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.

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 (r=0.88; p<0.01) than the Wyre (r=-0.36; 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.

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37Understanding the role of different rainfall scenarios on faecal indicator organism (FIO) 38 dynamics under variable field conditions is important to strengthen the evidence-base on 39 which regulators and land managers can base informed decisions regarding diffuse 40 microbial pollution risks. We sought to investigate the impact of low intensity summer rainfall 41 on E. coli -discharge (Q) patterns observed at the headwater catchment scale in order to 42 provide new empirical data on FIO concentrations observed during base-flow conditions. In 43 addition, we evaluated the potential impact of using automatic samplers to collect and store 44 freshwater samples for subsequent microbial analysis during summer storm sampling 45 campaigns. The temporal variation of E. coli concentrations with Q was captured during six 46 events throughout a relatively dry summer in central Scotland. The relationship between E. 47 coli concentration and Q was complex with no discernible patterns of cell emergence with Q 48 that were repeated across all events. On several occasions an order of magnitude increase 49 in E. coli concentrations occurred even with slight increases in Q, but responses were not 50 consistent and highlighted the challenges of attempting to characterise temporal responses 51 of E. coli concentrations relative to Q during low intensity rainfall. Cross-comparison of E. 52 coli concentrations determined in water samples using simultaneous manual grab and 53 automated sample collection was undertaken with no difference in concentrations observed 54 between methods. However, the duration of sample storage within the autosampler unit was 55 found to be more problematic in terms of impacting on the representativeness of microbial 56 water quality, with unrefrigerated autosamplers exhibiting significantly different 57 concentrations of E. coli relative to initial samples after 12 hours storage. The findings from 58 this study provide important empirical contributions to the growing evidence-base in the field 59 of catchment microbial dynamics.