Statistical Characterization and Development of Snowpack Predictions
- Yu, Michelle
- Advisor(s): Paciorek, Christopher J;
- Pérez, Fernando
Abstract
Accurate high-resolution spatiotemporal data on environmental variables is critical for informing natural resource management and guiding climate change mitigation and adaptation practices. From a hydrological perspective, mountain snowpack is a primary source of freshwater for meeting societal drinking and agricultural needs in various regions. As a result of climate change, these water resources are becoming increasingly vulnerable and declining at a steady pace. To support informed decision-making around water resource planning, accurate data on regular fine-scale spatial and temporal intervals is crucial. Several such gridded data products on key snowpack properties such as snow water equivalent (SWE) and snow depth (SD) have been introduced and provide great utility to the scientific community and practitioners in the environmental space. However, errors and uncertainties in these gridded snow products are not well understood and assumptions underlying the generation of these products are inadequately investigated. In this dissertation, questions around uncertainty and representativeness of gridded snow data are examined and quantified, and a novel statistical approach to estimating SWE is introduced.
The first chapter introduces key terms, concepts, and datasets used throughout the dissertation. The second chapter quantifies error and uncertainty underlying a widely-used gridded SWE product. The third chapter discusses limitations in this gridded product and proposes an alternative framework for empirical SWE prediction that is intuitive, scalable, and statistically sound. Finally, the fourth chapter explores questions around representativeness of point measurements and gridded estimates of true SD and SWE, addressing concerns associated with standard point-to-grid comparisons commonly used in evaluation methodologies.