Preprints
https://doi.org/10.5194/tc-2022-89
https://doi.org/10.5194/tc-2022-89
 
23 May 2022
23 May 2022
Status: this preprint is currently under review for the journal TC.

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

Sara E. Darychuk1, Joseph M. Shea1, Brian Menounos1,2, Anna Chesnokova1, Georg Jost3, and Frank Weber3 Sara E. Darychuk et al.
  • 1Deptartment of Geography, Earth and Environmental Sciences, University of Northern British Columbia, Prince George, V2N 4Z9, Canada
  • 2Hakai Institute, Campbell River, British Columbia, Canada
  • 3BC Hydro, Burnaby, British Columbia, Canada V3N 4X8

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: open (until 27 Jul 2022)

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 reply

Sara E. Darychuk et al.

Sara E. Darychuk et al.

Viewed

Total article views: 244 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
184 53 7 244 16 3 2
  • HTML: 184
  • PDF: 53
  • XML: 7
  • Total: 244
  • Supplement: 16
  • BibTeX: 3
  • EndNote: 2
Views and downloads (calculated since 23 May 2022)
Cumulative views and downloads (calculated since 23 May 2022)

Viewed (geographical distribution)

Total article views: 232 (including HTML, PDF, and XML) Thereof 232 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Jun 2022
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.