Articles | Volume 26, issue 9
Hydrol. Earth Syst. Sci., 26, 2519–2539, 2022
Hydrol. Earth Syst. Sci., 26, 2519–2539, 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.

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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,, 2006. a
Bandaragoda, C., Tarboton, D. G., and Woods, R.: Application of TOPNET in the distributed model intercomparison project, J. Hydrol., 298, 178–201,, 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,, 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,, 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,, 2004. a
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.