Articles | Volume 21, issue 11
https://doi.org/10.5194/hess-21-5663-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5663-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
15 Nov 2017
Research article |
| 15 Nov 2017
Identifying the connective strength between model parameters and performance criteria
Christian Albrechts University of Kiel, Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany
GFZ German Research Centre for Geosciences, Section 5.4 Hydrology, Potsdam, Germany
Matthias Pfannerstill
Christian Albrechts University of Kiel, Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany
Abror Gafurov
GFZ German Research Centre for Geosciences, Section 5.4 Hydrology, Potsdam, Germany
Jens Kiesel
Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
Christian Albrechts University of Kiel, Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany
Christian Lehr
Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Landscape Hydrology, Müncheberg, Germany
University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany
Nicola Fohrer
Christian Albrechts University of Kiel, Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany
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- Using numerical modeling error analysis methods to indicate changes in a watershed K. Mátyás & K. Bene 10.1556/606.2018.13.3.17
- How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures T. Pilz et al. 10.1029/2020WR028042
- A review of alternative climate products for SWAT modelling: Sources, assessment and future directions M. Tan et al. 10.1016/j.scitotenv.2021.148915
- When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species? J. Kiesel et al. 10.1002/eco.2193
- A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
- Improving structure identifiability of hydrological processes by temporal sensitivity with a flexible modeling framework L. Zhou et al. 10.1016/j.jhydrol.2022.128843
- Evaluation of overland flow modelling hypotheses with a multi‐objective calibration using discharge and sediment data A. de Lavenne et al. 10.1002/hyp.14767
- Variable Infiltration-Capacity Model Sensitivity, Parameter Uncertainty, and Data Augmentation for the Diyala River Basin in Iraq S. Waheed et al. 10.1061/(ASCE)HE.1943-5584.0001975
- To what extent does river routing matter in hydrological modeling? N. Cortés-Salazar et al. 10.5194/hess-27-3505-2023
- A multi-hydrological model ensemble prediction uncertainty estimation (e-PRUNE) framework C. Onyutha 10.2166/nh.2025.116
- Assessing the impact of a multimetric calibration procedure on modelling performance in a headwater catchment in Mau Forest, Kenya A. Kamamia et al. 10.1016/j.ejrh.2018.12.005
- Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall–runoff models E. Muñoz-Castro et al. 10.1080/02626667.2023.2231434
- Assessing parameter identifiability for multiple performance criteria to constrain model parameters B. Guse et al. 10.1080/02626667.2020.1734204
- Responses of hydrological model equifinality, uncertainty, and performance to multi-objective parameter calibration Y. Her & C. Seong 10.2166/hydro.2018.108
- Survey on the resolution and accuracy of input data validity for SWAT-based hydrological models N. Rasheed et al. 10.1016/j.heliyon.2024.e38348
- Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran M. Khoshchehreh et al. 10.1007/s12040-020-01462-5
- Do model parameters change under changing climate and land use in the upstream of the Lancang River Basin, China? D. Ma et al. 10.1080/02626667.2020.1782915
- Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria B. Guse et al. 10.1029/2019WR025605
- Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin J. Towner et al. 10.5194/hess-23-3057-2019
- Flood hydrograph prediction in a semiarid mountain catchment: The role of catchment subdivision H. Rezaei‐Sadr 10.1111/jfr3.12568
- A Review of the Application of the Soil and Water Assessment Tool (SWAT) in Karst Watersheds I. Al Khoury et al. 10.3390/w15050954
- Spatio-Temporal Dynamics of the Lanalhue Lake Basin in South-Central Chile L. Rodríguez-López et al. 10.3390/w17081114
- Analysis of spatial distribution of precipitation using hydrological modeling of watersheds (Case Study: Dez Dam Watershed) A. Gorjizade & A. Shahbazi 10.1007/s12145-024-01612-z
Latest update: 11 Jul 2025
Short summary
Performance measures are used to evaluate the representation of hydrological processes in parameters of hydrological models. In this study, we investigated how strongly model parameters and performance measures are connected. It was found that relationships are different for varying flow conditions, indicating that precise parameter identification requires multiple performance measures. The suggested approach contributes to a better handling of parameters in hydrological modelling.
Performance measures are used to evaluate the representation of hydrological processes in...
