A New Agent-Based Model for Assessing the Spatio-Temporal Dynamics of Sources and Transfer Mechanisms Contributing Faecal Indicator Organisms to Streams
Abstract
Managing microbial water quality requires an understanding of the processes underlying dynamics of faecal indicator organisms (FIOs) in streams at scales relevant for spatially targeting mitigation measures. Here, we present a new agent-based model (MAFIO) that simulates the small-scale behaviour of FIO-agents in a spatially-distributed, process-based manner, in order to elucidate the sources and transfer mechanisms contributing FIOs to streams at the sub-field scale. The model was evaluated through application to a 0.41 km2 agricultural catchment to simulate the dynamics of E. coli. Hydrological transfer mechanisms in MAFIO were driven by a hydrological environment simulated by the tracer-aided ecohydrological model EcH2O-iso following multi-criteria calibration to stream discharge and spatially-distributed δ2H data for April-December 2017. MAFIO was then applied to simulate bi-weekly E. coli loads quantified at three stream sites for the period June-August. Model performance gave reasonable confidence that MAFIO captures the general processes driving the transfer of FIOs from livestock to streams. However, more nuanced simulations may be possible with greater consideration of issues of scale, model calibration and FIO contributions from wildlife. MAFIO was also shown to have significant potential in providing management-relevant insights. Interrogating the attributes of FIO-agents routed out of the catchment revealed highly-localised source areas reflecting seepage from a few areas of degraded soil close to the stream and limited areas of overland flow generation; this implies that small-scale interventions may cause significant improvements to microbial water quality. Attributes could also be used to assess contributions from different livestock types, which could aid in estimating potential pathogen prevalence in the stream and consequent risk of infection to humans. Finally, stochastic process representation meant that uncertainty arising from unpredictability in processes such as direct defecation in the stream could be accounted for; this may be valuable in a decision-making context. Overall, the encouraging performance of MAFIO suggests that it may have promise for eventual incorporation into decision-support frameworks aimed at improving microbial water quality.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H34C..01N
- Keywords:
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- 0414 Biogeochemical cycles;
- processes;
- and modeling;
- BIOGEOSCIENCES;
- 0432 Contaminant and organic biogeochemistry;
- BIOGEOSCIENCES;
- 1834 Human impacts;
- HYDROLOGY;
- 1880 Water management;
- HYDROLOGY