Dissertation Information

Title: From the Cosmos to the Canopy: Quantifying Uncertainty Across Cosmological and Environmental Systems

Program: Computing Ph.D. - Data Science

Advisor: Dr. Nancy Glenn

Committee Members: Dr. Alejandro Flores, Dr. Jodi Mead

Abstract: This dissertation presents three distinct interdisciplinary research projects spanning cosmology and geosciences, with a foundation in data science. The first project addresses statistical and systematic uncertainties in weak lensing mass calibration for optically selected galaxy clusters. It focuses on calibrating the mass of dark matter halos (or galaxy clusters) while accounting for systematic effects in simulations. We investigate how these systematic uncertainties translate into weak lensing mass calibration uncertainties. Novel contributions include demonstrating how concentration biases can mimic systematic effects and showing that high signal-to-noise data allow for tighter mass constraints, even with simplified covariance matrices.

The second project seeks to quantify vegetation structure in dryland ecosystems using spaceborne lidar data and to understand the limitations of these datasets in quantifying vegetation structure in dryland ecosystems. We utilize data from two major spaceborne lidar instruments from NASA: GEDI and ICESat-2. In this project, we quantify the detection thresholds of vegetation height and uncertainties over complex topography, offering insights relevant to carbon monitoring and informing upcoming missions such as NASA’s Earth Dynamics Geodetic Explorer (EDGE) mission.

The third project focuses on downscaling geostationary surface albedo using neural networks for snowmelt processes. Climate change has significantly impacted snowpack in mountainous regions, but the role of processes such as snowmelt and water discharge in this change remains unclear. Using a case study in the Upper Colorado River Basin, this work aims to produce high-resolution albedo maps to enhance the modeling of snowmelt dynamics and water resource forecasting.

Although all three projects are distinct in terms of objectives, data involved, and research direction, they are unified by their emphasis on data science, remote sensing, data fusion, and uncertainty quantification. For instance, each project addresses a specific form of remote sensing problem. In conclusion, each project independently advances its field, while together they underscore the growing role of computational and data-driven methods in contemporary Earth and space science.