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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 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 Lithium isotopes are used to trace weathering intensity, but little is known about the processes that fractionate them in highly weathered settings, where secondary minerals play a dominant role in weathering reactions. To help fill this gap in our knowledge of Li isotope systematics, we investigated Li isotope fractionation at an andesitic catchment in Puerto Rico, where the highest rates of silicate weathering on Earth have been documented. We found the lowest δ⁷Li values published to date for porewater (−27‰) and bulk regolith (−38‰), representing apparent fractionations relative to parent rock of −31‰ and −42‰, respectively. We also found δ⁷Li values that are lower in the exchangeable fraction than in the bulk regolith or porewater, the opposite than expected from secondary mineral precipitation. We interpret these large isotopic offsets and the unusual relationships between Li pools as resulting from two distinct weathering processes at different depths in the regolith. At the bedrock‐regolith transition (9.3–8.5 m depth), secondary mineral precipitation preferentially retains the lighter⁶Li isotope. These minerals then dissolve further up the profile, leaching⁶Li from the bulk solid, with a total variation of about +50‰withinthe profile, attributable primarily to clay dissolution. Importantly, streamwater δ⁷Li (about +35‰) is divorced entirely from these regolith weathering processes, instead reflecting deeper weathering reactions (>9.3 m). Our work thus shows that the δ⁷Li of waters draining highly weathered catchments may reflect bedrock mineralogy and hydrology, rather than weathering intensity in the regolith covering the catchment. 

Abstract The Lamprey River Hydrological Observatory (LRHO) is a lowland coastal watershed in southeastern New Hampshire (USA). The LRHO offers a platform to investigate the effects of suburbanization and changing seasonality on watershed hydrology, biogeochemistry, and nutrient export to an estuarine ecosystem. The LRHO utilizes a nested‐watershed design to examine headwater stream and main‐stem river dynamics distributed across a mixed land‐use environment. Data sets from the LRHO now comprise over 20 years of weekly grab sample data as well as 7 years of high‐frequency sensor data. Collectively these data sets include measures of discharge, dissolved organic matter, nutrients, cations and anions, greenhouse gases, and other physio‐chemical properties. Here we share information on the setting and motivating questions of the LRHO and data availability. 

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 Climate change is altering the timing and duration of the vernal window, a period that marks the end of winter and the start of the growing season when rapid transitions in ecosystem energy, water, nutrient, and carbon dynamics take place. Research on this period typically captures only a portion of the ecosystem in transition and focuses largely on the dates by which the system wakes up. Previous work has not addressed lags between transitions that represent delays in energy, water, nutrient, and carbon flows. The objectives of this study were to establish the sequence of physical and biogeochemical transitions and lags during the vernal window period and to understand how climate change may alter them. We synthesized observations from a statewide sensor network in New Hampshire,USA, that concurrently monitored climate, snow, soils, and streams over a three‐year period and supplemented these observations with climate reanalysis data, snow data assimilation model output, and satellite spectral data. We found that some of the transitions that occurred within the vernal window were sequential, with air temperatures warming prior to snow melt, which preceded forest canopy closure. Other transitions were simultaneous with one another and had zero‐length lags, such as snowpack disappearance, rapid soil warming, and peak stream discharge. We modeled lags as a function of both winter coldness and snow depth, both of which are expected to decline with climate change. Warmer winters with less snow resulted in longer lags and a more protracted vernal window. This lengthening of individual lags and of the entire vernal window carries important consequences for the thermodynamics and biogeochemistry of ecosystems, both during the winter‐to‐spring transition and throughout the rest of the year.