Articles | Volume 26, issue 22
Research article
14 Nov 2022
Research article |  | 14 Nov 2022

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2022-136', Ross Brown, 17 May 2022
    • AC2: 'Reply on CC1', Eunsang Cho, 07 Aug 2022
  • RC1: 'Comment on hess-2022-136', Anonymous Referee #1, 17 Jun 2022
    • AC1: 'Reply on RC1', Eunsang Cho, 07 Aug 2022
  • RC2: 'Comment on hess-2022-136', Anonymous Referee #2, 24 Jun 2022
    • AC3: 'Reply on RC2', Eunsang Cho, 07 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (20 Aug 2022) by Jan Seibert
AR by Eunsang Cho on behalf of the Authors (24 Aug 2022)  Author's response
ED: Publish as is (12 Sep 2022) by Jan Seibert

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Eunsang Cho on behalf of the Authors (10 Nov 2022)   Author's adjustment   Manuscript
EA: Adjustments approved (12 Nov 2022) by Jan Seibert
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.