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Dynamic Stream Permanence Estimates at Local and Regional Extents

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Speaker: Konrad Hafen, USGS 

Topic: Dynamic Stream Permanence Estimates at Local and Regional Extents 

Abstract: In the United States (US), the frequency and duration of surface water in a stream channel (i.e. stream permanence) determines if a stream is subject to regulation under the Clean Water Act. The most comprehensive dataset of stream permanence classifications for the US is the National Hydrography Dataset (NHD), which has been shown to exhibit high rates of disagreement with in situ stream permanence observations. Analysis of NHD stream permanence disagreements with in situ observations indicates that differences in climate conditions between observation years contribute to the NHD disagreements and supports the need for dynamic simulation of stream permanence based on climate. In this study, two differing hydrological models were implemented to generate dynamic stream permanence estimates at a regional and local extents. In the Pacific Northwest (PNW) the Thornthwaite monthly water balance model (MWBM) was implemented to generate stream permanence estimates for headwater streams in the NHD network from 1977-2019. In the HJ Andrews Experimental Forest and Willow and Whitehorse watersheds of Oregon, the Watershed Erosion Prediction Project (WEPP) hydrologic model was applied to simulate stream permanence from 2011-2017. On 40% of PNW headwater streams no MWBM parameter sets were greater than 65% accurate when compared to observations. On 60% of headwater streams the MWBM dynamically simulated stream permanence with varying precision. Additional data would encourage and inform further model development. WEPP simulations produced daily stream permanence accuracies up to 93% and annual stream permanence accuracies up to 87% but different parameter sets performed better for daily and annual time steps. These results indicate that, when implemented for stream permanence simulation, assessment of physically-based models should include both daily and annual accuracies. Additionally, future stream permanence data collection methods and methodologies should be strategically planned to capture the spatiotemporal dynamics of stream permanence that are required to effectively develop and evaluate models.





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