Literature DB >> 29354871

Assessing uncertainty in annual nitrogen, phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset.

Patrick T Kelly1, Michael J Vanni2, William H Renwick3.   

Abstract

Accurate estimation of constituent loads is important for studies of ecosystem mass balance or total maximum daily loads. In response, there has been an effort to develop methods to increase both accuracy and precision of constituent load estimates. The relationship between constituent concentration and stream discharge is often complicated, potentially leading to high uncertainty in load estimates for certain constituents, especially at longer-term (annual) scales. We used the loadflex R package to compare uncertainty in annual load estimates from concentration vs. discharge relationships in constituents of interest in agricultural systems, including ammonium as nitrogen (NH4-N), nitrate as nitrogen (NO3-N), soluble reactive phosphorus (SRP), and suspended sediments (SS). We predicted that uncertainty would be greatest in NO3-N and SS due to complex relationships between constituent concentration and discharge. We also predicted lower uncertainty with a composite method compared to regression or interpolation methods. Contrary to predictions, we observed the lowest uncertainty in annual NO3-N load estimates (relative error 1.5-23%); however, uncertainty was greatest in SS load estimates, consistent with predictions (relative error 19-96%). For all constituents, we also generally observed reductions in uncertainty by up to 34% using the composite method compared to regression and interpolation approaches, as predicted. These results highlight differences in uncertainty among different constituents and will aid in model selection for future studies requiring accurate and precise estimates of constituent load.

Entities:  

Keywords:  Composite method; Loadflex; Nitrate; Stream load; Suspended sediments; Uncertainty

Mesh:

Substances:

Year:  2018        PMID: 29354871     DOI: 10.1007/s10661-018-6470-4

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  7 in total

1.  Storm Event Suspended Sediment-Discharge Hysteresis and Controls in Agricultural Watersheds: Implications for Watershed Scale Sediment Management.

Authors:  Sophie C Sherriff; John S Rowan; Owen Fenton; Philip Jordan; Alice R Melland; Per-Erik Mellander; Daire Ó hUallacháin
Journal:  Environ Sci Technol       Date:  2016-02-02       Impact factor: 9.028

2.  Timing of riverine export of nitrate and phosphorus from agricultural watersheds in Illinois: implications for reducing nutrient loading to the Mississippi River.

Authors:  Todd V Royer; Mark B David; Lowell E Gentry
Journal:  Environ Sci Technol       Date:  2006-07-01       Impact factor: 9.028

3.  Monitoring nutrient transport in large rivers.

Authors:  A Tonderski; A Grimvall; J R Dojlido; G M Van Dijk
Journal:  Environ Monit Assess       Date:  1995-02       Impact factor: 2.513

4.  Monitoring stream sediment loads in response to agriculture in Prince Edward Island, Canada.

Authors:  Ashley Alberto; Andre St-Hilaire; Simon C Courtenay; Michael R van den Heuvel
Journal:  Environ Monit Assess       Date:  2016-06-17       Impact factor: 2.513

5.  Assessment of uncertainty in constructed wetland treatment performance and load estimation methods.

Authors:  Riku Eskelinen; Anna-Kaisa Ronkanen; Hannu Marttila; Bjørn Kløve
Journal:  Environ Monit Assess       Date:  2016-05-25       Impact factor: 2.513

6.  Trends in water quality in LEASEQ rivers and streams (northwestern Ohio), 1975-1995. Lake Erie Agricultural Systems for Environmental Quality.

Authors:  R Peter Richards; David B Baker
Journal:  J Environ Qual       Date:  2002 Jan-Feb       Impact factor: 2.751

7.  Water quality trends and changing agricultural practices in a midwest U.S. watershed, 1994-2006.

Authors:  William H Renwick; Michael J Vanni; Qianyi Zhang; Jon Patton
Journal:  J Environ Qual       Date:  2008-08-08       Impact factor: 2.751

  7 in total

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