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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 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 Nitrous oxide (N₂O) reductase, the sole natural microbial sink for N₂O, exists in two microbial clades:nosZI andnosZII. Although previous studies have explored inter‐clade ecological differentiation, the intra‐clade variations and their implications for N₂O dynamics remain understudied. This study investigated both inter‐ and intra‐clade ecological differentiation among N₂O reducers, the drivers influencing these patterns, and their effects on N₂O emissions across continental‐scale river systems. The results showed that bothnosZI andnosZII community turnovers were associated with similar key environmental factors, particularly total phosphorus (TP), but these variables explained a larger proportion of variation in thenosZI community. The influence of mean annual temperature (MAT) on community composition increased for more widespread N₂O‐reducing taxa. We identified distinct ecological clusters within each clade of N₂O reducers and observed identical ecological clustering patterns across both clades. These clusters were primarily characterized by distinct MAT regimes, coarse sediment texture as well as low TP levels, and high abundance of N₂O producers, with MAT‐related clusters constituting predominant proportions. Intra‐clade ecological differentiation was a crucial predictor of N₂O flux and reduction efficiency. Although different ecological clusters showed varying or even contrasting associations with N₂O dynamics, the shared ecological clusters across clades exhibited similar trends. Low‐MAT clusters in both thenosZI andnosZII communities were negatively correlated with denitrification‐normalized N₂O flux and the N₂O:(N₂O + N₂) ratio, whereas high‐MAT clusters showed positive correlations. This contrasting pattern likely stems from low‐MAT clusters being better adapted to eutrophic conditions and their more frequent co‐occurrence with N₂O‐producing genes. These findings advance our understanding of the distribution and ecological functions of N₂O reducers in natural ecosystems, suggesting that warming rivers may have decreased N₂O reduction efficiency and thereby amplify temperature‐driven emissions. 

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 River networks play a crucial role in the global carbon cycle, as relevant sources of carbon dioxide (CO₂) to the atmosphere. Advancements in high‐frequency monitoring in aquatic environments have enabled measurement of dissolved CO₂concentration at temporal resolutions essential for studying carbon variability and evasion from these dynamic ecosystems. Here, we describe the adaptation, deployment, and validation of an open‐source and relatively low‐cost in situpCO₂sensor system for lotic ecosystems, the lotic‐SIPCO2. We tested the lotic‐SIPCO2 in 10 streams that spanned a range of land cover and basin size. Key system adaptations for lotic environments included prevention of biofouling, configuration for variable stage height, and reduction of headspace equilibration time. We then examined which input parameters contribute the most to uncertainty in estimating CO₂emission rates and found scaling factors related to the gas exchange velocity were the most influential when CO₂concentration was significantly above saturation. Near saturation, sensor measurement ofpCO₂contributed most to uncertainty in estimating CO₂emissions. We also found high‐frequency measurements ofpCO₂were not necessary to accurately estimate median emission rates given the CO₂regimes of our streams, but daily to weekly sampling was sufficient. High‐frequency measurements ofpCO₂remain valuable for exploring in‐stream metabolic variability, source partitioning, and storm event dynamics. Our adaptations to the SIPCO2 offer a relatively affordable and robust means of monitoring dissolved CO₂in lotic ecosystems. Our findings demonstrate priorities and related considerations in the design of monitoring projects of dissolved CO₂and CO₂evasion dynamics more broadly. 

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 Riverine exports of silicon (Si) influence global carbon cycling through the growth of marine diatoms, which account for ∼25% of global primary production. Climate change will likely alter river Si exports in biome‐specific ways due to interacting shifts in chemical weathering rates, hydrologic connectivity, and metabolic processes in aquatic and terrestrial systems. Nonetheless, factors driving long‐term changes in Si exports remain unexplored at local, regional, and global scales. We evaluated how concentrations and yields of dissolved Si (DSi) changed over the last several decades of rapid climate warming using long‐term data sets from 60 rivers and streams spanning the globe (e.g., Antarctic, tropical, temperate, boreal, alpine, Arctic systems). We show that widespread changes in river DSi concentration and yield have occurred, with the most substantial shifts occurring in alpine and polar regions. The magnitude and direction of trends varied within and among biomes, were most strongly associated with differences in land cover, and were often independent of changes in river discharge. These findings indicate that there are likely diverse mechanisms driving change in river Si biogeochemistry that span the land‐water interface, which may include glacial melt, changes in terrestrial vegetation, and river productivity. Finally, trends were often stronger in months outside of the growing season, particularly in temperate and boreal systems, demonstrating a potentially important role of shifting seasonality for the flux of Si from rivers. Our results have implications for the timing and magnitude of silica processing in rivers and its delivery to global oceans. 

Stream metabolism, encompassing gross primary production and ecosystem respiration, reflects the fundamental energetic dynamics of freshwater ecosystems. These processes regulate the concentrations of dissolved gases like oxygen and carbon dioxide, which in turn shape aquatic food webs and ecosystem responses to stressors such as floods, drought, and nutrient loading. Historically difficult to quantify, stream metabolism is now measurable at high temporal resolution thanks to advances in sensor technology and modeling. The StreamPULSE dataset includes high-frequency sensor data, metadata, and modeled estimates of ecosystem metabolism. This living dataset contributes to a growing body of open-access data characterizing the metabolic pulse of stream ecosystems worldwide. To contribute to StreamPULSE, visit data.streampulse.org. All data contributed to StreamPULSE become public after an optional embargo period. Use this publication to access annual data releases, or use data.streampulse.org to download new data as they become available.