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Abstract. Air–water gas exchange is essential to understanding and quantifying many biogeochemical processes in streams and rivers, including greenhouse gas emissions and metabolism. Gas exchange depends on two factors, which are often quantified separately: (1) the air–water concentration gradient of the gas and (2) the gas exchange velocity.  There are fewer measurements of gas exchange velocity compared to concentrations in streams and rivers, which limits accurate characterization of air–water gas exchange (i.e., flux rates). The National Ecological Observatory Network (NEON) conducts SF6 gas-loss experiments in 22 of their 24 wadeable streams using standardized methods across all experiments and sites, and publishes raw concentration data from these experiments on the NEON data portal. NEON also conducts NaCl injections that can be used to characterize hydraulic geometry at all 24 wadeable streams. These NaCl injections are conducted both as part of the gas-loss experiments and separately. Here, we use these data to estimate gas exchange and water velocity using the reaRate R package. The dataset presented includes estimates of hydraulic parameters, cleaned raw concentration SF6 tracer-gas data (including removing outliers and failed experiments), estimated SF6 gas-loss rates, normalized gas exchange velocities (k600; m d−1) and normalized depth-dependent gas exchange rates (K600; d−1). This dataset provides one of the largest compilations of gas-loss experiments (n=339) in streams to date. This dataset is unique in that it contains gas exchange estimates from repeated experiments in geographically diverse streams across a range of discharges. In addition, this dataset contains information on the hydraulic geometry of all 24 NEON wadeable streams, which will support future research using NEON aquatic data. This dataset is a valuable resource that can be used to explore both within- and across-reach variability in the hydraulic geometry and gas exchange velocity in streams. The data are available at https://doi.org/10.6073/pasta/18dcc1871ee71cf0b69f2ee4082839d0 (Aho et al., 2024), and the reaRate R package code is available at https://doi.org/10.5281/zenodo.12786089 (Cawley et al., 2024). 

Abstract Headwater stream networks contribute substantially to the global carbon dioxide terrestrial flux because of high turbulence and coupling with terrestrial environments. Heterogeneity within headwater stream networks, both spatially and temporally, makes measuring and upscaling these emissions challenging because measurements of carbon dioxide in streams are often limited to a few monitoring points. We modified a stream network model to reflect real measurements made under base flow and high flow conditions at Martha Creek in Stabler, WA in the US Pacific Northwest. We found that under high flow conditions, the stream network had much greater total carbon emissions than during low flow conditions (1.22 Mg C day⁻¹vs. 0.034 Mg C day⁻¹). We attribute this increase to a larger overall stream network area (0.04 vs. 0.01 km²) and discharge (1.9 m³ s⁻¹vs. 0.005 m³ s⁻¹) in November versus August. Our results demonstrate the need to understand the nonperennial stream reaches when calculating carbon emissions. We compared the stream network emissions with the terrestrial net ecosystem exchange (NEE) estimated by local eddy covariance measurements per watershed area (−5.5 Mg C day⁻¹in August and −2.2 Mg C day⁻¹in November). Daily stream emissions in November accounted for a much larger percentage of NEE than in August (54% vs. 0.62%). We concluded that the stream network can emit a large percentage of the forest NEE in the winter months, and annual estimates of stream network emissions must consider the flow regime throughout the year. 

Abstract Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways towards mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.