Animal Waste, Water Quality and Human Health

178(vi)Animal Waste, Water Quality and Human Healththe probability density function of overland- and through-flow in eachone km 2 cell.The Screening Tool authors note the lack of empirical ground-truth data on riverinefaecal indicator concentrations which would be needed to assess the predictiveaccuracy of this approach. However, they produce annual FIO (i.e. E. coli)runoff loadings for each one km 2 grid cell covering the whole of Scotland andNorthern Ireland, suggesting annual ranges in FIO export of between 1 × 10 14 cfu km −2 .pa −1 (see Anon. 2006: pages 128 and 129).The only environmental FIO ground-truth data available to the Screening Toolauthors were the bathing beach compliance data collected as required by Directive76/160/EEC, the EU Bathing Water Directive which required FIO measurement atover 500 coastal bathing waters around the coast of the United Kingdom (Anon.1976). They sought to test the modelling approach by parametric correlationanalysis between the number of samples (i.e. of the 20 samples collected eachbathing season at each compliance point in the United Kingdom) achieving theDirective 76/160/EEC Guide value for faecal coliform (i.e. 100 cfu · 100 mL −1 )and the annual mean (i.e. arithmetic mean) value of the E. coli export from allone km 2 grids which fell within the hydrological contributing catchment thoughtto affect specific coastal bathing water compliance locations. The FIO exportmodels for 60 correlating pairs gave poorly explained variance of the number ofcompliant samples with r 2 values ranging between 0.01 and 0.33 (see pages 176to 180 of Anon. 2006). This is, perhaps, unsurprising given: (i) the unpredictableand inherently dissimilar, near-shore dilution and transport effects which wouldbe found at different coastal locations linking riverine inputs to the bathing watercompliance point where water quality is measured; (ii) the seasonal mismatchbetween the “summer” compliance data (dependent) and the “annual” loading(predictor) variables which were used in the correlation analysis; (iii) theprobable right skew in the predictor variable and (iv) the effect of using the meanloading for each one km 2 grid to characterise catchment derived flux to thebathing water (i.e. at the catchment outlet which may have a greater proportionof high FIO-export land use than, for example, afforested headwater areas).Efforts at the EU scale are evident to develop integrated modelling strategiesable to address the needs of WFD implementation (Moore & Tindall 2005).However, operationally useful, that is fully white box, deterministic andprocess-based faecal indicator models able to predict the effects of individualremedial “programmes of measures” (POMs) or “best management practices”(BMPs) on catchment scale FIO fluxes do not exist at the present time.Predictive black-box modelling of faecal indicator flux, using satellite andGIS-derived data in the 1500 km 2 Ribble catchment in Lancashire, UK, has been