The application of high temporal resolution data in river catchment modelling and management strategies

Abstract

Modelling changes in river water quality, and by extension developing river management strategies, has historically been reliant on empirical data collected at relatively low temporal resolutions. With access to data collected at higher temporal resolutions, this study investigated how these new dataset types could be employed to assess the precision and accuracy of two phosphorus (P) load apportionment models (LAMs) developed on lower resolution empirical data. Predictions were made of point and diffuse sources of P across ten different sampling scenarios. Sampling resolution ranged from hourly to monthly through the use of 2000 newly created datasets from high frequency P and discharge data collected from a eutrophic river draining a 9.48 km catchment. Outputs from the two LAMs were found to differ significantly in the P load apportionment (51.4% versus 4.6% from point sources) with reducing precision and increasing bias as sampling frequency decreased. Residual analysis identified a large deviation from observed data at high flows. This deviation affected the apportionment of P from diffuse sources in particular. The study demonstrated the potential problems in developing empirical models such as LAMs based on temporally relatively poorly-resolved data (the level of resolution that is available for the majority of catchments). When these models are applied ad hoc and outside an expert modelling framework using extant datasets of lower resolution, interpretations of their outputs could potentially reduce the effectiveness of management decisions aimed at improving water quality.

Challenges of Reducing Phosphorus Based Water Eutrophication in the Agricultural Landscapes of Northwest Europe

“…One commonly used method for source identification is statistical analysis of water quality time series. In particular, load apportionment models (LAMs) based on concentration -discharge (C-Q) relationships (Bowes et al, 2008(Bowes et al, , 2014(Bowes et al, , 2015Greene et al, 2011;Lamba et al, 2015;Crockford et al, 2017;Glendell et al, 2018) or the identification of periods of the year when one source is believed to dominate over other sources, a method is used to disentangle point sources from diffuse sources (Legeay et al, 2015). Equations in LAMs all rely on the assumption that point source emissions are constant in time (leading to negative C-Q relationships due to dilution when discharge increases) while diffuse source emissions increase with discharge (leading to positive C-Q relationships due to mobilization and delivery of P during storm events).…”

Non-domestic phosphorus release in rivers during low-flow: Mechanisms and implications for sources identification

“…Therefore, it would not be reasonable to split the 22 years into more than two periods to apply the LAM, as these models are very sensitive to the monitoring frequency and/or duration (Crockford et al, 2017). More interestingly, the estimated virtual contribution of point sources increased in the three sub-catchments.…”

An investigation into the impact of nine catchment characteristics on the accuracy of two phosphorus load apportionment models

“…Despite initial studies asserting their accuracy (Bowes et al, 2008;Bowes et al, 2009;Bowes et al, 2010;Greene et al, 2011), Crockford et al (2017) found both LAMs (BM and GM) are prone to substantial errors by calculating certainty statistics for each model under varying sampling temporal frequencies. The authors concluded this having used high frequency data from a river in Ireland and the statistical method of bootstrapping (Efron, 1979) to enable the calculation of standard errors (SEs) when the LAMs were applied to Q-P datasets.…”

“…Therefore, in point source dominated rivers P concentration should decrease as a function of Q, due to dilution, whereas the opposite would be true for diffuse pollution. Details of model functions and dissimilarities are available in Crockford et al (2017).…”