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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 Machine‐learning models have been surprisingly successful at predicting stream solute concentrations, even for solutes without dedicated sensors. It would be extremely valuable if these models could predict solute concentrations in streams beyond the one in which they were trained. We assessed the generalisability of random forest models by training them in one or more streams and testing them in another. Models were made using grab sample and sensor data from 10 New Hampshire streams and rivers. As observed in previous studies, models trained in one stream were capable of accurately predicting solute concentrations in that stream. However, models trained on one stream produced inaccurate predictions of solute concentrations in other streams, with the exception of solutes measured by dedicated sensors (i.e., nitrate and dissolved organic carbon). Using data from multiple watersheds improved model results, but model performance was still worse than using the mean of the training dataset (Nash–Sutcliffe Efficiency < 0). Our results demonstrate that machine‐learning models thus far reliably predict solute concentrations only where trained, as differences in solute concentration patterns and sensor‐solute relationships limit their broader applicability. 

Abstract Climate and atmospheric deposition interact with watershed properties to drive dissolved organic carbon (DOC) concentrations in lakes. Because drivers of DOC concentration are inter-related and interact, it is challenging to assign a single dominant driver to changes in lake DOC concentration across spatiotemporal scales. Leveraging forty years of data across sixteen lakes, we used structural equation modeling to show that the impact of climate, as moderated by watershed characteristics, has become more dominant in recent decades, superseding the influence of sulfate deposition that was observed in the 1980s. An increased percentage of winter precipitation falling as rain was associated with elevated spring DOC concentrations, suggesting a mechanistic coupling between climate and DOC increases that will persist in coming decades as northern latitudes continue to warm. Drainage lakes situated in watersheds with fine-textured, deep soils and larger watershed areas exhibit greater variability in lake DOC concentrations compared to both seepage and drainage lakes with coarser, shallower soils, and smaller watershed areas. Capturing the spatial variability in interactions between climatic impacts and localized watershed characteristics is crucial for forecasting lentic carbon and nutrient dynamics, with implications for lake ecology and drinking water quality. 

Abstract Dissolved organic matter (DOM) concentrations and composition within wet deposition are rarely monitored despite contributing a large input of bioavailable dissolved organic carbon (DOC) and nitrogen (DON) to the Earth's surface. Lacking from the literature are spatially comprehensive assessments of simultaneous measurements of wet deposition DOC and DON chemistry and their dependencies on metrics of climate and environmental factors. Here, we use archived precipitation samples from the US National Atmospheric Deposition Program collected in 2017 to 2018 from 17 sites across six ecoregions to investigate variability in the concentration and composition of depositional DOM. We hypothesize metrics of DOM chemistry vary with ecoregion, season, large‐scale climate drivers, and precipitation geographic source. Findings indicate differences in DOC and DON concentrations and loads among ecoregions. The highest wet deposition concentrations are from sites in the Northern Forests and lowest concentrations from sites in Marine West Coast Forests. Summer and autumn samples contained the highest DOC concentrations and DON concentrations that were consistently above detection limit, corresponding with seasonality of peak air temperatures and the phenology of the growing season in the northern hemisphere. Compositional trends suggest lighter DOM molecules in autumn and winter and heavier molecules in spring and summer. Climate drivers explain 51% of variation in DOM chemistry, revealing differing drivers on the concentrations and loads of DOC versus DON in wet deposition. This study highlights the necessity of incorporating DOC and DON measurements into national deposition monitoring networks to understand spatial and temporal feedbacks between climate change, atmospheric chemistry and landscape biogeochemistry. 

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. 

Abstract Nitrogen (N) wet deposition chemistry impacts watershed biogeochemical cycling. The timescale and magnitude of (a)synchrony between wet deposition N inputs and watershed N outputs remains unresolved. We quantify deposition‐river N (a)synchrony with transfer entropy (TE), an information theory metric enabling quantification of lag‐dependent feedbacks in a hydrologic system by calculating directional information flow between variables. Synchrony is defined as a significant amount of TE‐calculated reduction in uncertainty of river N from wet deposition N after conditioning for antecedent river N conditions. Using long‐term timeseries of wet deposition and river DON, NO₃⁻, and NH₄⁺concentrations from the Lamprey River watershed, New Hampshire (USA), we constrain the role of wet deposition N to watershed biogeochemistry. Wet deposition N contributed information to river N at timescales greater than quick‐flow runoff generation, indicating that river N losses are a lagged non‐linear function of hydro‐biogeochemical forcings. River DON received the most information from all three wet deposition N solutes while wet deposition DON and NH₄⁺contributed the most information to all three river N solutes. Information theoretic algorithms facilitated data‐driven inferences on the hydro‐biogeochemical processes influencing the fate of N wet deposition. For example, signals of mineralization and assimilation at a timescale of 12 to 21‐weeks lag display greater synchrony than nitrification, and we find that N assimilation is a positive lagged function of increasing N wet deposition. Although wet deposition N is not the main driver of river N, it contributes a significant amount of information resolvable at time scales of transport and transformations. 

Abstract Freshwater ecosystems reflect the landscapes in which they are embedded. The biogeochemistry of these systems is fundamentally linked to climate and watershed processes that control fluxes of water and the mobilization of energy and nutrients imprinting as variation in stream water chemistry. Disentangling these processes is difficult as they operate at multiple scales varying across space. We examined the relative importance of climate, soil, and watershed characteristics in mediating direct and indirect pathways that influence carbon and nitrogen availability in streams and rivers across spatial scales. Our data set comprised landscape and climatic variables and 37,995 chemistry measurements of carbon and nitrogen across 459 streams and rivers spanning the continental United States. Models explained a small fraction of carbon and nitrogen concentrations at the continental scale (25% and 6%, respectively) but 61% and 40%, respectively, at smaller spatial scales. Hydrometeorological processes were always important in mediating the availability of solutes but the mechanistic implications were variable across spatial scales. The influence of hydrometeorology on concentrations was often not direct, rather it was mediated by soil characteristics for carbon and watershed characteristics for nitrogen. For example, the seasonality of precipitation was often important in determining carbon concentrations through its influence on soil moisture at biogeoclimatic spatial scales, whereas it had a direct influence on concentrations at the continental scale. Our results suggest that hydrometeorological forcing remains the consistent driver of energy and nutrient concentrations but the mechanism influencing patterns varies across broad spatial scales. 

Abstract Fluvial silicon (Si) plays a critical role in controlling primary production, water quality, and carbon sequestration through supporting freshwater and marine diatom communities. Geological, biogeochemical, and hydrological processes, as well as climate and land use, dictate the amount of Si exported by streams. Understanding Si regimes—the seasonal patterns of Si concentrations—can help identify processes driving Si export. We analyzed Si concentrations from over 200 stream sites across the Northern Hemisphere to establish distinct Si regimes and evaluated how often sites moved among regimes over their period of record. We observed five distinct regimes across diverse stream sites, with nearly 60% of sites exhibiting multiple regime types over time. Our results indicate greater spatial and interannual variability in Si seasonality than previously recognized and highlight the need to characterize the watershed and climate variables that affect Si cycling across diverse ecosystems. 

Abstract Processes that drive variability in catchment solute sourcing, transformation, and transport can be investigated using concentration–discharge (C–Q) relationships. These relationships reflect catchment and in‐stream processes operating across nested temporal scales, incorporating both short and long‐term patterns. Scientists can therefore leverage catchment‐scale C–Q datasets to identify and distinguish among the underlying meteorological, biological, and geological processes that drive solute export patterns from catchments and influence the shape of their respective C–Q relationships. We have synthesized current knowledge regarding the influence of biological, geological, and meteorological processes on C–Q patterns for various solute types across diel to decadal time scales. We identify cross‐scale linkages and tools researchers can use to explore these interactions across time scales. Finally, we identify knowledge gaps in our understanding of C–Q temporal dynamics as reflections of catchment and in‐stream processes. We also lay the foundation for developing an integrated approach to investigate cross‐scale linkages in the temporal dynamics of C–Q relationships, reflecting catchment biogeochemical processes and the effects of environmental change on water quality. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water QualityScience of Water > Water and Environmental Change 

Abstract Elevated salt concentrations in streams draining developed watersheds are well documented, but the effects of hydrologic variability and the role of groundwater in surface water salinization are poorly understood. To characterize these effects, we use long‐term data (12–19 yr) and high‐frequency specific conductance (SPC) data collected from 13 streams across New Hampshire, USA. Concentration–discharge (C–Q) relationships for chloride (Cl⁻) derived from high‐frequency SPC showed distinct seasonal variability. Diluting behavior was common, but flushing behavior occurred in autumn and winter, suggesting that both groundwater and surface runoff contribute salts to streams. Long‐term data show that although extreme flood events initially reduced salt concentrations in groundwater and rural streams, concentrations recovered to preflood conditions in about a decade. Chronic Cl⁻exceedances occurred in urban streams during all seasons. This research suggests that variation in stream flow, extreme events and application of deicing agents play a role in freshwater salinization.