Articles | Volume 26, issue 9
Hydrol. Earth Syst. Sci., 26, 2519–2539, 2022
https://doi.org/10.5194/hess-26-2519-2022
Hydrol. Earth Syst. Sci., 26, 2519–2539, 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 et al.

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
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221, https://doi.org/10.5194/hess-26-1203-2022,https://doi.org/10.5194/hess-26-1203-2022, 2022
Short summary
Sequential data assimilation for real-time probabilistic flood inundation mapping
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021,https://doi.org/10.5194/hess-25-4995-2021, 2021
Short summary
Key challenges facing the application of the conductivity mass balance method: a case study of the Mississippi River basin
Hang Lyu, Chenxi Xia, Jinghan Zhang, and Bo Li
Hydrol. Earth Syst. Sci., 24, 6075–6090, https://doi.org/10.5194/hess-24-6075-2020,https://doi.org/10.5194/hess-24-6075-2020, 2020
Short summary
Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
Aynom T. Teweldebrhan, Thomas V. Schuler, John F. Burkhart, and Morten Hjorth-Jensen
Hydrol. Earth Syst. Sci., 24, 4641–4658, https://doi.org/10.5194/hess-24-4641-2020,https://doi.org/10.5194/hess-24-4641-2020, 2020
A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020,https://doi.org/10.5194/hess-24-4463-2020, 2020
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.