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
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025,https://doi.org/10.5194/hess-29-1703-2025, 2025
Short summary
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025,https://doi.org/10.5194/hess-29-1725-2025, 2025
Short summary
Extended-range forecasting of stream water temperature with deep-learning models
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025,https://doi.org/10.5194/hess-29-1685-2025, 2025
Short summary
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025,https://doi.org/10.5194/hess-29-1749-2025, 2025
Short summary
Projections of streamflow intermittence under climate change in European drying river networks
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025,https://doi.org/10.5194/hess-29-1615-2025, 2025
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
Share