Publication Date

8-2022

Date of Final Oral Examination (Defense)

6-6-2022

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Geophysics

Department

Geosciences

Supervisory Committee Chair

Alejandro Flores Ph.D.

Supervisory Committee Member

Hans-Peter Marshall Ph.D.

Supervisory Committee Member

James McNamara Ph.D.

Supervisory Committee Member

Rosemary Carroll Ph.D.

Abstract

Mountain ranges are vital ”water towers” of the world and are uniquely threatened by anthropogenic climate change. At the same time, the paucity of observing networks limits our understanding of hydrometeorological processes in water-resource critical regions, including the Western United States. In the past decade, non-hydrostatic, convection permitting ( 1-4km horizontal resolution) regional climate models (RCMs) have emerged as a promising tool for both reconstructing regional scale mountain hydroclimates, and for forecasting the impacts of climate perturbations on watersheds and water-resources. Still, challenges remain. To-date, computational and data storage limitations have generally precluded many RCM studies to a handful of individual years, limiting the characterization of model uncertainties/biases and thus the interpretation of model outputs. Moreover, validating spatial precipitation fields from RCMs remains a challenge, as gridded precipitation datasets are highly uncertain in locations far away from observing stations. Consequently, further validations of regional climate models require leveraging diverse or indirect sources of hydrologic information. I develop three studies to meet these challenges in this dissertation. In the first, I examine the fidelity of coupled hydrologic-model/RCM for simulating streamflow in four water resource significant, snow-dominated basins in the Boise River basin. In the second, I develop a long-term RCM simulation (1987- 2020) in the Upper Colorado River basin and evaluate precipitation fields using a novel precipitation-from-streamflow bayesian inference strategy. The third chapter of the dissertation examines orographic precipitation sensitivities to cloud-microphysical parameterizations schemes, and leverages Airborne LIDAR snow-depth datasets to evaluate both spatial patterns of precipitation enhancement and watershed-total precipitation delivery. Together, the results from this dissertation demonstrate the utility of multi-decadal regional climate modeling for interrogating mountain hydro-climates, and demonstrates the opportunities and challenges for leveraging diverse hydrologic data sources (streamflow, airborne LIDAR) and methods (bayesian inference) for evaluating RCMs.

DOI

https://doi.org/10.18122/td.1992.boisestate

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