22 Jun 2021

22 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Jared D. Smith1, Laurence Lin2, Julianne D. Quinn1, and Lawrence E. Band1,2 Jared D. Smith et al.
  • 1Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
  • 2Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA

Abstract. Spatially distributed hydrologic models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing decision objectives. Identifying parameters that control physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using parameter multipliers to further reduce dimensionality. We evaluate six sensitivity metrics that align with four decision objectives; two metrics consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results to those obtained by four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that 1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, 2) evaluating sensitivity metrics at only the basin outlet does not capture many parameters that control streamflows in hillslopes, and 3) for some parameter multipliers, calibration of just one of the parameters being adjusted may be the preferred approach for reducing dimensionality. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

Jared D. Smith et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-324', Fanny Sarrazin, 16 Aug 2021
    • AC1: 'Reply on RC1', Jared Smith, 21 Sep 2021
  • RC2: 'Comment on hess-2021-324', Anonymous Referee #2, 19 Aug 2021
    • AC2: 'Reply on RC2', Jared Smith, 21 Sep 2021

Jared D. Smith et al.

Data sets

RHESSys Morris Sensitivity Analysis Data Repository for Smith et al. Jared D. Smith

Model code and software

RHESSys_ParamSA-Cal-GIOpt Jared D. Smith

Jared D. Smith et al.


Total article views: 433 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
309 112 12 433 35 2 2
  • HTML: 309
  • PDF: 112
  • XML: 12
  • Total: 433
  • Supplement: 35
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 22 Jun 2021)
Cumulative views and downloads (calculated since 22 Jun 2021)

Viewed (geographical distribution)

Total article views: 388 (including HTML, PDF, and XML) Thereof 388 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 22 Oct 2021
Short summary
To inform the design of green infrastructure (GI) projects for runoff and nutrient control, watershed models are used to simulate the effects of GI on these variables. However, data are limited for watershed features that are parameterized in such models. This requires screening the most important to be calibrated. We show that this screening should use decision-relevant metrics at spatial scales that control flows across the watershed. This should enable the design of robust GI portfolios.