Graduate Thesis Or Dissertation


Headwater Dynamics: Quantifying Spatial Differences in Flow Permanence and Network Connectivity across Diverse Landscapes Public Deposited

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  • Forested headwater streams are critical for the supply of water, sediment, nutrients, and organic matter to downstream water bodies. Nearly half of all headwater streams display temporary flow regimes (i.e., non-perennial), but with climate change they may become more common. Thus, it will be increasingly imperative to adequately represent non-perennial watercourses and describe the mechanisms that drive flow permanence and stream network connectivity. We conducted an extensive field campaign in streams draining diverse climatic and geologic conditions predominantly within the Anadromous Salmonid Protection Area of California. We visited 101 headwater streams in four distinct geomorphic provinces between late June and September 2018. Streams had contributing drainage areas ranging from 0.04–3.14 km2. At each stream, we measured channel geometry (bankfull and wetted dimensions) and channel grain size across a ~60 m reach, beginning at the outlet. Additionally, we calculated channel slope, topographic wetness, catchment curvature, and several other geospatially-derived metrics for each stream in our sample. Field and geospatial data were used to estimate perennial and non-perennial character using random forest classification. Results provided strong evidence for differences in flow permanence and variables of headwater streams related to climate, geology and landcover among headwater streams draining four distinct geomorphic provinces. Modeling results indicated that the possible controlling factors of flow permanence include grain size, canopy coverage, contributing drainage area and minimum annual temperature. Furthermore, the results of our model support the use of field-based and geospatially-derived metrics for potentially describing and predicting perennial and non-perennial character in headwater streams across spatially variable terrain (model prediction accuracy: ~63–73%).
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  • Pending Publication
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  • 2019-09-24 to 2020-10-25



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