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Abstract Evaluating stream water chemistry patterns provides insight into catchment ecosystem and hydrologic processes. Spatially distributed patterns and controls of stream solutes are well‐established for high‐relief catchments where solute flow paths align with surface topography. However, the controls on solute patterns are poorly constrained for low‐relief catchments where hydrogeologic heterogeneities and river corridor features, like wetlands, may influence water and solute transport. Here, we provide a data set of solute patterns from 58 synoptic surveys across 28 sites and over 32 months in a low‐relief wetland‐rich catchment to determine the major surface and subsurface controls along with wetland influence across the catchment. In this low‐relief catchment, the expected wetland storage, processing, and transport of solutes is only apparent in solute patterns of the smallest subcatchments. Meanwhile, downstream seasonal and wetland influence on observed chemistry can be masked by large groundwater contributions to the main stream channel. These findings highlight the importance of incorporating variable groundwater contributions into catchment‐scale studies for low‐relief catchments, and that understanding the overall influence of wetlands on stream chemistry requires sampling across various spatial and temporal scales. Therefore, in low‐relief wetland‐rich catchments, given the mosaic of above and below ground controls on stream solutes, modeling efforts may need to include both surface and subsurface hydrological data and processes. 

Abstract Riverine silicon (Si) plays a vital role in governing primary production, water quality, and carbon cycling. Climate and land cover change have altered how dissolved Si (DSi) is processed on land, transported to rivers, and cycled through aquatic ecosystems. The Global Aggregation of Stream Silica (GlASS) database was constructed to assess changes in river Si concentrations and fluxes, their relationship to other nutrients (nitrogen (N) and phosphorus (P)), and to evaluate mechanisms driving the availability of Si. GlASS includes concentrations of DSi, dissolved inorganic N (NO₃, NO_x, and NH₄), and dissolved inorganic P (as soluble reactive P or PO₄-P) at daily to quarterly time steps from 1963 to 2024; daily discharge; and watershed characteristics for 421 rivers spanning eight climate zones. Original data sources are cited, data quality assurance workflows are public, and input files to a common load model are provided. GlASS offers critical data to address questions about patterns, controls, and trajectories of global river Si biogeochemistry and stoichiometry. 

ABSTRACT Non‐perennial streams are globally prevalent. These streams are vital components of ecosystems, yet their drying patterns and resulting impacts on hydrologic connectivity remain poorly understood at the watershed scale. Aridity is a dominant driver of stream drying, but its influences on hydrologic connectivity have not been fully explored. In this study, we investigated the role of aridity in shaping streamflow and connectivity patterns in non‐perennial stream networks that span the continental United States aridity gradient. Using hydrologic models, we simulated daily streamflow and stream network connectivity under current and future climate scenarios. Our findings support previous research showing that aridity and streamflow are strongly linked. We also found that connectivity was related to aridity, although this relationship was weaker. Under the future climate scenario, mean runoff increased in most watersheds in the future, while mean connectivity decreased in the majority of watersheds. This difference is an indicator of the complex relationship between streamflow and connectivity. Aridity was a strong predictor of changes in very high and very low connectivity periods that resulted from climate change, but aridity did not predict changes in mean connectivity. Arid watersheds tended to experience more high connectivity days due to climate change while humid networks tended to have more low connectivity days. By modelling climate impacts at the network scale and across a broad hydroclimatic gradient, we highlight the importance of considering context‐dependent changes in network connectivity in river flow management and watershed conservation plans. 

Abstract Climate change is rapidly altering hydrological processes and consequently the structure and functioning of Arctic ecosystems. Predicting how these alterations will shape biogeochemical responses in rivers remains a major challenge. We measured [C]arbon and [N]itrogen concentrations continuously from two Arctic watersheds capturing a wide range of flow conditions to assess understudied event‐scale C and N concentration‐discharge (C‐Q) behavior and post‐event recovery of stoichiometric conditions. The watersheds represent low‐gradient, tundra landscapes typical of the eastern Brooks Range on the North Slope of Alaska and are part of the Arctic Long‐Term Ecological Research sites: the Kuparuk River and Oksrukuyik Creek. In both watersheds, we deployed high‐frequency optical sensors to measure dissolved organic carbon (DOC), nitrate (), and total dissolved nitrogen (TDN) for five consecutive thaw seasons (2017–2021). Our analyses revealed a lag in DOC: stoichiometric recovery after a hydrologic perturbation: while DOC was consistently elevated after high flows, diluted during rainfall events and consequently, recovery in post‐event concentration was delayed. Conversely, the co‐enrichment of TDN at high flows, even in watersheds with relatively high N‐demand, represents a potential “leak” of hydrologically available organic N to downstream ecosystems. Our use of high‐frequency, long‐term optical sensors provides an improved method to estimate carbon and nutrient budgets and stoichiometric recovery behavior across event and seasonal timescales, enabling new insights and conceptualizations of a changing Arctic, such as assessing ecosystem disturbance and recovery across multiple timescales. 

Key Points We re‐evaluate equations proposed by Francis Hall to assess concentration‐discharge ( C ‐ Q ) relationships using newly available long‐term and high‐frequency data sets Across time steps we find that log‐log and log‐linear models perform equally well to describe C ‐ Q relationships Parametrization of storage‐discharge relationships via recession analyses provides additional insight to C ‐ Q relationships 

Abstract Studies of stream macroinvertebrates traditionally use sampling methods that target benthic habitats. These methods could underestimate biodiversity if important assemblage components exist outside of the benthic zone. To test the efficacy of different sampling methods, we collected paired reach‐wide benthic and edge samples from up to 10 study reaches in nine basins spanning an aridity gradient across the United States. Edge sampling targeted riparian‐adjacent microhabitats not typically sampled, including submerged vegetation, roots, and overhanging banks. We compared observed richness, asymptotic richness, and assemblage dissimilarity between benthic samples alone and different combinations of benthic and edge samples to determine the magnitude of increased diversity and assemblage dissimilarity values with the addition of edge sampling. We also examined how differences in richness and assemblage composition varied across an aridity gradient. The addition of edge sampling significantly increased observed richness (median increase = 29%) and asymptotic richness (median increase = 173%). Similarly, median Bray–Curtis dissimilarity values increased by as much as 0.178 when benthic and edge samples were combined. Differences in richness metrics were generally higher in arid basins, but assemblage dissimilarity either increased or decreased across the aridity gradient depending on how benthic and edge samples were combined. Our results suggest that studies that do not sample stream edges may significantly underestimate reach diversity and misrepresent assemblage compositions, with effects that can vary across climates. We urge researchers to carefully consider sampling methods in field studies spanning climatic zones and the comparability of existing data sets when conducting data synthesis studies. 

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.