Preprints
https://doi.org/10.5194/egusphere-2023-1603
https://doi.org/10.5194/egusphere-2023-1603
18 Jul 2023
 | 18 Jul 2023

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich

Abstract. Snow is a vital component of the Earth system. Yet, no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use an Observation System Simulation Experiment, specifically investigating how much snow simulated using popular models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24°-by-37° domain in the Western United States and Canada, simulating snow at 250 m resolution and hourly timesteps in water-year 2019. We perform two data assimilation experiments, including: 1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals, and 2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that assimilating synthetic SWE observations improved average SWE biases at peak snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at peak snowpack were 111 mm, and average SWE biases were on the order of 150 %. Here, the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River basin, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash Sutcliffe Efficiency of runoff improved from –2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

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Journal article(s) based on this preprint

13 Feb 2024
Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024,https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1603', Anonymous Referee #1, 28 Aug 2023
    • AC3: 'Reply on RC1', Justin Pflug, 10 Oct 2023
  • RC2: 'Comment on egusphere-2023-1603', Anonymous Referee #2, 01 Sep 2023
    • AC2: 'Reply on RC2', Justin Pflug, 10 Oct 2023
  • RC3: 'Comment on egusphere-2023-1603', Anonymous Referee #3, 08 Sep 2023
    • AC1: 'Reply on RC3', Justin Pflug, 10 Oct 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1603', Anonymous Referee #1, 28 Aug 2023
    • AC3: 'Reply on RC1', Justin Pflug, 10 Oct 2023
  • RC2: 'Comment on egusphere-2023-1603', Anonymous Referee #2, 01 Sep 2023
    • AC2: 'Reply on RC2', Justin Pflug, 10 Oct 2023
  • RC3: 'Comment on egusphere-2023-1603', Anonymous Referee #3, 08 Sep 2023
    • AC1: 'Reply on RC3', Justin Pflug, 10 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (19 Oct 2023) by Shraddhanand Shukla
AR by Justin Pflug on behalf of the Authors (02 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Nov 2023) by Shraddhanand Shukla
RR by Anonymous Referee #3 (14 Nov 2023)
ED: Publish subject to minor revisions (review by editor) (22 Dec 2023) by Shraddhanand Shukla
AR by Justin Pflug on behalf of the Authors (05 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (09 Jan 2024) by Shraddhanand Shukla
AR by Justin Pflug on behalf of the Authors (10 Jan 2024)

Journal article(s) based on this preprint

13 Feb 2024
Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024,https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich

Data sets

Pflug et al. (2023) ‒ Model configuration and outputs Justin M. Pflug https://www.hydroshare.org/resource/e0ad80f818bf4062a335e9e0d7362834/

Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich

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Short summary
250 m estimates of snow in the Western US and Canada were improved by assimilating observations representative of a future snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both snow and runoff throughout spring snowmelt.