Publication Date

5-2025

Date of Final Oral Examination (Defense)

2-26-2025

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Computing

Department

Computing

Supervisory Committee Chair

Hans-Peter Marshall, Ph.D.

Supervisory Committee Co-Chair

Jodi Mead, Ph.D.

Supervisory Committee Member

Kyungduk Ko, Ph.D.

Supervisory Committee Member

Ernesto Trujillo, Ph.D.

Abstract

Seasonal snow is a critical freshwater resource for an estimated 2 billion people worldwide. Yet, accurately measuring the amount of water sitting in a snowpack, referred to as snow water equivalent (SWE), over large, often mountainous regions has posed a long-standing challenge. Ground-based measurements of SWE are precise but sparse, while remote sensing techniques like passive microwave sensors struggle with coarse resolution and signal saturation in deep snow. Due to the challenges of direct SWE measurement, snow depth has emerged as an alternative pathway to SWE estimation. SWE is strongly correlated with snow depth, and by leveraging this relationship, we can estimate SWE from snow depth measurements if the snowpack bulk density is known. Snow depth has been successfully measured with remote sensing techniques, such as light detection and ranging (lidar). However, the high cost of lidar prevents its widespread adoption, necessitating an alternative remote sensing approach (e.g., active microwave). This dissertation explored the potential of L-band Interferometric Synthetic Aperture Radar (InSAR), which can be deployed over large areas, and machine learning (ML) to estimate snow depth at various spatial resolutions. Additionally, we developed an ML model to estimate snow density from snow depth and variables that can be readily recorded or derived from the date and location of depth observations (i.e., snow class, day of water year, and elevation). This approach reduces the SWE estimation problem to a snow depth monitoring problem combined with a snow density modeling effort. Results indicated that this approach is promising and may complement existing snow monitoring practices.

Comments

https://orcid.org/0000-0001-9480-6731

DOI

10.18122/td.2358.boisestate

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