In many regions of the world, a significant portion of the surface water originates in mountain headwaters where the timing and magnitude of streamflow is largely dictated by the seasonal storage of precipitation as snowpack and long-term storage as glaciers. Accumulation, persistence, and melt of snow and ice are functions of the climate in which they exist and therefore respond to changes in that climate. One important use of water in many regions is for hydropower energy production. While reservoir-based hydropower infrastructure has some ability to absorb changes in timing of streamflow, run-of-river hydropower infrastructure does not. Thus, in assessing the economic feasibility of new or existing run-of-river infrastructure, it is important to account for potential impacts climate change may have over the lifetime of the project. Projecting impacts of climate change on surface water resources, and in particular on run-of-river hydropower resource potential, requires robustly characterizing the linkages between the climate, cryosphere, and streamflow. Two obstacles to increasing our understanding of mountain systems are the sparsity of observation data and complexity of weather patterns. The first part of my research addresses the issue of climate data availability in mountain regions through development of statistical models to characterize the high-spatial resolution distribution of historic and projected future precipitation and temperature. I demonstrate these climate products through projecting long-term changes in snowfall for the Alaska Range, Alps, Central Andes, and Himalaya-Karakoram-Hindu Kush ranges. I then present a framework for assessing conceptual cryosphere hydrology models and implement the framework for two long-term glacier study sites in Alaska, USA. Using this framework, I identify novel formulations for modeling the heat transfer and energy balance of snowpacks and glaciers that improve model robustness relative to the current generation of cryosphere hydrology models typically used in data-sparse mountain environments. I then demonstrate a method for understanding the impacts of projected future climate change on run-of-river hydropower resource potential, using Falls Creek in Oregon, USA as a test case. A core value of this work is producing models that can be straightforwardly applied to any region, thus decreasing the impacts of data disparities between regions on our ability to characterize climate change impacts on mountain regions.
