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
Viewed
Total article views: 2,876 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,691 | 1,100 | 85 | 2,876 | 72 | 107 |
- HTML: 1,691
- PDF: 1,100
- XML: 85
- Total: 2,876
- BibTeX: 72
- EndNote: 107
Total article views: 1,955 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Nov 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,178 | 718 | 59 | 1,955 | 54 | 59 |
- HTML: 1,178
- PDF: 718
- XML: 59
- Total: 1,955
- BibTeX: 54
- EndNote: 59
Total article views: 921 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 513 | 382 | 26 | 921 | 18 | 48 |
- HTML: 513
- PDF: 382
- XML: 26
- Total: 921
- BibTeX: 18
- EndNote: 48
Viewed (geographical distribution)
Total article views: 2,876 (including HTML, PDF, and XML)
Thereof 2,809 with geography defined
and 67 with unknown origin.
Total article views: 1,955 (including HTML, PDF, and XML)
Thereof 1,909 with geography defined
and 46 with unknown origin.
Total article views: 921 (including HTML, PDF, and XML)
Thereof 900 with geography defined
and 21 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
26 citations as recorded by crossref.
- 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
- 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
- Analysis of the Relative Importance of Model Parameters in Watersheds with Different Hydrological Regimes Y. Medina & E. Muñoz 10.3390/w12092376
- Machine learning-enabled calibration of river routing model parameters Y. Zhao et al. 10.2166/hydro.2023.030
- 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
- Modeling the impact of agricultural crops on the spatial and seasonal variability of water balance components in the Lake Tana basin, Ethiopia T. Tigabu et al. 10.2166/nh.2019.170
- Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: experience from the Adyar catchment, India T. Tigabu et al. 10.1007/s12665-023-11047-2
- 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 spatial resolution in soil and drainage data to combine natural and anthropogenic water functions at catchment scale in agricultural landscapes L. Malmquist & J. Barron 10.1016/j.agwat.2023.108304
- Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria B. Guse et al. 10.1029/2019WR025605
- Using numerical modeling error analysis methods to indicate changes in a watershed K. Mátyás & K. Bene 10.1556/606.2018.13.3.17
- 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
- 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
- Incorporating vegetation dynamics noticeably improved performance of hydrological model under vegetation greening P. Bai et al. 10.1016/j.scitotenv.2018.06.233
- Distinguishing the Relative Contribution of Environmental Factors to Runoff Change in the Headwaters of the Yangtze River M. Guo et al. 10.3390/w11071432
24 citations as recorded by crossref.
- 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
- 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
- Analysis of the Relative Importance of Model Parameters in Watersheds with Different Hydrological Regimes Y. Medina & E. Muñoz 10.3390/w12092376
- Machine learning-enabled calibration of river routing model parameters Y. Zhao et al. 10.2166/hydro.2023.030
- 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
- Modeling the impact of agricultural crops on the spatial and seasonal variability of water balance components in the Lake Tana basin, Ethiopia T. Tigabu et al. 10.2166/nh.2019.170
- Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: experience from the Adyar catchment, India T. Tigabu et al. 10.1007/s12665-023-11047-2
- 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 spatial resolution in soil and drainage data to combine natural and anthropogenic water functions at catchment scale in agricultural landscapes L. Malmquist & J. Barron 10.1016/j.agwat.2023.108304
- Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria B. Guse et al. 10.1029/2019WR025605
- Using numerical modeling error analysis methods to indicate changes in a watershed K. Mátyás & K. Bene 10.1556/606.2018.13.3.17
- 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
- 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
2 citations as recorded by crossref.
- Incorporating vegetation dynamics noticeably improved performance of hydrological model under vegetation greening P. Bai et al. 10.1016/j.scitotenv.2018.06.233
- Distinguishing the Relative Contribution of Environmental Factors to Runoff Change in the Headwaters of the Yangtze River M. Guo et al. 10.3390/w11071432
Latest update: 23 Apr 2024
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...
