Year of Award

2014

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Geosciences

Other Degree Name/Area of Focus

Watershed Hydrology

Department or School/College

Department of Geosciences

Committee Chair

Marco Maneta

Commitee Members

Shu-Hua Chen, Joel Harper, John Kimball, Johnnie Moore

Keywords

Bayesian, Budyko, Complex terrain, Land use/cover, Uncertainty, Winter precipitation

Abstract

The hydroclimatic regime of mountain landscapes in the Pacific Northwest, USA plays a large role in collecting, storing, and distributing water throughout the greater United States. The complexities of the landscape, vegetation, and anthropogenic impacts within this region form a dynamic web of interactions that create unique challenges when quantifying hydrologic variables. This dissertation focuses on these challenges and introduces a new way of deciphering the driving mechanism of streamflow trends. The first project examines the differences between modeling techniques in mountain terrain. In this project, we use a physically-based regional climate model to dynamically downscale global climate estimates for winter precipitation over western Montana. We compare these estimates with an observationally-based model over the same region. Results show large discrepancies at high elevations where little observations exist. In these areas, the physics-based model consistently estimates higher amounts of winter precipitation and interannual variability. Potential biases in both models are evaluated. The second project focuses on the uncertainty in estimating winter precipitation at high elevation using the current observational network. We use Bayesian inference to calculate and spatially distribute uncertainty across the western Montana landscape. We analyze this uncertainty in terms of potential differences in winter precipitation over the next 40 years and find that aspect and elevation are key components in quantifying uncertainty and potential change in mountain terrain. Overall, we find that current observational networks may be missing climate change signals at high elevation and we identify optimal locations based on topographic attributes and climate projections where additional weather stations would be most beneficial. The third project expands the boundaries of the domain to the entire Pacific Northwest, USA. In this project, we develop a framework for distinguishing streamflow trends that are driven by land use from those that are driven by climate. This framework is based on unique runoff sensitivities between driving mechanisms that are related to water and energy limitations. We validate the framework using over 1,500 stream gages across the United States and then apply it in a case study of the Pacific Northwest. Our results show that the majority of the streamflow trends are primarily driven by land use and cover not monotonic changes in climate forcings.

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© Copyright 2014 Nicholas Loren Silverman