Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-5011-2024
https://doi.org/10.5194/hess-28-5011-2024
Research article
 | 
26 Nov 2024
Research article |  | 26 Nov 2024

On the importance of discharge observation uncertainty when interpreting hydrological model performance

Jerom P. M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, and Rolf W. Hut

Related authors

Relevance of feedbacks between water availability and crop systems using a coupled hydrology – crop growth model
Sneha Chevuru, Rens L. P. H. van Beek, Michelle T. H. van Vliet, Jerom P. M. Aerts, and Marc F. P. Bierkens
EGUsphere, https://doi.org/10.5194/egusphere-2024-465,https://doi.org/10.5194/egusphere-2024-465, 2024
Short summary
Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022,https://doi.org/10.5194/hess-26-5971-2022, 2022
Short summary
Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022,https://doi.org/10.5194/hess-26-4407-2022, 2022
Short summary
The eWaterCycle platform for open and FAIR hydrological collaboration
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022,https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Comparison of estimates of global flood models for flood hazard and exposed gross domestic product: a China case study
Jerom P. M. Aerts, Steffi Uhlemann-Elmer, Dirk Eilander, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 20, 3245–3260, https://doi.org/10.5194/nhess-20-3245-2020,https://doi.org/10.5194/nhess-20-3245-2020, 2020
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
A data-centric perspective on the information needed for hydrological uncertainty predictions
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024,https://doi.org/10.5194/hess-28-4099-2024, 2024
Short summary
A decomposition approach to evaluating the local performance of global streamflow reanalysis
Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024,https://doi.org/10.5194/hess-28-3597-2024, 2024
Short summary
How much water vapour does the Tibetan Plateau release into the atmosphere?
Chaolei Zheng, Li Jia, Guangcheng Hu, Massimo Menenti, and Joris Timmermans
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-55,https://doi.org/10.5194/hess-2024-55, 2024
Revised manuscript accepted for HESS
Short summary
Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems
Uwe Ehret and Pankaj Dey
Hydrol. Earth Syst. Sci., 27, 2591–2605, https://doi.org/10.5194/hess-27-2591-2023,https://doi.org/10.5194/hess-27-2591-2023, 2023
Short summary
Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox: facilitating the communication of epistemic uncertainty
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 27, 2523–2534, https://doi.org/10.5194/hess-27-2523-2023,https://doi.org/10.5194/hess-27-2523-2023, 2023
Short summary

Cited articles

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, 2017. a
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrolog. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2020. a
Aerts, J.: jeromaerts/CAMELS-GB_Comparison_Uncertainty: Initial Zenodo Commit, Zenodo [code], https://doi.org/10.5281/zenodo.7956488, 2023. a
Aerts, J.: Results Discharge Observation Uncertainty and Model Performance Pub (Version v1), Zenodo [data set], https://doi.org/10.5281/zenodo.14186584, 2024. a
Aerts, J. P. M., Hut, R. W., van de Giesen, N. C., Drost, N., van Verseveld, W. J., Weerts, A. H., and Hazenberg, P.: Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model, Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, 2022. a
Download
Short summary
For users of hydrological models, model suitability often hinges on how well simulated outputs match observed discharge. This study highlights the importance of including discharge observation uncertainty in hydrological model performance assessment. We highlight the need to account for this uncertainty in model comparisons and introduce a practical method suitable for any observational time series with available uncertainty estimates.