Stormflow and baseflow phosphorus (P) concentrations and loads in rivers may exert different ecological pressures during different seasons. These pressures and subsequent impacts are important to disentangle in order to target and monitor the effectiveness of mitigation measures. This study investigated the influence of stormflow and baseflow P pressures on stream ecology in six contrasting agricultural catchments. A five-year high resolution dataset was used consisting of stream discharge, P chemistry, macroinvertebrate and diatom ecology, supported with microbial source tracking and turbidity data. Total reactive P (TRP) loads delivered during baseflows were low (1-7% of annual loads), but TRP concentrations frequently exceeded the environmental quality standard (EQS) of 0.035mgL during these flows (32-100% of the time in five catchments). A pilot microbial source tracking exercise in one catchment indicated that both human and ruminant faecal effluents were contributing to these baseflow P pressures but were diluted at higher flows. Seasonally, TRP concentrations tended to be highest during summer due to these baseflow P pressures and corresponded well with declines in diatom quality during this time (R=0.79). Diatoms tended to recover by late spring when storm P pressures were most prevalent and there was a poor relationship between antecedent TRP concentrations and diatom quality in spring (R=0.23). Seasonal variations were less apparent in the macroinvertebrate indices; however, there was a good relationship between antecedent TRP concentrations and macroinvertebrate quality during spring (R=0.51) and summer (R=0.52). Reducing summer point source discharges may be the quickest way to improve ecological river quality, particularly diatom quality in these and similar catchments. Aligning estimates of P sources with ecological impacts and identifying ecological signals which can be attributed to storm P pressures are important next steps for successful management of agricultural catchments at these scales.

This study investigated internal loading of sediment-derived phosphorus (P) in a small, meso-eutrophic lake (surface area 0.2 km 2 , catchment area 2.7 km 2 , mean depth 6 m, maximum depth 14 m) on the Atlantic seaboard of western Europe. High resolution data collected over 2.5 years (1 Mar 2011 to 30 Sep 2013) revealed inconsistent patterns in (1) the timing and magnitude of lake turnover and (2) the relative importance of the transfer of hypolimnetic sediment-derived P to the epilimnion when compared with external catchment loading. Lake turnover events during spring and summer had the effect of increasing the internal loading of epilimnetic P during the main growing season, thus adding to eutrophication pressure and contributing to algal blooms in the lake. Abrupt pre-fall (autumnal) turnover events and associated increases in eutrophication pressure such as those reported here may become more frequent occurrences in western Europe because of warming-induced increases in Atlantic summer storm frequency and magnitude, and they could counter the apparent effectiveness of measures aimed at reducing eutrophication impacts through limiting external loadings of nutrients from the catchment.

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.

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Funding No funding was provided for this study aside from supervisory support through Harper Adams University. Conflicts of interest/Competing interests NAAvailability of data and material Data were sourced from the National River Flow Archive and the EA historical river water quality. These were obtained under licence and are unavailable for distribution without the express permission of NRFA or the EA.

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UN Sustainable Development Goal 6 aims to achieve clean water for all. Access to clean water is a basic human right but can be costly and challenging. Using natural processes to provide cleaner water for treatment is a cost effective, and often beneficial to other ecosystem services, method. Unfortunately, there are a number of barriers to the implementation of natural processes for cleaner water such as the difficulty of funding these nature-based solutions which is linked to the requirement of accurate valuation. Once funded, partnership with land practitioners is important to ensure that detrimental impacts are not experienced elsewhere and to ensure that these natural processes such as ponds and constructed wetlands are maintained and managed appropriately. The future in the United Kingdom and Europe, in general, is optimistic despite the large funding gap for nature-based solutions overall. Green finance, essentially a loan or investment to support environmentally-friendly activities, has been developed to funnel money towards sustainable investments with an environmental focus, and the percentage of world wealth spent on such investments has increased.