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Spatial and Temporal Modeling of Microbial Contaminants on Grazing Farmlands
Author(s) -
Tian Yong Q.,
Gong Peng,
Radke John D.,
Scarborough James
Publication year - 2002
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2002.8600
Subject(s) - environmental science , fecal coliform , hydrology (agriculture) , streams , indicator bacteria , contamination , spatial heterogeneity , grazing , population , watershed , drainage basin , land use , water quality , ecology , geography , computer science , geology , biology , sociology , computer network , demography , geotechnical engineering , cartography , machine learning
This paper introduces an integrated spatial and temporal modeling system developed mathematically for assessing microbial contaminants on animal‐grazed farmlands. The model uses fecal coliform, specifically Escherichia coli , as an indicator of fecal contamination and describes the sources, sinks, transport processes, and fate of E. coli contaminants in catchments and associated streams. Spatial features include grazing location, land topography, distance to a nearby stream, and distance through the stream network to the outlet. Temporal features are population dynamics on the land surface, in flow, and on streambeds. The model applies the principles of conservation of mass balance on two different types of pools: grid cells on land surfaces and networked stream segments. The model aims to improve the prediction of the effects of different land management strategies on the fecal contamination of waterways. This is achieved by characterizing the movement of fecal contaminants from land to streams and in‐stream mobilization. Processes of attenuation, diffusion, and transport govern the movement. Our study site is a hill land catchment with an area of 140 ha and is used exclusively for animal grazing. The model was calibrated with previous research results, and then tested using the data collected at the outlet of the catchment. The sensitivity of the model predictions was analyzed for different scenarios: effect of stock rate, attenuation rate, and flow volumes. The similar pattern between monitored and predicted E. coli concentration proved that the model captures the key features that control the population dynamics of fecal contaminants. Further experiments are required to expand the model's functionality for covering more mitigation options.