Methods for detecting change in hydrochemical time series in response to targeted pollutant mitigation in river catchments

Lloyd, Charlotte; Freer, J. E.; Collins, Adrian L.; Johnes, Penny J; Jones, J. Iwan

doi:10.1016/j.jhydrol.2014.04.036

Cited by 56 publications

(45 citation statements)

References 133 publications

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“…The method of comparison over the 5-year period was to identify similar discharge ranges in each year corresponding to the discharge range, and extract the concurrent P concentration data. Following tests of normality (Lloyd et al, 2014), the mean and variance of TP concentrations, for each discharge range, were compared in a year on year basis by one-way ANOVA in order to determine the magnitude and significance of any change.…”

Section: Discussionmentioning

confidence: 99%

Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments

Campbell

Jordan

Arnscheidt

2015

Hydrol. Earth Syst. Sci.

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Abstract. This study reports the use of high-resolution water quality monitoring to assess the influence of changes in land use management on total phosphorus (TP) transfers in two 5 km 2 agricultural sub-catchments. Specifically, the work investigates the issue of agricultural soil P management and subsequent diffuse transfers at high river flows over a 5-year timescale. The work also investigates the phenomenon of low flow P pollution from septic tank systems (STSs) and mitigation efforts -a key concern for catchment management. Results showed an inconsistent response to soil P management over 5 years with one catchment showing a convergence to optimum P concentrations and the other an overall increase. Both catchments indicated an overall increase in P concentration in defined high flow ranges. Low flow P concentration showed little change or higher P concentrations in defined low flow ranges despite replacement of defective systems and this is possibly due to a number of confounding reasons including increased housing densities due to new-builds. The work indicates fractured responses to catchment management advice and mitigation and that the short to medium term may be an insufficient time to expect the full implementation of policies (here defined as convergence to optimum soil P concentration and mitigation of STSs) and also to gauge their effectiveness.

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Section: Discussionmentioning

confidence: 99%

Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments

Campbell

Jordan

Arnscheidt

2015

Hydrol. Earth Syst. Sci.

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“…In fact, the Lunj-Box test for the non-correlation hypothesis does not reject, using up to 30 different lags in all the locations under study, and Figure 4 presents the histograms of the residuals that resemble the normal curve. 9.599 0.00 0.00 9.983 0.00 0.00 9.673 0.00 0.00 9.559 0.00 0.00 9.532 0.00 0.00 β 3 9.212 0.00 0.00 9.546 0.00 0.00 9.138 0.00 0.00 9.504 0.00 0.00 9.737 0.00 0.00 β 4 8.753 0.00 0.00 9.001 0.00 0.00 9.356 0.00 0.00 9.134 0.00 0.00 9.014 0.00 0.00 β 5 8.313 0.00 0.00 9.262 0.00 0.00 8.555 0.00 0.00 8.688 0.00 0.00 8.753 0.00 0.00 β 6 8.362 0.00 0.00 7.771 0.00 0.00 7.270 0.00 0.00 7.847 0.00 0.00 7.939 0.00 0.00 β 7 7.273 0.00 0.00 8.164 0.00 0.00 7.271 0.00 0.00 8.576 0.00 0.00 8.376 0.00 0.00 β 8 7.087 0.00 0.00 8.844 0.00 0.00 6.556 0.00 0.00 7.668 0.00 0.00 8.260 0.00 0.00 β 9 7.971 0.00 0.00 7.941 0.00 0.00 5.800 0.00 0.00 7.166 0.00 0.00 7.319 0.00 0.00 β 10 7.410 0.00 0.00 7.844 0.00 0.00 7.673 0.00 0.00 7.561 0.00 0.00 8.477 0.00 0.00 β 11 8.030 0.00 0.00 8.968 0.00 0.00 9.737 0.00 0.00 9.673 0.00 0.00 9.408 0.00 0.00 β 12 9.859 0.00 0.00 9.946 0.00 0.00 9.418 0.00 0.00 9.025 0.00 0.00 9.207 0.00 0.00 Furthermore, with the exception of the CAR location, the residuals of the calibration model do not reject (at a 1% significance level) the normality assumption using the Jarque-Bera test or the Kolmogorov-Smirnov test; the K-S p-values are presented in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…Control charts were developed by [8] to treat the case of a French river, for which the parameter of interest, the dissolved oxygen concentration (DO), was characterized by a non-stationary and seasonal time evolution. A range of statistical techniques, discussed in [9], can be used to detect gradual or abrupt changes in hydrochemistry, including parametric, non-parametric, and signal decomposition methods.…”

Section: Introductionmentioning

confidence: 99%

A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables

Monteiro

Costa

2018

Hydrology

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Abstract:The monitoring and prediction of water quality parameters are important tasks in the management of water resources. In this work, the performances of time series statistical models were evaluated to predict and forecast the dissolved oxygen (DO) concentration in several monitoring sites located along the main river Vouga, in Portugal, during the period from January 2002 to May 2015. The models being compared are a regression model with correlated errors and a state-space model, which can be seen as a calibration model. Both models allow the incorporation of water quality variables, such as time correlation or seasonality. Results show that, for the DO variable, the calibration model outperforms the regression model for sample modeling, that is, for a short-term forecast, while the regression model with correlated errors has a better performance for the forecasting h-steps ahead framework. So, the calibration model is more useful for water monitoring using an online or real-time procedure, while the regression model with correlated errors can be applied in order to forecast over a longer period of time.

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“…Highfrequency monitoring enables the estimation of trend characteristics in shorter periods, being less sensible for longerterm trends (e.g. Lloyd et al, 2014). Many studies focus on the interactions between groundwater and surface water, in particular the different flow paths of nutrients towards the surface water (cf.…”

Section: F C Van Geer Et Al: High-resolution Monitoring Of Nutrienmentioning

confidence: 99%

“…High-frequency variations (noise) tend to obscure the low-frequency signal. High-frequency monitoring enables filtering out the noise (low-pass filter) during relatively short monitoring periods in order to elucidate the long-term trend (Bierkens et al, 1999;Halliday et al, 2012;Aubert et al, 2013;Lloyd et al, 2014;Van der Grift et al, 2016).…”

Section: Seasonal and Annual Patterns And Long-term Behaviourmentioning

confidence: 99%

High-resolution monitoring of nutrients in groundwater and surface waters: process understanding, quantification of loads and concentrations, and management applications

Geer

Kronvang

Broers

2016

Hydrol. Earth Syst. Sci.

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Abstract. Four sessions on "Monitoring Strategies: temporal trends in groundwater and surface water quality and quantity" at the EGU conferences in 2012, 2013, 2014, and 2015 and a special issue of HESS form the background for this overview of the current state of high-resolution monitoring of nutrients. The overview includes a summary of technologies applied in high-frequency monitoring of nutrients in the special issue. Moreover, we present a new assessment of the objectives behind high-frequency monitoring as classified into three main groups: (i) improved understanding of the underlying hydrological, chemical, and biological processes (PU); (ii) quantification of true nutrient concentrations and loads (Q); and (iii) operational management, including evaluation of the effects of mitigation measures (M). The contributions in the special issue focus on the implementation of high-frequency monitoring within the broader context of policy making and management of water in Europe for support of EU directives such as the Water Framework Directive, the Groundwater Directive, and the Nitrates Directive. The overview presented enabled us to highlight the typical objectives encountered in the application of highfrequency monitoring and to reflect on future developments and research needs in this growing field of expertise.

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Methods for detecting change in hydrochemical time series in response to targeted pollutant mitigation in river catchments

Cited by 56 publications

References 133 publications

Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments

Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments

A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables

High-resolution monitoring of nutrients in groundwater and surface waters: process understanding, quantification of loads and concentrations, and management applications

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