Preprints
https://doi.org/10.5194/tc-2022-89
https://doi.org/10.5194/tc-2022-89
23 May 2022
 | 23 May 2022
Status: a revised version of this preprint was accepted for the journal TC and is expected to appear here in due course.

Snowmelt Characterization from Optical and Synthetic Aperture Radar Observations in the Lajoie Basin, British Columbia

Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber

Abstract. Snowmelt runoff serves both human needs and ecosystem services and is an important parameter in operational forecasting systems. Sentinel-1 Synthetic Aperture Radar (SAR) observations can estimate the timing of melt within a snowpack; however, these estimates have not been applied on large spatial scales. We present here a workflow to fuse Sentinel-1 SAR and optical data from Landsat-8 and Sentinel-2 to estimate the onset and duration of snowmelt in the Lajoie Basin, a large watershed in the Southern Coast Mountains of British Columbia. A backscatter threshold is used to infer the point at which snowpack saturation occurs, and the snowpack begins to produce runoff. Multispectral imagery is used to estimate snow free dates across the basin to define the end of the snowmelt period. SAR estimates of snowmelt onset form consistent trends by elevation and aspect on the watershed scale and reflect snowmelt records from continuous SWE observations. SAR estimates of snowpack saturation are most effective on moderate to low slopes (< 30°) in open areas. The accuracy of snowmelt durations is reduced due to persistent cloud cover in optical imagery. Despite these challenges, snowmelt durations agree with trends in snow depths observed in the Lajoie. This approach has high potential for adaptability to other alpine regions and can provide estimates of snowmelt timing in ungauged basins.

Sara E. Darychuk et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-89', Carlo Marin, 24 Jun 2022
    • AC1: 'Reply on RC1', Sara Darychuk, 15 Sep 2022
  • RC2: 'Comment on tc-2022-89', Giacomo Bertoldi, 22 Aug 2022
    • AC2: 'Reply on RC2', Sara Darychuk, 23 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-89', Carlo Marin, 24 Jun 2022
    • AC1: 'Reply on RC1', Sara Darychuk, 15 Sep 2022
  • RC2: 'Comment on tc-2022-89', Giacomo Bertoldi, 22 Aug 2022
    • AC2: 'Reply on RC2', Sara Darychuk, 23 Sep 2022

Sara E. Darychuk et al.

Sara E. Darychuk et al.

Viewed

Total article views: 593 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
423 157 13 593 44 5 4
  • HTML: 423
  • PDF: 157
  • XML: 13
  • Total: 593
  • Supplement: 44
  • BibTeX: 5
  • EndNote: 4
Views and downloads (calculated since 23 May 2022)
Cumulative views and downloads (calculated since 23 May 2022)

Viewed (geographical distribution)

Total article views: 572 (including HTML, PDF, and XML) Thereof 572 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Mar 2023
Download
Short summary
We use Synthetic Aperture Radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and landcover types, and present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.