Articles | Volume 23, issue 10
https://doi.org/10.5194/hess-23-4323-2019
https://doi.org/10.5194/hess-23-4323-2019
Technical note
 | 
25 Oct 2019
Technical note |  | 25 Oct 2019

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Wouter J. M. Knoben, Jim E. Freer, and Ross A. Woods

Related authors

Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279,https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript under review for HESS
Short summary
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024,https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024,https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
OpenWQ v.1: A multi-chemistry modelling framework to enable flexible, transparent, interoperable, and reproducible water quality simulations in existing hydro-models
Diogo Costa, Kyle Klenk, Wouter Knoben, Andrew Ireson, Raymond J. Spiteri, and Martyn Clark
EGUsphere, https://doi.org/10.5194/egusphere-2023-2787,https://doi.org/10.5194/egusphere-2023-2787, 2023
Preprint archived
Short summary
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022,https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025,https://doi.org/10.5194/hess-29-27-2025, 2025
Short summary
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024,https://doi.org/10.5194/hess-28-5511-2024, 2024
Short summary
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024,https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024,https://doi.org/10.5194/hess-28-5353-2024, 2024
Short summary
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024,https://doi.org/10.5194/hess-28-5331-2024, 2024
Short summary

Cited articles

Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017a. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies. version 2.0., UCAR/NCAR, Boulder, CO, USA, https://doi.org/10.5065/D6G73C3Q, 2017b. 
Andersson, J. C. M., Arheimer, B., Traoré, F., Gustafsson, D., and Ali, A.: Process refinements improve a hydrological model concept applied to the Niger River basin, Hydrol. Process., 31, 4540–4554, https://doi.org/10.1002/hyp.11376, 2017. 
Beven, K. J., Younger, P. M., and Freer, J.: Struggling with Epistemic Uncertainties in Environmental Modelling of Natural Hazards, in: Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA), 13–16 July 2014, Liverpool, UK, American Society of Civil Engineers, 13–22, 2014. 
Download
Short summary
The accuracy of model simulations can be quantified with so-called efficiency metrics. The Nash–Sutcliffe efficiency (NSE) has been often used in hydrology, but recently the Kling–Gupta efficiency (KGE) is gaining in popularity. We show that lessons learned about which NSE scores are acceptable do not necessarily translate well into understanding of the KGE metric.