The amount of water flowing through streams and rivers changes through time. The seasonality and duration of these changes can have profound impacts on human freshwater availability, aquatic habitat, and biogeochemical cycling. Numerous factors are thought to influence streamflow regime, including drainage basin area, temperature, precipitation, and land cover. Few of these qualities have remained untouched, either directly or indirectly, by expanding human activities. Altered climate, sweeping changes to large portions of the earth's surface, and the construction of dams and other infrastructure have fundamentally altered streamflows worldwide. Understanding the nature of these changes, both globally and regionally in the Western United States, is the subject of this thesis. In chapter 1 we explore ideal metric spaces for describing streamflow regime. The representation of information in concise terms is usually preliminary to developing an understanding of any system, and streamflow regime, which has been described with over 600 unique variables, is no exception. We demonstrate the efficacy of dimensionality reduction techniques, as well as frequency decompositions, in succinctly capturing much of the information previously described with hundreds of variables. We use this succinct language to gain key insights into major drivers of streamflow regime and present a new hypothesis about the mechanisms mediating flow variability. In chapter 2, we use frequency decompositions and several machine learning approaches to characterize streamflow regimes around the world and to understand how they are changing through time. Finally, in chapter 3, we analyze the effect that wildfire has had on the timing, amount, and variability of flow in the western US in recent decades. The work presented here demonstrates the power that advances in data science, particularly in time series analysis methods and machine learning, can have when coupled with large datasets in revealing insights into global and regional phenomena in hydrology.



College and Department

Life Sciences; Plant and Wildlife Sciences



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hydrology, machine learning, wildfire, frequency decomposition, neural network



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Life Sciences Commons