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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 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. 

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 Ethical guidelines have provided a cornerstone for morally appropriate research on human or other vertebrate animal subjects since at least 1945. By contrast, although there are environmental impacts associated with all science research activities (including field, laboratory, and computational projects), no comprehensive guiding framework to determine environmentally responsible research practices has been proposed. Drawing from existing models within social, medical, and animal sciences, we propose a framework for explicitly incorporating environmentally focused ethics into scientific research. The Environmental Responsibility 5‐R Framework (ER⁵F) is centered around Recognition, Refinement, Reduction, Replacement, and Restoration. ER⁵F starts with Recognizing that research can have environmental consequences, while each subsequent “R” serves as an opportunity for acknowledging, evaluating, and mitigating the environmental impacts of scientific research. These R's include: Refining research questions, Reducing the resources and energy consumed, Replacing materials with sustainable options and altering methods, and in the case of field research, Restoring an environment to mitigate any harm done. By introducing this novel and approachable framework, we strive to promote enhanced awareness across the entire scientific community by encouraging researchers to recognize their responsibility and identify potential mitigation opportunities for the environmental consequences of their research activities. We affirm that in doing so, scientists can more effectively balance the dual goals of maximizing their novel research outputs while minimizing possible harm to the environment.