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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.