Literature DB >> 34060819

Context is Everything: Interacting Inputs and Landscape Characteristics Control Stream Nitrogen.

Jana E Compton1, Ryan A Hill1, Alan T Herlihy1,2, Robert D Sabo3, J Renée Brooks1, Marc Weber1, Brian Pickard4, Steve G Paulsen1, John L Stoddard1.   

Abstract

To understand the environmental and anthropogenic drivers of stream nitrogen (N) concentrations across the conterminous US, we combined summer low-flow data from 4997 streams with watershed information across three survey periods (2000-2014) of the US EPA's National Rivers and Streams Assessment. Watershed N inputs explained 51% of the variation in log-transformed stream total N (TN) concentrations. Both N source and input rates influenced stream NO3/TN ratios and N concentrations. Streams dominated by oxidized N forms (NO3/TN ratio > 0.50) were more strongly responsive to the N input rate compared to streams dominated by other N forms. NO3 proportional contribution increased with N inputs, supporting N saturation-enhanced NO3 export to aquatic ecosystems. By combining information about N inputs with climatic and landscape factors, random forest models of stream N concentrations explained 70, 58, and 60% of the spatial variation in stream concentrations of TN, dissolved inorganic N, and total organic N, respectively. The strength and direction of relationships between watershed drivers and stream N concentrations and forms varied with N input intensity. Model results for high N input watersheds not only indicated potential contributions from contaminated groundwater to high stream N concentrations but also the mitigating role of wetlands.

Entities:  

Keywords:  N concentration; N inventory; N species; conterminous US; machine learning; nutrient; random forest; spatial variation; water quality

Mesh:

Substances:

Year:  2021        PMID: 34060819      PMCID: PMC8673309          DOI: 10.1021/acs.est.0c07102

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   11.357


  35 in total

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3.  Unexpected spatial stability of water chemistry in headwater stream networks.

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4.  Reduced forms of nitrogen are a driver of non-nitrogen-fixing harmful cyanobacterial blooms and toxicity in Lake Erie.

Authors:  Silvia E Newell; Timothy W Davis; Thomas H Johengen; Duane Gossiaux; Ashley Burtner; Danna Palladino; Mark J McCarthy
Journal:  Harmful Algae       Date:  2019-01-02       Impact factor: 4.273

5.  Evaluating stream water quality through land use analysis in two grassland catchments: impact of wetlands on stream nitrogen concentration.

Authors:  A Hayakawa; M Shimizu; K P Woli; K Kuramochi; R Hatano
Journal:  J Environ Qual       Date:  2006-03-01       Impact factor: 2.751

6.  Water-quality trends in U.S. rivers, 2002 to 2012: Relations to levels of concern.

Authors:  Megan E Shoda; Lori A Sprague; Jennifer C Murphy; Melissa L Riskin
Journal:  Sci Total Environ       Date:  2018-10-02       Impact factor: 7.963

7.  The contribution of wildland fire emissions to deposition in the U S: implications for tree growth and survival in the Northwest.

Authors:  Shannon N Koplitz; Christopher G Nolte; Robert D Sabo; Christopher M Clark; Kevin J Horn; R Quinn Thomas; Tamara A Newcomer-Johnson
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8.  Assessing water-quality changes in US rivers at multiple geographic scales using results from probabilistic and targeted monitoring.

Authors:  Lori A Sprague; Richard M Mitchell; Amina I Pollard; James A Falcone
Journal:  Environ Monit Assess       Date:  2019-05-04       Impact factor: 2.513

9.  Variable impacts of contemporary versus legacy agricultural phosphorus on US river water quality.

Authors:  Sarah M Stackpoole; Edward G Stets; Lori A Sprague
Journal:  Proc Natl Acad Sci U S A       Date:  2019-09-23       Impact factor: 11.205

10.  Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

Authors:  Longzhu Q Shen; Giuseppe Amatulli; Tushar Sethi; Peter Raymond; Sami Domisch
Journal:  Sci Data       Date:  2020-05-28       Impact factor: 6.444

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  3 in total

1.  δ15N of Chironomidae: An index of nitrogen sources and processing within watersheds for national aquatic monitoring programs.

Authors:  J Renée Brooks; Jana E Compton; Jiajia Lin; Alan Herlihy; Amanda M Nahlik; William Rugh; Marc Weber
Journal:  Sci Total Environ       Date:  2021-11-23       Impact factor: 7.963

2.  Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding.

Authors:  Nathan J Smucker; Erik M Pilgrim; Huiyun Wu; Christopher T Nietch; John A Darling; Marirosa Molina; Brent R Johnson; Lester L Yuan
Journal:  Sci Total Environ       Date:  2022-04-01       Impact factor: 10.753

3.  Limited progress in nutrient pollution in the U.S. caused by spatially persistent nutrient sources.

Authors:  Rebecca J Frei; Gabriella M Lawson; Adam J Norris; Gabriel Cano; Maria Camila Vargas; Elizabeth Kujanpää; Austin Hopkins; Brian Brown; Robert Sabo; Janice Brahney; Benjamin W Abbott
Journal:  PLoS One       Date:  2021-11-29       Impact factor: 3.240

  3 in total

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