Date of Award
1-24-2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Earth Sciences
Advisor(s)
Sam Tuttle
Keywords
Remote Sensing;Snowmelt
Abstract
Snowmelt is a critical component of hydrologic processes in mountainous and seasonally cold regions. As such, monitoring and understanding regional snowmelt patterns and fluctuations is a crucial aspect of water resource management. While ground-based snow monitoring stations can provide continuous data on melting processes, they are cost-prohibitive for dense coverage at regional to global scales. Satellites, however, can provide global data on weekly to twice daily time scales. Previous studies have found that passive microwave (PMW) remote sensing data from satellites with twice daily observations can be used to detect onset of snowmelt using changes in brightness temperature (a measure of emitted radiation) from day to night, known as the diurnal amplitude variation (DAV). This study first evaluates the accuracy of an enhanced DAV method developed by Tuttle & Jacobs (2019) in a heterogenous environment consisting of forest and cropland by comparing satellite detected melt events to detailed ground snow observations collected at Sleeper’s River Research Watershed, VT between 2021-2023. Using lessons learned, the analysis is extended to over 500 snow stations located throughout the western US and Canada, using daily SWE and snow depth data from 2002-2011. This study aims to fill gaps in 1) evaluating PMW melt detection techniques using detailed observations of the snowpack energy state, and 2) assessing their performance in mid-latitude regions and a variety of different terrains/climates. I find that snow surface temperature observations are more valuable than other tested methods for validation of melt events detected using PMW observations. I also find that, in accordance with previous studies, PMW melt detection methods are likely most sensitive to liquid water at the surface of the snowpack, making them more useful for detecting midwinter surface melt and the onset of the spring melt period, rather than hydrologically significant releases of snowmelt.
Access
Open Access
Recommended Citation
Rienzo, Angela, "Evaluating A Satellite Passive Microwave Snowmelt Detection Algorithm Using In-Situ Snowmelt Indicators" (2024). Theses - ALL. 808.
https://surface.syr.edu/thesis/808
