Spatiotemporal variations in liquid water content in a seasonal snowpack: implications for radar remote sensing
Date
2020
Authors
Bonnell, Randall Ray, author
McGrath, Daniel, advisor
Fassnacht, Steven, committee member
Rasmussen, Kristen, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
Mountain snowpacks act as seasonal reservoirs, providing a critical water resource to ~1.2 billion people globally. Regions with persistent snowpacks (e.g., mountain and polar environments) are responding quickly to climate change and are warming at faster rates than low-elevation temperate and equatorial regions. Since 1915, snow water equivalent (SWE) in the western U.S. snowpack has declined by 21% and snow covered area is contracting in the Rocky Mountains. Despite the clear importance of this resource and the identification of changes affecting it, no current remote sensing approach can accurately measure SWE at high spactiotemporal resolution. L-band (1-2 GHz) Interferometric Synthetic Aperture Radar (InSAR) is a promising approach for detecting changes in SWE at high spatiotemporal resolution in complex topography, but there are uncertainties regarding its performance, particularly when liquid water content (LWC) is present in the snowpack. LWC exhibits high spatial variability, causing spatially varying radar velocity that introduces significant uncertainty in SWE-retrievals. The objectives of this thesis include: (1) examine the importance of slope, aspect, canopy cover, and air temperature in the development of LWC in a continental seasonal snowpack using 1 GHz ground-penetrating radar (GPR), a proxy for L-band InSAR, and (2) quantify the uncertainty in L-band radar SWE-retrievals in wet-snow. This research was performed at Cameron Pass, a high elevation pass (3120 m) located in north-central Colorado, over the course of multiple survey dates during the melt season of 2019. Transects were chosen which represent a range in slope, aspect and canopy cover. Slope and aspect were simplified using the northness index (NI). Canopy cover was quantified using the leaf area index (LAI). Positive degree days (PDD) was used to represent available melt-energy from air temperature. The spatiotemporal development of LWC was studied along the transects using GPR, probed depths, and snowpit measured density. A subset of this project substituted Terrestrial LiDAR Scans (TLS) for probed depths. Surveys (17 in total, up to 3 surveys per date) were performed on seven dates which began on5 April 2019, where LWC values were ~0 vol. %, and ended on 19 June 2019 where LWC values exceeded 10 vol. %. Point measurements of LWC were observed to change (ΔLWC) by +9 vol. % or -8 vol. % over the course of a single day, but median ΔLWC were ~0 vol. % or slightly negative. LAI was negatively correlated with LWC for 13 out of the 17 surveys. NI was negatively correlated with LWC for 10 out of the 17 surveys. Multi-variable linear regressions to estimate ΔLWC identified several statistically significant variables (p-value < 0.10): LAI, NI, ΔPDD, and NI x ΔPDD. Snow-on Terrestrial LiDAR Scans (TLS) were conducted twice during the melt season, and a snow-off scan was conducted in late summer. Snow-on scans were differenced from the snow-off scan to produce distributed snow depth maps. TLS-derived snow depths compared poorly with probe-derived depths, which is attributed to poor LiDAR penetration through the thick vegetation present during the snow-off scan. Finally, radar measurements of SWE (SWE-retrievals), if coupled with velocities derived from dry-snow densities, overestimated the mean SWE along transects by as much as 40% during the melt season, highlighting a potential issue for water managers during the melt season. Future work to support the testing of L-band radar SWE-retrievals in wet-snow should test radar signal-power attenuation methods and the capabilities of snow models for estimating LWC.
Description
Rights Access
Subject
L-band radar
snow
snow water equivalent
liquid water content
ground-penetrating radar
snow melt