Abstract
A new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is developed that attempts to overcome limitations in existing faecal indicator organism (FIO) models arising from coarse spatial discretisations and poorly-constrained hydrological processes. MAFIO is a spatially-distributed, process-based model presently designed to simulate the fate and transport of agents representing FIOs shed by livestock at the sub-field scale in small (<10 km2) agricultural catchments. Specifically, FIO loading, die-off, detachment, surface routing, seepage and channel routing are modelled on a regular spatial grid. Central to MAFIO is that hydrological transfer mechanisms are simulated based on a hydrological environment generated by an external model for which it is possible to robustly determine the accuracy of simulated catchment hydrological functioning. The spatially-distributed, tracer-aided ecohydrological model EcH2O-iso is highlighted as a possible hydrological environment generator. The present paper provides a rationale for and description of MAFIO, whilst a companion paper applies the model in a small agricultural catchment in Scotland to provide a proof-of-concept.
Original language | English |
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Article number | 110903 |
Number of pages | 18 |
Journal | Journal of Environmental Management |
Volume | 270 |
Early online date | 14 Jul 2020 |
DOIs | |
Publication status | Published - 15 Sep 2020 |
Keywords
- Diffuse pollution
- E. coli
- EcH2O-iso
- Microbial water quality
- Tracer-aided modelling
- Water quality modelling
- EcH O-iso
- CATCHMENT-SCALE
- CELLULAR-AUTOMATA
- EcH(2)O-iso
- HYDROLOGICAL CONNECTIVITY
- ESCHERICHIA-COLI
- FRESH-WATER
- E. COLI
- ECOHYDROLOGIC SEPARATION
- LAND-USE
- WATER-QUALITY
- SURFACE WATERS
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MAFIO: Model for the Agent-based simulation of Faecal Indicator Organisms
Neill, A. J. (Creator), Tetzlaff, D. (Supervisor), Strachan, N. (Supervisor), Hough, R. L. (Supervisor), Avery, L. M. (Supervisor), Kuppel, S. (Other), Maneta, M. (Other) & Soulsby, C. (Supervisor), University of Aberdeen, 28 Oct 2019
DOI: 10.20392/66f74663-ece3-4a52-8bed-f0cf52d0831a
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