Under the Water Framework Directive (WFD) EU member states are legally required to design “programmes of measures
” to manage point and diffuse sources of faecal indicator organisms (FIOs) that could cause non-compliance of bathing and shellfish-harvesting waters with microbial standards [1
]. To meet this requirement, data are needed to define FIO concentrations and fluxes in individual rivers and streams so that the magnitude of the problem can be assessed and more heavily polluted waters identified for potential remedial action. Knowledge is also required of the effectiveness of specific measures to reduce FIO concentrations, especially those aimed at addressing diffuse pollution sources.
Previous studies have highlighted the importance of rainfall-induced hydrograph (“high-flow”) conditions for the mobilisation and transport of FIOs within catchments, with FIO concentrations and discharge volumes both typically increasing by an order of magnitude compared with dry-weather (“base”) flow, leading to a c.100-fold increase in export coefficients. Kay et al.
], for example, report an increase in geometric mean (GM) faecal coliform (presumptive Escherichia coli
) (“FC”) export coefficient for 205 UK rivers and streams from 5.5 × 108
at base flow to 3.6 × 1010
at high flow, and a corresponding increase for enterococci (“EN”) from 8.3 × 107
to 7.1 × 109
. Unfortunately, FIO concentration data are lacking for many UK watercourses, and, even for those that are routinely monitored, there is often poor characterisation of high-flow conditions.
Given the legal requirements of the WFD, there is an urgent imperative from the research and policy communities for generic models that can accurately predict base- and high-flow FIO concentrations across the UK, thereby informing future integrated catchment management programmes.
While physically based watershed modelling (Hydrological Simulation Program Fortran (HSPF), Simulated Catchments (SIMCAT), Soil and Water Assessment Tool (SWAT), etc.
) is now quite advanced in relation to nutrients and sediments, deployment of these model systems to FIOs is prevented by the absence of empirical data with which to parameterise and evaluate any of these models. For example, credible regionally specific in-channel deposition and re-entrainment coefficients, together with empirically based water column real-time T90
coefficients, are simply not available. A similar absence of parameterisation data is evident for the terrestrial land phase of FIO flux, making application of process-based models, at this stage, difficult, if not impossible. The development of the Scotland and Northern Ireland Forum for Environmental Research (SNIFFER) “screening tool” (at a 1 × 1 km grid cell resolution) for identifying and characterising diffuse pollution in Scotland and Northern Ireland has provided valuable insights into the types and strengths of FIO pollution sources and the factors affecting the risk of pollutant mobilisation and delivery to watercourses, thus enabling the potential FIO export coefficients for catchments to be determined [4
]. However, SNIFFER does not provide a basis for characterising base- and high-flow FIO concentrations, and the veracity of the export coefficient calculations have yet to be fully evaluated against data from monitored catchments. Some of the most successful catchment-scale FIO modelling has been undertaken using linear regression techniques to model relationships between GM FIO concentrations recorded at monitored sites and land use within their catchments, using variables such as the proportions of grassland and built-up land as proxies for key sources of faecal pollution. Hitherto, this work has been primarily based on individual catchment studies [6
The aim of the present study was to extend this latter approach by investigating: (i) whether improved models might be achieved by augmenting the predictor variables to include both direct measures of the key FIO sources (i.e., human population and livestock density data) and factors that may affect source strength and the mobilisation, transport, die-off and sedimentation of FIOs within catchments (e.g., volume of runoff, soil hydrology and catchment size); and (ii) the extent to which models developed by combining data from discrete UK catchment studies, sampled at different times and under different antecedent weather conditions, are truly “generic” and transferable across the UK.
Previously, Kay et al.
] have presented a synthesis of FIO concentration and export coefficient data for 205 sampling points in 15 study catchments, which are broadly representative of the diverse land use types and climatic regimes across the UK. Southern England and areas of chalk downland are the notable regions/landscapes that are not represented. In the present study, data from those points with catchments ≥ 5 km2
(153 from 14 of the study catchments) have been used to model base- and high-flow FC and EN concentrations during the summer bathing season, using predictor variables that are readily available and have national coverage.
The transferable models presented here provide a tool for characterising FIO concentrations in rivers and streams across the UK, though they clearly need to be applied with some degree of caution in Southern England. In addition, they can provide valuable insights into the key sources and factors affecting transfer and survival of FIOs at a catchment scale, thereby informing the development of policies and prioritisation of investment to reduce microbial pollution as an optimal mix of cost-effective regional and site specific policy remediation strategies will be required to achieve the highest reductions.
Importantly, too, these models can form a basis for quantifying the likely impact of different land use/stocking levels that might result from the implementation of measures designed to reduce FIO loadings, or reforms in agricultural policy/funding e.g. by linking to land use change models, such as those developed by Fezzi et al.
] and Jones and Trantor [9
The models presented here, developed to inform the Rural Economy and Land Use funded Catchment, Hydrology, Resources, Economics and Management programme [10
], have been used to generate predictions of FIO concentrations at base- and high-flow, within the Humber river basin district (RBD), and generate predictions of FIO concentration reductions within that RBD following the implementation of seven land use management and policy instruments; fiscal constraint, production constraint, cost intervention, area intervention, demand-side constraint, input constraint, and micro-level land use management. The results of these analyses are detailed in Hampson et al.
4.1. Dominant Faecal Indicator Organism Sources within Catchments
The regression models clearly identify both humans and livestock as key FIO sources within catchments. It should be noted that some FIOs from both sources, especially particle-attached FIOs, may be deposited on the stream bed under base-flow conditions and re-suspended at times of high-flow. The FIO concentrations reported are therefore derived from both newly entrained and newly added organisms into the water column. Indeed, a significant proportion of the elevated concentration at high flow may well be from the stream bed.
Under base-flow conditions human sources (as reflected in the HUMAN and URBAN variables) are more important than livestock sources in accounting for the observed variance in FC and EN concentrations. Indeed, the DAIRY or GRASSLAND variables that are entered at Step 2 in the base-flow regression models provide only very limited additional explanation. This suggests that sewage-related sources are dominant at base flow, with relatively little FIO input from agricultural sources. The former will be largely treated effluents from WwTWs, which generally have much lower FIO concentrations under base-flow conditions than high flow [12
]. The relatively low levels of explained variance in the base-flow models probably reflects the fact that in this ‘black box’ modelling, no account is taken of the nature of the effluent quality of individual WwTWs, which varies with the type of treatment [12
]; and also that the URBAN and HUMAN data for individual subcatchments will poorly reflect the magnitude of sewage effluent inputs to the subcatchment watercourses in cases where WwTWs serving a significant proportion of the built-up area are located downstream of the monitoring point (i.e.
, sewage is exported out of the subcatchment for treatment). It is also interesting to note that the HUMAN and URBAN variables provide very similar levels of explained variance—suggesting that, for the purpose of catchment-scale modelling, built-up land is a good proxy for human population.
At high flow both human and livestock sources assume importance, with the latter generally being the more dominant. Under such conditions some untreated sewage from CSOs or overflows from WwTW storage tanks is likely to be discharged to watercourses, and the quality of treated effluents from many WwTWs will be reduced as a consequence of more rapid transmission through the plant [12
]. The importance of human sources is evidenced by the inclusion of URBAN and HUMAN as key variables in the various high-flow models.
The general importance of livestock sources at high flow is reflected in the land cover-based models by the prominence of GRASSLAND, which is entered first for FC and makes a major contribution to the explained variance achieved for EN (Table 5
). It should be noted that the GRASSLAND land use category comprises all temporary/permanent grassland, other than that which is mapped as rough grazing. As such it encompasses quite a wide range in terms of quality and productivity, extending from very fertile lowland pastures, which tend to be dominated by dairy farming, up to quite high altitudes in some subcatchments, where beef and sheep production systems tend to dominate. Because of this, GRASSLAND is simply a proxy variable for the more intensive areas of livestock production. Consequently, land cover data, as are traditionally used in FIO modelling, inevitably have limited explanatory power and potential for scenario modelling. By incorporating livestock density data, the present study provides insight into the relative significance of different production systems. Of the various livestock variables used in the modelling (Table 3
), DAIRY emerges consistently as the key variable, with levels of explained variance that are consistently higher than GRASSLAND. In the case of the high-flow FC models, for example, the DAIRY has an r2
value of 0.439, compared with 0.316 for GRASSLAND, which clearly highlights the importance dairy farming systems (cf
. beef cattle and sheep) as a FIO source. This presumably reflects the high intensity of most dairy farming operations, which tend to be largely confined to the better land in the lowlands; the concentration of animals close to farm buildings for milking; and the storage and disposal to land of large quantities of waste (mostly in form of slurry) from yard areas and indoor winter housing–all of which pose potential pollution risks in terms of both diffuse sources (e.g., faeces voided directly in fields and slurry/manure applications to land) and point-source pollution (e.g., runoff from farmyards and milking parlours, slurry stores and manure heaps). By contrast, beef and sheep systems are not so confined to the better land, are often less intensive, and generate smaller amounts of waste for disposal. Sheep may, however, be present in quite large numbers in some catchments, both in areas of temporary/permanent grassland and rough grazing. They therefore represent a potentially significant FIO source, and this is reflected in SHEEP being entered at Step 3 with a +ve b
value in both high-flow population-based models (Table 6
). On the basis of these results, the design and implementation of measures to address FIO pollution from agricultural sources should be targeted initially on areas of dairy production.
4.2. Other Potential Factors Affecting Faecal Indicator Concentrations
Several “other” (i.e.
, non-source) variables (Table 3
) were included in the all-variable modelling. These relate to three catchment characteristics that may affect source strength and the mobilisation, transport, die-off and sedimentation of FIOs within catchments, namely: runoff volume, soil hydrology and catchment size.
Volume of runoff during the study period may be an important factor since, during prolonged periods of wet weather, certain FIO sources (especially those associated with diffuse sources, such as animal faeces in fields and stream source contributory areas) will tend to become depleted. It might be anticipated, therefore, that a period of high flow will tend to be associated with higher FIO concentrations if preceded by a long spell of dry weather than if it followed a relatively wet period. Due to differences in weather conditions between the 6–8 weeks of each of the 14 catchment studies, there is very marked inter-subcatchment variability in runoff volumes (e.g., “TOTRUNOFF”: range, 3.94–211.88 m3 km−2 h−1). In the case of soil hydrology, in subcatchments with more poorly drained soils (i.e., with a higher mean SPR) there will likely be more surface runoff per unit rainfall and hence increased mobilisation and transport of FIOs from land to adjacent watercourses, which may well lead to increases in FIO concentrations. In the present study the SPR for both the subcatchments as a whole (“TOTSPR”: range, 22.47–59.41%) and for the areas of permanent/temporary grassland (“GRSPR”: range, 18.38–58.44%) were used as predictor variables. Catchment size may also be an important factor, since the opportunity for die-off of FIOs along watercourses as a result of exposure to UV light is increased within larger catchments as a result of the greater length of channel flow. This is particularly likely under base-flow conditions when flow velocities, water depth and turbidity are all at a minimum, thereby maximising UV exposure. The 153 subcatchments used in the modelling range in size (“AREA”) from 5.01–1,013.18 km2.
Despite the marked inter-subcatchment variability of runoff volume, soil hydrology and catchment area, only AREA was entered in any of the models, and that with a (counter-intuitive) +ve b
value in the base-flow FC model (Table 4
). Clearly, controlled experimental studies are needed to assess more fully the effects of these factors. On present evidence, however, it would seem that their role in affecting FIO concentrations in watercourses at the regional and national scales is minor compared with differences in human population density, stocking levels and associated land use types (URBAN and GRASSLAND)–i.e.
, the factors that relate directly or indirectly to the key FIO sources.
4.3. Inter-Catchment Transferability of the Models
The out-of-sample testing reveals some degree of inter-study variability in the model evaluated, and this will inevitably tend to be greater in models with lower levels of explained variance, notably the base-flow models. This is not unexpected [29
]; and is likely to be attributable to a combination of both inter-catchment and temporal factors. The former reflect systematic differences between the catchments affecting the sources, survival and mobility of FIOs that are not accounted for by the variables in the final regression models (i.e.
, the unexplained variance). For example, there may be inter-catchment variations in livestock farming facilities and management practices that limit the extent to which key predictor variables such as GRASSLAND and DAIRY provide a measure of FIO sources. Also, soil hydrology (as outlined above) seems likely to account for some degree of inter-catchment variability, but its influence is not sufficiently strong to be included in the all-variable models; and other factors that were not included as potential predictor variables (e.g., temperature and topography) are likely to have a similar effect. The temporal factors, on the other hand, reflect the fact that the individual studies were undertaken over 6–8 week monitoring periods with markedly contrasting weather conditions, both before and during the studies; and at different times during the bathing season, which could, for example, affect FIO source strength in grazed fields as a result of the progressive accumulation over the summer months of faeces from dairy cattle (which are housed over winter). Volume of runoff during the individual study periods, which was considered most likely to be the key temporal factor, was included in the predictor variable set, but (cf.
hydrology) was not sufficiently significant to be entered in the all-variable models.
The strength of the present models lies in the fact that they are based on a FIO database that has such extensive geographical coverage (land use, climate, topography, soils, etc.) and encompasses a wide range of weather conditions during the individual monitoring periods. Some of the inter-study transfer errors are inevitably quite high, and these are partly attributable to temporal factors. Clearly, by combining the data from all 14 catchment studies the effects of the temporal factors are minimised and the inter-catchment errors reduced. The resulting land cover- and population-based models developed in the present study can therefore be applied with some confidence for predicting base- and high-flow GM FC and EN concentrations during the summer bathing season in UK watercourses with catchments areas between 5 and approximately 1,000 km2. While the lower size threshold is determined by the level of resolution of the available agricultural census data, the upper limit simply reflects the size of the larger catchments used in the present modelling. Further investigations are needed to establish the validity of applying these models to much larger catchments. By combining these GM FIO concentrations with discharge data, then the contribution that the individual rivers/streams make to overall FIO loadings to coastal waters can be estimated.
The models can also be used to evaluate the likely impact of different land use/stocking level and human population change scenarios, as might result from the implementation of measures designed to reduce FIO loadings, or reforms in agricultural policy/funding, as reported in Hampson et al.
In order to meet European WFD requirements there is an urgent need for generic (i.e., transferable) models that can accurately predict base- and high-flow GM FIO concentrations in UK watercourses. Previous studies of individual catchments have successfully developed regression models based on relationships between GM FIO concentrations recorded at monitored sites and land use within their subcatchments. The present study extended this approach by augmenting the predictor variables to include direct measures of key FIO sources (i.e., human population and livestock density data) and various factors (catchment size, runoff and soil hydrology) that may affect source strength and the mobilisation, transport and die-off of FIOs; and exploring the development of generic models by combining data from 14 different catchment studies across the UK.
Statistically significant base- and high-flow regression models have been developed for both FC and EN, with levels of explained variance consistently higher in latter models. Population variables (notably HUMAN and DAIRY) generally provide higher levels of explained variance than the land cover variables. Under base-flow conditions human, sewage-related, sources are dominant, whereas livestock sources tend to assume greater significance at high flow, with dairy farming systems (cf. beef cattle and sheep) being particularly important sources. Neither runoff, soil hydrology or catchment size were significant predictor variables. In more parsimonious land cover- and population-based models, developed for ease of transferability to other UK catchments, relatively high levels of explained variance were achieved for all the high-flow models, with adjusted r2 values ranging from 0.540 (land use model for FC) to 0.624 (population model for EN).
A programme of out-of-sampling testing on the high-flow EN model indicated some degree of inter-study variability, which is likely attributable to a combination of: (i) inter-catchment factors, which reflect systematic differences between the catchments that affect the sources, survival and mobility of FIOs that are not accounted for by the variables in the models; and (ii) temporal factors, which reflect the fact that the FIO monitoring was undertaken under different weather conditions and at different times during the summer bathing season. However, it is argued that by combining data from all 14 studies, which have a wide geographical distribution across UK and encompass a wide range of weather conditions, the effects of the temporal factors are minimised and the inter-catchment errors reduced.
Our research contributes to the emerging international debate on the use of farm best management practices and policy instruments to reduce FIOs and agricultural diffuse pollution (e.g., Bateman et al.
], Chadwick et al.
], Monaghan et al.
], Helming and Reinhard [32
], Hutchins et al.
], Maringanti et al.
] and Oliver et al.
]) and the resulting land cover- and population-based models can be employed, with some confidence, in UK catchments both to predict base- and high-flow FC and EN concentrations in unmonitored watercourses and to evaluate the likely impact of different land use/stocking level and human population change scenarios.