Guidance on evaluating parametric model uncertainty at decision-relevant scales

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

doi:https://doi.org/10.5194/hess-26-2519-2022

Articles | Volume 26, issue 9

https://doi.org/10.5194/hess-26-2519-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-2519-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 9

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

Supplement

https://doi.org/10.5194/hess-26-2519-2022-supplement

Data sets

RHESSys Morris Sensitivity Analysis Data Repository for Smith et al. Jared D. Smith https://doi.org/10.4211/hs.c63ddcb50ea84800a529c7e1b2a21f5e

Model code and software

RHESSys_ParamSA-Cal-GIOpt Jared D. Smith https://github.com/jds485/RHESSys_ParamSA-Cal-GIOpt

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.