Development and testing of a risk indexing framework to determine field-scale critical source areas of faecal bacteria on grassland.

This paper draws on lessons from a UK case study in the management of diffuse microbial pollution from grassland farm systems in the Taw catchment, south west England. We report on the development and preliminary testing of a field-scale faecal indicator organism risk indexing tool (FIORIT). This tool aims to prioritise those fields most vulnerable in terms of their risk of contributing FIOs to water. FIORIT risk indices were related to recorded microbial water quality parameters (faecal coliforms [FC] and intestinal enterococci [IE]) to provide a concurrent on-farm evaluation of the tool. There was a significant upward trend in Log[FC] and Log[IE] values with FIORIT risk score classification (r2 =0.87 and 0.70, respectively and P<0.01 for both FIOs). The FIORIT was then applied to 162 representative grassland fields through different seasons for ten farms in the case study catchment to determine the distribution of on-farm spatial and temporal risk. The high risk fields made up only a small proportion (1%, 2%, 2% and 3% for winter, spring, summer and autumn, respectively) of the total number of fields assessed (and less than 10% of the total area), but the likelihood of the hydrological connection of high FIO source areas to receiving watercourses makes them a priority for mitigation efforts. The FIORIT provides a preliminary and evolving mechanism through which we can combine risk assessment with risk communication to end-users and provides a framework for prioritising future empirical research. Continued testing of FIORIT across different geographical areas under both low and high flow conditions is now needed to initiate its long term development into a robust indexing tool

Risk-based modelling of faecal indicator organism export from agricultural landscapes

Microbial contamination of watercourses can threaten ecosystem services related to clean water; for example, recreational bathing, shellfish harvesting and potable water supplies. This is because pathogens associated with faeces from warm blooded animals can cause gastrointestinal illness in exposed human beings. Microbial water quality impacts from point sources associated with wastewater transfer and treatment have been reduced through engineering solutions. However, as these sources of contamination have been reduced diffuse sources have become more important. Diffuse pollution describes water quality impacts originating from accumulations of many small, spatially distributed, inputs. These sources of pollution are difficult to manage because their loading and connectivity to sensitive receptors varies spatially and temporally. The Sensitive Catchment Integrated Mapping Analysis Platform (SCIMAP) is a risk-based approach that has been developed to map sources of diffuse sediment and conservative nutrient pollution allowing for efficient targeting of mitigation efforts which are often expensive and occupy valuable productive land. SCIMAP has been well received within the regulatory community in the United Kingdom and its development to account for diffuse microbial pollution is therefore timely. The primary goal for this thesis was to explore SCIMAP’s application to microbial pollution, highlight areas for improvement and work towards a new SCIMAP framework that accounts for microbial diffuse pollution. An initial application of SCIMAP, as it exists, revealed that the time-integrated approach currently employed may be inappropriate for sources of microbial pollution that are likely to vary temporally due to microbial die off. Furthermore, an enhanced description of land use incorporating spatial distributions of the numbers and types of livestock may improve SCIMAP’s 4 performance. Spatial variations in microbial source loading arising from differences in the persistence of E. coli (an indicator of faecal pollution) in the faeces of different livestock was investigated within a controlled environment facility. This controlled experiment provided a novel non-linear description of E. coli growth in ovine and 2 types of bovine faeces for a period of 30 days post defecation. Potential variation in rainfall induced E. coli release from faecal matrices associated, with beef cattle, dairy cattle and sheep were explored using rainfall simulation. An asymptotic model of E. coli release with increasing rainfall depth was developed and no difference was discovered in the profile of release from sheep, beef cattle and dairy cattle. Finally lessons from these investigations were combined to propose a framework for an evolution of SCIMAP allowing for a better description of microbial source and transfer risk. This new version of SCIMAP will provide a decision support tool allowing for more efficient targeting of mitigation efforts reducing microbial impacts to important ecosystem services relying on clean water

Predicting diffuse microbial pollution risk across catchments: The performance of SCIMAP and recommendations for future development

Microbial pollution of surface waters in agricultural catchments can be a consequence of poor farm management practices, such as excessive stocking of livestock on vulnerable land or inappropriate handling of manures and slurries. Catchment interventions such as fencing of watercourses, streamside buffer strips and constructed wetlands have the potential to reduce faecal pollution of watercourses. However these interventions are expensive and occupy valuable productive land. There is, therefore, a requirement for tools to assist in the spatial targeting of such interventions to areas where they will have the biggest impact on water quality improvements whist occupying the minimal amount of productive land. SCIMAP is a risk-based model that has been developed for this purpose but with a focus on diffuse sediment and nutrient pollution. In this study we investigated the performance of SCIMAP in predicting microbial pollution of watercourses and assessed modelled outputs of E. coli, a common faecal indicator organism (FIO), against observed water quality information. SCIMAP was applied to two river catchments in the UK. SCIMAP uses land cover risk weightings, which are routed through the landscape based on hydrological connectivity to generate catchment scale maps of relative in-stream pollution risk. Assessment of the model's performance and derivation of optimum land cover risk weightings was achieved using a Monte-Carlo sampling approach. Performance of the SCIMAP framework for informing on FIO risk was variable with better performance in the Yealm catchment (rs = 0.88; p 0.05). Across both catchments much uncertainty was associated with the application of optimum risk weightings attributed to different land use classes. Overall, SCIMAP showed potential as a useful tool in the spatial targeting of FIO diffuse pollution management strategies; however, improvements are required to transition the existing SCIMAP framework to a robust FIO risk-mapping tool

Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics

Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics

Impact of low intensity summer rainfall on E. coli-discharge event dynamics with reference to sample acquisition and storage

Understanding the role of different rainfall scenarios on faecal indicator organism (FIO) dynamics under variable field conditions is important to strengthen the evidence base on which regulators and land managers can base informed decisions regarding diffuse microbial pollution risks. We sought to investigate the impact of low intensity summer rainfall on Escherichia coli-discharge (Q) patterns at the headwater catchment scale in order to provide new empirical data on FIO concentrations observed during baseflow conditions. In addition, we evaluated the potential impact of using automatic samplers to collect and store freshwater samples for subsequent microbial analysis during summer storm sampling campaigns. The temporal variation of E. coli concentrations with Q was captured during six events throughout a relatively dry summer in central Scotland. The relationship between E. coli concentration and Q was complex with no discernible patterns of cell emergence with Q that were repeated across all events. On several occasions, an order of magnitude increase in E. coli concentrations occurred even with slight increases in Q, but responses were not consistent and highlighted the challenges of attempting to characterise temporal responses of E. coli concentrations relative to Q during low intensity rainfall. Cross-comparison of E. coli concentrations determined in water samples using simultaneous manual grab and automated sample collection was undertaken with no difference in concentrations observed between methods. However, the duration of sample storage within the autosampler unit was found to be more problematic in terms of impacting on the representativeness of microbial water quality, with unrefrigerated autosamplers exhibiting significantly different concentrations of E. coli relative to initial samples after 12-h storage. The findings from this study provide important empirical contributions to the growing evidence base in the field of catchment microbial dynamics

Seasonal and within-herd variability of E. coli concentrations in fresh dairy faeces

This study provides a comprehensive temporal data set of faecal indicator organism (FIO) counts (both E. coli and other coliforms) in fresh dairy faeces for Scotland. Such faecal audits for the UK are scarce which is surprising given that livestock constitute one of the largest agricultural sources of diffuse microbial pollution of surface waters and contributors to poor bathing water quality. Such FIO concentration data (and evaluation of variability across seasonal, within-herd and year-on-year counts) in fresh faeces is a fundamental precursor to the robust parameterization of models that aim to predict the fate and transfer of both FIOs and pathogens in agricultural catchments

The effectiveness of Payments for Ecosystem Services at delivering improvements in water quality: lessons for experiments at the landscape scale

Background Randomised Control Trials (RCTs) are used in impact evaluation in a range of fields. However, despite calls for their greater use in environmental management, their use to evaluate landscape scale interventions remains rare. Payments for Ecosystem Services (PES) incentivise land users to manage land to provide environmental benefits. We present the first RCT evaluation of a PES program aiming to improve water quality. Watershared is a program which incentivises landowners to avoid deforestation and exclude cattle from riparian forests. Using this unusual landscape-scale experiment we explore the efficacy of Watershared at improving water quality, and draw lessons for future RCT evaluations of landscape-scale environmental management interventions. Methods One hundred and twenty-nine communities in the Bolivian Andes were randomly allocated to treatment (offered Watershared agreements) or control (not offered agreements) following baseline data collection (including Escherichia coli contamination in most communities) in 2010. We collected end-line data in 2015. Using our end-line data, we explored the extent to which variables associated with the intervention (e.g. cattle exclusion, absence of faeces) predict water quality locally. We then investigated the efficacy of the intervention at improving water quality at the landscape scale using the RCT. This analysis was done in two ways; for the subset of communities for which we have both baseline and end-line data from identical locations we used difference-in-differences (matching on baseline water quality), for all sites we compared control and treatment at end-line controlling for selected predictors of water quality. Results The presence of cattle faeces in water adversely affected water quality suggesting excluding cattle has a positive impact on water quality locally. However, both the matched difference-in-differences analysis and the comparison between treatment and control communities at end-line suggested Watershared was not effective at reducing E. coli contamination at the landscape scale. Uptake of Watershared agreements was very low and the most important land from a water quality perspective (land around water intakes) was seldom enrolled. Discussion Although excluding cattle may have a positive local impact on water quality, higher uptake and better targeting would be required to achieve a significant impact on the quality of water consumed in the communities. Although RCTs potentially have an important role to play in building the evidence base for approaches such as PES, they are far from straightforward to implement. In this case, the randomised trial was not central to concluding that Watershared had not produced a landscape scale impact. We suggest that this RCT provides valuable lessons for future use of randomised experiments to evaluate landscape-scale environmental management interventions