Theses and Dissertations Collection

The hydroclimate of mountain environments shapes the spatiotemporal distribution of energy and water on scales from local to global. Climate change is inducing rapid changes in mountain hydroclimate in the form of warmer temperatures, reduced snowpack and glacier mass balance, and earlier snowmelt runoff. These changes have large implications for local and downstream ecosystems and societies. However, understanding of current and future mountain hydroclimate is hindered by a paucity of observations and the need for high resolution data and models to capture the effects of topographic complexity. New modeling approaches, tools, and datasets are needed. This dissertation addresses these needs with a focus on three important aspects of mountain hydroclimate: air temperature, snow, and rock glaciers. In the first study, I quantify the error and uncertainty in air temperature lapse rates and outline best practices for lapse rate estimation. In the second study, I develop a novel energy balance snow model and force it with a new high-resolution multitemporal climate dataset to create a dataset of snow and climate metrics for the western United States. In the final study, I employ a machine learning method to understand the response of rock glacier spatial distributions to climate change. This dissertation contributes to scientific understanding of mountain hydroclimate and provides tools and datasets to further develop this understanding.