi.org/10.5194/hess-24-5859-2020,https://doi.org/10.5194/hess-24-5859-2020, 2020
Dynamics of hydrological model parameters: calibration and reliability
Tian Lan, Kairong Lin, Xuezhi Tan, Chong-Yu Xu, and Xiaohong Chen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-301,https://doi.org/10.5194/hess-2019-301, 2019
Manuscript not accepted for further review
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024,https://doi.org/10.5194/hess-28-1373-2024, 2024
Short summary
Empirical stream thermal sensitivity cluster on the landscape according to geology and climate
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024,https://doi.org/10.5194/hess-28-1351-2024, 2024
Short summary
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024,https://doi.org/10.5194/hess-28-1191-2024, 2024
Short summary
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024,https://doi.org/10.5194/hess-28-1127-2024, 2024
Short summary
Toward interpretable LSTM-based modeling of hydrological systems
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024,https://doi.org/10.5194/hess-28-945-2024, 2024
Short summary

Cited articles

Aldrich, J.: R. A. Fisher and the making of maximum likelihood 1912–1922, Statist. Sci., 12, 162–176, https://doi.org/10.1214/ss/1030037906, 1997. 
Arora, S. and Singh, S.: The firefly optimization algorithm: convergence analysis and parameter selection, Int. J. Comput. Appl., 69, 48–52, https://doi.org/10.5120/11826-7528, 2013. 
Arsenault, R., Poulin, A., Côté, P., and Brissette, F.: Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration, J. Hydrol. Eng., 19, 1374–1384, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000938, 2014. 
Azad, S. K. J. S. and Optimization, M.: Monitored convergence curve: a new framework for metaheuristic structural optimization algorithms, Struct. Multidiscip. O., 60, 481–499, https://doi.org/10.1007/s00158-019-02219-5, 2019. 
Bárdossy, A.: Calibration of hydrological model parameters for ungauged catchments, Hydrol. Earth Syst. Sci., 11, 703–710, https://doi.org/10.5194/hess-11-703-2007, 2007. 
Download