Preprints
https://doi.org/10.5194/hess-2022-136
https://doi.org/10.5194/hess-2022-136
 
13 May 2022
13 May 2022
Status: a revised version of this preprint was accepted for the journal HESS.

Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent

Eunsang Cho1,2, Carrie M. Vuyovich1, Sujay V. Kumar1, Melissa L. Wrzesien1,2, Rhae Sung Kim1,3, and Jennifer M. Jacobs4,5 Eunsang Cho et al.
  • 1Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 3Goddard Earth Sciences Technology and Research II, University of Maryland Baltimore County, Baltimore, MD, USA
  • 4Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
  • 5Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA

Abstract. Seasonal snow is an essential component of regional and global water and energy cycles, particularly in snow-dominant regions that rely on snowmelt for water resources. Land surface models (LSMs) are a common approach for developing spatially and temporally complete estimates of snow water equivalent (SWE) and hydrologic variables at a large scale. However, the accuracy of the LSM-based SWE outputs is limited and unclear by mixed factors such as uncertainties in the meteorological boundary conditions and the model physics. In this study, we assess the SWE, snowfall, precipitation, and air temperature products from a twelve-member ensemble – with four LSMs and three meteorological forcings – using automated SWE, precipitation, and temperature observations from 809 Snowpack Telemetry stations over the western U.S. Results show that the mean annual maximum LSM SWE is underestimated by 268 mm. The timing of peak SWE from the LSMs is on average 36 days earlier than that of the observations. By the date of peak SWE, winter accumulated precipitation is underestimated (forcings mean: 485 mm vs. stations: 690 mm). In addition, the precipitation partitioning physics generates different snowfall estimates by an average of 113 mm with the same forcing data. Even though there are widespread cold biases (up to 3 °C) in the temperature forcings, larger ablations and lower ratios of SWE to total precipitation are found even in the accumulation period, indicating that melting physics in LSMs drives some SWE uncertainties. Based on the principal component analysis, we find that precipitation bias has the largest contribution to the first principal component, which accounts for more than half of the total variance. The results provide insights into prioritizing strategies to improve SWE estimates from LSMs for hydrologic applications.

Eunsang Cho et al.

Status: final response (author comments only)

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

Eunsang Cho et al.

Eunsang Cho et al.

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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.