Advancing Understanding of Changes in the Cryosphere of the Western United States and Alaska: A Case for the Importance of Spatial and Temporal Scaling
The global cryosphere, defined as the world’s ice and snow covered regions, is a crucial water source for society and ecosystem functions, as well as an important regulator of the earth’s energy budget. Melt from glaciers and seasonal snow cover provides water for more than a sixth of the world’s po...
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| Other Authors: | , , , , , , , , , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English unknown |
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Oregon State University
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| Online Access: | https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/d217qx84v |
| Summary: | The global cryosphere, defined as the world’s ice and snow covered regions, is a crucial water source for society and ecosystem functions, as well as an important regulator of the earth’s energy budget. Melt from glaciers and seasonal snow cover provides water for more than a sixth of the world’s population, and has a crucial role to play in temperature regulation and nutrient cycles in river systems around the world. Simultaneously, glacier cover around the world is receding, contributing to global sea level rise, and seasonal snow melt timing is shifting earlier in many places, with complex repercussions for all regions involved. Although many of these changes have been documented, the complexity of the issues leaves unanswered many questions that are relevant to management and response to a changing snow and hydrological regime. To begin addressing these questions, I examine changes in the cryosphere at a higher temporal granularity, a different temporal resolution, or a more nuanced spatial scale than has been previously done. Research foci include mapping glacier-covered area in Alaska using a novel method, evaluating snow drought occurrences in the US Pacific Northwest (PNW) at a variety of spatial scale and spatial aggregation choices, and, finally, examining temporal and spatial patterns of snowmelt-related runoff in the Western United States (WUS) with an eye towards likely future shifts under climate change. Chapter 2 presents a novel deep learning-based method for mapping Alaska’s extensive glacier covered area. It also describes the associated dataset and its performance compared to other contemporary products. The work represents several important methodological advances, including an object-based classification approach with no post-hoc manual editing and the ability to process large quantities of remote sensing data quickly to produce high temporal granularity maps. Evaluated in several ways, the model produced maps with very high spatial fidelity. Using this dataset, we showed in rich temporal ... |
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