Literature DB >> 20082001

Long-term monitoring of river water nitrate: how much data do we need?

T P Burt1, N J K Howden, F Worrall, M J Whelan.   

Abstract

Long records of river water quality are invaluable for helping to understand the biogeochemistry of hydrological systems. They allow relationships to be established between changes in water quality (including seasonal cycles, episodic responses and long-term trends) and potential drivers, such as climatic forcing or human activity; they can act as a stimulus for process-oriented experimental research; they can be used to help to make predictions about future temporal and spatial patterns; and they can help to guide management options to mitigate water pollution. In this paper we present the case in favour of maintaining some long records of river water nitrate concentration at "benchmark" sites, in terms of enhancing process understanding and identifying system lags. Many long-term time series of nitrate concentration data share similar features including a pronounced seasonality characterised by a clear winter maximum, an upward trend in the post-war period followed by a levelling off, or even a decline in the last 20 years, and unusually high concentrations following drought years. Concentrations in any one year are often dependent on conditions in previous years; relationships can be established between concentrations and hydrological drivers (such as rainfall) with different lag periods which can yield information about supply or transport limitations to nitrate transfers. The interpretation of any record is dependent on its length: short records have a high potential for misinterpretation. Often, the value of long records only becomes apparent when analysed in retrospect, perhaps yielding insight into processes and phenomena for which the data collection programme was not originally designed. We, therefore, urge monitoring agencies to devise a strategy for maintaining long records--at least for a few benchmark stations.

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Year:  2009        PMID: 20082001     DOI: 10.1039/b913003a

Source DB:  PubMed          Journal:  J Environ Monit        ISSN: 1464-0325


  6 in total

1.  Quantifying Variability in Four US Streams Using a Long-Term Data Set: Patterns in Water Quality Endpoints.

Authors:  Douglas B McLaughlin; Camille A Flinders
Journal:  Environ Manage       Date:  2015-09-24       Impact factor: 3.266

2.  Human factors and tidal influences on water quality of an urban river in Can Tho, a major city of the Mekong Delta, Vietnam.

Authors:  Hirokazu Ozaki; Thi Kinh Co; Anh Kha Le; Viet Nu Pham; Van Be Nguyen; Mitsunori Tarao; Huu Chiem Nguyen; Viet Dung Le; Hieu Trung Nguyen; Masaki Sagehashi; Sachi Ninomiya-Lim; Takashi Gomi; Masaaki Hosomi; Hideshige Takada
Journal:  Environ Monit Assess       Date:  2013-10-10       Impact factor: 2.513

3.  Analysis of long-term water quality for effective river health monitoring in peri-urban landscapes--a case study of the Hawkesbury-Nepean river system in NSW, Australia.

Authors:  U Pinto; B L Maheshwari; R L Ollerton
Journal:  Environ Monit Assess       Date:  2012-10-07       Impact factor: 2.513

4.  Temporal variability in nutrient concentrations and loads in the River Tamar and its catchment (SW England) between 1974 and 2004.

Authors:  Alan D Tappin; Utra Mankasingh; Ian D McKelvie; Paul J Worsfold
Journal:  Environ Monit Assess       Date:  2012-10-11       Impact factor: 2.513

5.  Long-term trends in Swiss rivers sampled continuously over 39 years reflect changes in geochemical processes and pollution.

Authors:  Juerg Zobrist; Ursula Schoenenberger; Simon Figura; Stephan J Hug
Journal:  Environ Sci Pollut Res Int       Date:  2018-04-03       Impact factor: 4.223

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

Authors:  Jana E Compton; Ryan A Hill; Alan T Herlihy; Robert D Sabo; J Renée Brooks; Marc Weber; Brian Pickard; Steve G Paulsen; John L Stoddard
Journal:  Environ Sci Technol       Date:  2021-06-01       Impact factor: 11.357

  6 in total

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