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The protection of headwater streams faces increasing challenges, exemplified by limited global recognition of headwater contributions to watershed resiliency and a recent US Supreme Court decision limiting federal safeguards. Despite accounting for ~77% of global river networks, the lack of adequate headwaters protections is caused, in part, by limited information on their extent and functions—in particular, their flow regimes, which form the foundation for decision-making regarding their protection. Yet, headwater streamflow is challenging to comprehensively measure and model; it is highly variable and sensitive to changes in land use, management and climate. Modelling headwater streamflow to quantify its cumulative contributions to downstream river networks requires an integrative understanding across local hillslope and channel (that is, watershed) processes. Here we begin to address this challenge by proposing a consistent definition for headwater systems and streams, evaluating how headwater streamflow is characterized and advocating for closing gaps in headwater streamflow data collection, modelling and synthesis. 

Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging‐derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub‐watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5‐m stream sub‐reach across 426 sub‐watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub‐watersheds that contained training data and decreased to 0.67 for sub‐watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection. 

Abstract Modeled stream discharge is often used to drive sediment transport models across channel networks. Because sediment transport varies non‐linearly with flow rates, discharge modeled from daily total precipitation distributed evenly over 24‐hr may significantly underestimate actual bedload transport capacity. In this study, we assume bedload transport capacity determined from a hydrograph resulting from the use of hourly (1‐hr) precipitation is a close approximation of actual transport capacity and quantify the error introduced into a network‐scale bedload transport model driven by daily precipitation at channel network locations varying from lowland pool‐riffle channels to upland colluvial channels in a watershed where snow accumulation and melt can affect runoff processes. Transport capacity is determined using effective stresses and the Wilcock and Crowe (2003) equations and expressed in terms of transport capacity normalized by the bankfull value. We find that, depending on channel network location, cumulative error can range from 10% to more than two orders of magnitude. Surprisingly, variation in flow rates due to differences in hillslope and channel runoff do not seem to dictate the network locations where the largest errors in predicted bedload transport capacity occur. Rather, spatial variability of the magnitude of the effective‐bankfull‐excess shear stress and changes in runoff due to snow accumulation and melt exert the greatest influence. These findings have implications for flood‐hazard and aquatic habitat models that rely on modeled sediment transport driven by coarse‐temporal‐resolution climate data. 

Abstract Intermittent and ephemeral streams in dryland environments support diverse assemblages of aquatic and terrestrial life. Understanding when and where water flows provide insights into the availability of water, its response to external controlling factors, and potential sensitivity to climate change and a host of human activities. Knowledge regarding the timing of drying/wetting cycles can also be useful to map critical habitats for species and ecosystems that rely on these temporary water sources. However, identifying the locations and monitoring the timing of streamflow and channel sediment moisture remains a challenging endeavor. In this paper, we analyzed daily conductivity from 37 sensors distributed along 10 streams across an arid mountain front in Arizona (United States) to assess spatiotemporal patterns in flow permanence, defined as the timing and extent of water in streams. Conductivity sensors provide information on surface flow and sediment moisture, supporting a stream classification based on seasonal flow dynamics. Our results provide insight into flow responses to seasonal rainfall, highlighting stream reaches very reactive to rainfall versus those demonstrating more stable streamflow. The strength of stream responses to precipitation are explored in the context of surficial geology. In summary, conductivity data can be used to map potential stream habitat for water‐dependent species in both space and time, while also providing the basis upon which sensitivity to ongoing climate change can be evaluated.