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Abstract The seasonal behavior of fluvial dissolved silica (DSi) concentrations, termedDSi regime, mediates the timing of DSi delivery to downstream waters and thus governs river biogeochemical function and aquatic community condition. Previous work identified five distinct DSi regimes across rivers spanning the Northern Hemisphere, with many rivers exhibiting multiple DSi regimes over time. Several potential drivers of DSi regime behavior have been identified at small scales, including climate, land cover, and lithology, and yet the large‐scale spatiotemporal controls on DSi regimes have not been identified. We evaluate the role of environmental variables on the behavior of DSi regimes in nearly 200 rivers across the Northern Hemisphere using random forest models. Our models aim to elucidate the controls that give rise to (a) average DSi regime behavior, (b) interannual variability in DSi regime behavior (i.e., Annual DSi regime), and (c) controls on DSi regime shape (i.e., minimum and maximum DSi concentrations). Average DSi regime behavior across the period of record was classified accurately 59% of the time, whereas Annual DSi regime behavior was classified accurately 80% of the time. Climate and primary productivity variables were important in predicting Average DSi regime behavior, whereas climate and hydrologic variables were important in predicting Annual DSi regime behavior. Median nitrogen and phosphorus concentrations were important drivers of minimum and maximum DSi concentrations, indicating that these macronutrients may be important for seasonal DSi drawdown and rebound. Our findings demonstrate that fluctuations in climate, hydrology, and nutrient availability of rivers shape the temporal availability of fluvial DSi. 

Autotrophic and heterotrophic microbes in stream biofilms dominate biogeochemical cycling and rely on nutrient and energy resources for growth and productivity. In the boreal forest, variation in these resources can originate from permafrost distribution and controls competition for nutrients between stream autotrophs and heterotrophs. We investigated which resources control nutrient uptake and metabolism in headwater stream biofilms of subarctic Alaska, USA, and how resource availability affects competition for inorganic nutrients. We hypothesized that the competitive outcome between autotrophs and heterotrophs for inorganic nutrients would be dependent on availability of organic C, or inorganic nutrients (N and P). To test our hypotheses, we measured resource limitation at the patch and reach scales along a permafrost gradient in interior Alaska. At the patch scale, nutrient diffusing substrata revealed that, secondary to light, N and P were colimiting to autotrophic growth, whereas C was primarily limiting to heterotrophic respiration. In the presence of labile C, heterotrophs exhibited a larger response to nutrient enrichment and outcompeted autotrophs for inorganic nutrients. At the reach scale, light availability had the largest influence on nutrient uptake, but inorganic nutrients were also important. The positive response to increased nutrient and C availability at the patch scale suggests that the predicted increase in exports into fluvial networks with permafrost degradation will alter biofilm structure and function. Ultimately, biofilm communities will shift to more heterotroph-dominated patches if heterotrophs outcompete autotrophs for inorganic nutrients. As permafrost thaws and nutrients and organic C mobilize into streams, nutrient uptake dynamics and competition within biofilms will be altered, affecting nutrient use and export. 

Real-time monitoring usingin-situsensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data fromin-situsensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive–moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly. 

Riverine silicon (Si) plays a vital role in governing primary production, water quality, and carbon sequestration. The Global Aggregation of Stream Silica (GlASS) database was constructed to assess changes in riverine Si concentrations and fluxes, their relationship to available nutrients, and to evaluate mechanisms driving these patterns. GlASS includes dissolved Si (DSi), dissolved inorganic nitrogen, and dissolved inorganic phosphorus concentrations at daily to quarterly time steps, daily discharge, and watershed characteristics for rivers with drainage areas ranging < 1 km2 to 3 million km2 and spanning eight climate zones, mainly in the northern hemisphere. Data range between years 1963 and 2023. GlASS uses publicly available datasets, ensuring transparency and reproducibility. Original data sources are cited, data quality assurance workflows are public, and input files to a common load estimator are provided. 

Key Points We compared tools for describing streamflow timeseries, including streamflow metrics, wavelet, and Fourier analysis Each method indicated streamflow data are structured: variability at short timescales is negatively correlated with long timescales Globally, dams were less correlated with streamflow regime than catchment size and climate were 

These data include dissolved silicon concentration and yield from 60 rivers across North America, the Caribbean, and Antarctica from 1964-2021 and are associated with the publication “Long-term change in concentration and yield of riverine dissolved silicon from the poles to the tropics”. Data were compiled from multiple public sources including the Long-term Ecological Research Network, Great Arctic Rivers Observatory, Upper Mississippi River Restoration program, and the U.S. Geological Survey. Concentration and yield estimates were generated by the Weighted Regressions on Time, Discharge and Season model (WRTDS; Hirsch et al. 2010). The dataset includes six files: discrete dissolved silicon data and daily discharge data used as inputs to WRTDS; annual estimates of discharge, concentration, and yield for all rivers; monthly estimates of discharge, concentration, and yield for all rivers; long-term trends in concentration and yield; and a file containing coordinates and drainage area information for each site. 

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems. 

Abstract Forest and freshwater ecosystems are tightly linked and together provide important ecosystem services, but climate change is affecting their species composition, structure, and function. Research at nine US Long Term Ecological Research sites reveals complex interactions and cascading effects of climate change, some of which feed back into the climate system. Air temperature has increased at all sites, and those in the Northeast have become wetter, whereas sites in the Northwest and Alaska have become slightly drier. These changes have altered streamflow and affected ecosystem processes, including primary production, carbon storage, water and nutrient cycling, and community dynamics. At some sites, the direct effects of climate change are the dominant driver altering ecosystems, whereas at other sites indirect effects or disturbances and stressors unrelated to climate change are more important. Long-term studies are critical for understanding the impacts of climate change on forest and freshwater ecosystems. 

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.