Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2519-2022
https://doi.org/10.5194/hess-26-2519-2022
Research article
 | 
16 May 2022
Research article |  | 16 May 2022

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Jared D. Smith, Laurence Lin, Julianne D. Quinn, and Lawrence E. Band

Related authors

Borehole research in New York State can advance utilization of low-enthalpy geothermal energy, management of potential risks, and understanding of deep sedimentary and crystalline geologic systems
Teresa Jordan, Patrick Fulton, Jefferson Tester, David Bruhn, Hiroshi Asanuma, Ulrich Harms, Chaoyi Wang, Doug Schmitt, Philip J. Vardon, Hannes Hofmann, Tom Pasquini, Jared Smith, and the workshop participants
Sci. Dril., 28, 75–91, https://doi.org/10.5194/sd-28-75-2020,https://doi.org/10.5194/sd-28-75-2020, 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

Anderson, R. M., Koren, V. I., and Reed, S. M.: Using SSURGO data to improve Sacramento Model a priori parameter estimates, J. Hydrol., 320, 103–116, https://doi.org/10.1016/j.jhydrol.2005.07.020, 2006. a
Bandaragoda, C., Tarboton, D. G., and Woods, R.: Application of TOPNET in the distributed model intercomparison project, J. Hydrol., 298, 178–201, https://doi.org/10.1016/j.jhydrol.2004.03.038, 2004. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a, b
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Modell. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007. a
Canfield, H. E. and Lopes, V. L.: Parameter identification in a two-multiplier sediment yield model, J. Am. Water Resour. As., 40, 321–332, https://doi.org/10.1111/j.1752-1688.2004.tb01032.x, 2004. a
Download
Short summary
Watershed models are used to simulate streamflow and water quality, and to inform siting and sizing decisions for runoff and nutrient control projects. Data are limited for many watershed processes that are represented in such models, which requires selecting the most important processes to be calibrated. We show that this selection should be based on decision-relevant metrics at the spatial scales of interest for the control projects. This should enable more robust project designs.