Nitrogen wet deposition stoichiometry: the role of organic nitrogen, seasonality, and snow

Murray, Desneiges S.; Shattuck, Michelle D.; McDowell, W. H.; Wymore, Adam S.

doi:10.1007/s10533-022-00966-0

Cited by 14 publications

(12 citation statements)

References 47 publications

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“…The watershed is primarily forested (73%), but agriculture (5%), wetlands (10%), and developed areas (7%) are also present (Wymore et al., 2021). During the study period, the mean annual air temperature was 9.2 ± 0.8°C and the site received an average of 127 ± 6 cm of precipitation per year, with 2%–16% falling as snow (Murray et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

“…Future studies could aggregate dormant and growing seasons by an indicator variable such as leaf-out date or air temperature, which would allow for the seasonal signal to reflect changes in phenology and whether phenological variability drives currently synchronous phases to become a-synchronous (Seybold, Fork, et al, 2022). Importantly, our application of TE to long-term wet deposition-river timeseries assumed temporal stationarity across the data record, despite regional trends of declining wet deposition inorganic N and increases in DON (Murray et al, 2022). TE calculations are run on weekly anomalies, which destroys temporal variability, thus it is likely that signals between deposition and river N would be stronger were we able to account for long-term trends.…”

Section: Implications Of Temporal Stationaritymentioning

confidence: 99%

“…The raw wet deposition and river chemistry, discharge, and precipitation data used for supporting the results presented in this paper are openly available on Hydroshare at https://doi.org/10.4211/hs.0d123f789d-3944f7a32aedc1fd4ea2e5 (Murray et al, 2023). The scripts used to run information theory algorithms were accessed and modified with permission from and Moges, Ruddell, Zhang, Driscoll, Norton, et al (2022).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…While wet deposition chemistry can impart significant long‐term influence on surface water chemistry (Aber et al., 2003; Monteith et al., 2007; Murdoch & Stoddard, 1992; Newcomer et al., 2021; Templer et al., 2022), the timescales over which wet deposition N inputs are related to watershed N outputs remain unresolved. As reactive N deposition inputs change in concentration and stoichiometry due to emission policies like the Clean Air Act (Gilliam et al., 2019; Murray et al., 2022), determining the magnitude to which N wet deposition contributes to river N losses is important for managing excess N loading to receiving waterbodies.…”

Section: Introductionmentioning

confidence: 99%

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Synchrony of Nitrogen Wet Deposition Inputs and Watershed Nitrogen Outputs Using Information Theory

Murray,

Moges,

Larsen

et al. 2023

Water Resources Research

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Implications Of Temporal Stationaritymentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synchrony of Nitrogen Wet Deposition Inputs and Watershed Nitrogen Outputs Using Information Theory

Murray,

Moges,

Larsen

et al. 2023

Water Resources Research

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“…Similarly, N can accumulate in snow-covered soil and snowpack and flush out during spring snowmelt (Brooks et al, 1998;Brooks and Williams, 1999;Campbell et al, 2006;Pellerin et al, 2011). During rain-on-snow events, which are increasing in their frequency (Il Jeong and Sushama, 2018), a large amount of N can be exported in a brief period of time (Murray et al, 2022;Seybold et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Disentangling the responses of dissolved organic carbon and nitrogen concentrations to overlapping drivers in a northeastern United States forested watershed

et al. 2023

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The concurrent reduction in acid deposition and increase in precipitation impact stream solute dynamics in complex ways that make predictions of future water quality difficult. To understand how changes in acid deposition and precipitation have influenced dissolved organic carbon (DOC) and nitrogen (N) loading to streams, we investigated trends from 1991 to 2018 in stream concentrations (DOC, ~3,800 measurements), dissolved organic nitrogen (DON, ~1,160 measurements), and dissolved inorganic N (DIN, ~2,130 measurements) in a forested watershed in Vermont, USA. Our analysis included concentration-discharge (C-Q) relationships and Seasonal Mann-Kendall tests on long-term, flow-adjusted concentrations. To understand whether hydrologic flushing and changes in acid deposition influenced long-term patterns by liberating DOC and dissolved N from watershed soils, we measured their concentrations in the leachate of 108 topsoil cores of 5 cm diameter that we flushed with solutions simulating high and low acid deposition during four different seasons. Our results indicate that DOC and DON often co-varied in both the long-term stream dataset and the soil core experiment. Additionally, leachate from winter soil cores produced especially high concentrations of all three solutes. This seasonal signal was consistent with C-Q relation showing that organic materials (e.g., DOC and DON), which accumulate during winter, are flushed into streams during spring snowmelt. Acid deposition had opposite effects on DOC and DON compared to DIN in the soil core experiment. Low acid deposition solutions, which mimic present day precipitation, produced the highest DOC and DON leachate concentrations. Conversely, high acid deposition solutions generally produced the highest DIN leachate concentrations. These results are consistent with the increasing trend in stream DOC concentrations and generally decreasing trend in stream DIN we observed in the long-term data. These results suggest that the impact of acid deposition on the liberation of soil carbon (C) and N differed for DOC and DON vs. DIN, and these impacts were reflected in long-term stream chemistry patterns. As watersheds continue to recover from acid deposition, stream C:N ratios will likely continue to increase, with important consequences for stream metabolism and biogeochemical processes.

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Catchment concentration–discharge relationships across temporal scales: A review

Speir,

Rose,

Blaszczak

et al. 2023

WIREs Water

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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 Processes Science of Water > Water Quality Science of Water > Water and Environmental Change

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Nitrogen wet deposition stoichiometry: the role of organic nitrogen, seasonality, and snow

Cited by 14 publications

References 47 publications

Synchrony of Nitrogen Wet Deposition Inputs and Watershed Nitrogen Outputs Using Information Theory

Synchrony of Nitrogen Wet Deposition Inputs and Watershed Nitrogen Outputs Using Information Theory

Disentangling the responses of dissolved organic carbon and nitrogen concentrations to overlapping drivers in a northeastern United States forested watershed

Catchment concentration–discharge relationships across temporal scales: A review

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