Statistical analysis of nitrate concentrations from the River Frome (Dorset) for the period 1965–76

Casey, H.; Clarke, Ralph T.

doi:10.1111/j.1365-2427.1979.tb01492.x

Cited by 70 publications

(53 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example Casey and Clarke (1979) in their study were dealing with the statistical analysis of nitrate concentrations in River Frome in the Southern England. Changes in stream nitrate concentrations caused by the land management practices were studied by Worrall et al (2003), where the time series of nitrate concentrations was decomposed into trend and annual cycle before modeled by AR models.…”

Section: Intoductionmentioning

confidence: 99%

Analysis of Nitrate Concentrations Using Nonlinear Time Series Models

Valent¹,

Howden²,

Szolgay³

et al. 2011

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

This study examines two long-term time series of nitrate-nitrogen concentrations from the River Ouse and Stour situated in the Eastern England. The time series of monthly averages were decomposed into trend, seasonal and cyclical components and residuals to create a simple additive model. Residuals were then modelled by linear time series models represented by models of the ARMA (autoregressive moving average) class and nonlinear time series models with multiple regimes represented by SETAR (self-exciting threshold autoregressive) and MSW (Markov switching) models. The analysis showed that, based on the minimal value of residual sum of squares (RSS) of one-step ahead forecast in both datasets, SETAR and MSW models described the time series better than models ARMA. However, the relative improvement of SETAR models against ARMA models was low ranging between 1% and 4% with the exception of the three-regime model for the River Stour where the improvement was 48.9%. In comparison, the relative improvement of MSW models was between 44.6% and 52.5 for two-regime and from 60.4% to 75% for three-regime models. However, the visual assessment of models plotted against original datasets showed that despite a high value of RSS, some ARMA models could describe the analyzed time series better than AR (autoregressive), MA (moving average) and SETAR models with lower values of RSS. In both datasets MSW models provided a very good visual fit describing most of the extreme values. The results of this work could be used as a base for construction of other time series models used to describe or predict nitratenitrogen concentrations.KEY WORDS: Nitrate Time Series, ARMA Models, SETAR Models, MSW Models.Peter Valent, Nicholas J. K. Howden, Ján Szolgay, Magda Komorníková: ANALÝZA KONCENTRÁCIÍ DUSIČNANOV POMOCOU NELINEÁRNYCH MODELOV ČASOVÝCH RADOV. J. Hydrol. Hydromech., 59, 2011, 3; 37 lit., 9 obr., 2 tab.Štúdia sa zaoberá analýzou dlhých časových radov koncentrácií dusičnanového dusíka v rieke Ouse a Stour vo Východnom Anglicku. Časové rady priemerných mesačných koncentrácií dusičnanov boli rozložené na trendovú, sezónnu a cyklickú zložku a reziduá pripočítané k sebe a tvoriace jednoduchý aditívny model. Reziduá boli ďalej modelované zložitejšími lineárnymi modelmi reprezentovanými modelmi triedy ARMA a nelineárnymi viacrežimovými modelmi SETAR a MSW. Výsledky analýzy ukázali, že na základe minimálnej hodnoty sumy štvorcov reziduí (SSR) jednokrokovej predpovede, v oboch prípadoch SETAR aj MSW modely opísali časové rady lepšie ako modely triedy ARMA. Vo väčšine prípadov relatívne zlepšenie modelov SETAR oproti jednoduchým AR(1) modelom bolo malé v rozmedzí od 1 do 4 % s výnimkou trojrežimového modelu pre rieku Stour, kde to bolo až 48,9 %. Naopak, relatívne zlepšenie modelov MSW oproti AR(1) modelom bolo v rozmedzí 44,6 až 52,5 % pre dvojrežimové a 60,4 až 75 % pre trojrežimové modely. Vizuálne posúdenie jednotlivých modelov však ukázalo, že napriek vysokým hodnotám SSR, niektoré ARMA modely dokázali lepšie opísať dané časové rady ako...

show abstract

Section: Intoductionmentioning

confidence: 99%

Analysis of Nitrate Concentrations Using Nonlinear Time Series Models

Valent¹,

Howden²,

Szolgay³

et al. 2011

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

show abstract

“…Studies of temporal variation in nitrate concentration in relation to changing climate and land use have focused on single river basins (Casey and Clarke, 1979;José, 1989;Worrall and Burt, 1999) or combined records from many catchments to make regional comparisons (Roberts and Marsh, 1987;Betton et al, 1991;Stuart et al, 2007). For example, an upward trend in nitrate concentrations was reported for the period 1971-1985 at Slapton Wood, a small catchment of mixed land use in Devon, UK (Burt et al, 1988).…”

Section: Introductionmentioning

confidence: 99%

Importance of long-term monitoring for detecting environmental change: lessons from a lowland river in south east England

et al. 2008

View full text Add to dashboard Cite

Abstract. Rising nitrate concentrations in rivers and groundwater in regions with intensive agriculture have been of concern for several decades. Long records of nitrate concentration are rare; in the UK, few pre-date 1974. Records from the River Stour from 1937 are analysed using moving-windows to explore the effects of window width on trend detection and process inference. Nitrate concentrations rose sharply from the late 1950s in response to widespread ploughing of grassland, but have fallen since the early 1980s, suggesting that widespread nitrogen control measures, which were introduced after the start of the downward trend, may not be the principal driver for recent change. Short windows (5 years) are too noisy; longer windows (10, 15 and 30 years) identify signals (i.e. trends) more reliably. Since system response times can be of the order of 20-30 years, management decisions made on shorter data sets (even up to 15 years) could be misleading.

show abstract

“…Riverine nitrate concentrations also follow a definite seasonal pattern that is related to the flow of the river [2,3]. Plant [4], whereas freezing at -20C gave more reliable results [5].…”

Section: Introductionmentioning

confidence: 99%

A submersible battery‐powered flowinjection (FI) sensor for the determinationof nitrate in estuarine and coastal waters

David

McCormack²,

Worsfold³

1999

Journal of Analytical Methods in Chemistry

View full text Add to dashboard Cite

The design, construction and performance of a remotely deployed submersible flow injection-based nutrient (total oxidized nitrogen) sensor are described. The sensor featured a custom-built microcomputer and a solid-state, flow-through spectrophotometric detector, and the derivatization chemistry was based on in-line coppercadmium reduction of nitrate to nitrite, and diazotization with N1NED and sulphanilamide.

show abstract

Statistical analysis of nitrate concentrations from the River Frome (Dorset) for the period 1965–76

Cited by 70 publications

References 6 publications

Analysis of Nitrate Concentrations Using Nonlinear Time Series Models

Analysis of Nitrate Concentrations Using Nonlinear Time Series Models

Importance of long-term monitoring for detecting environmental change: lessons from a lowland river in south east England

A submersible battery‐powered flowinjection (FI) sensor for the determinationof nitrate in estuarine and coastal waters

Contact Info

Product

Resources

About