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dc.contributor.authorSafwat, Amr
dc.contributor.authorTeklitz, Allen
dc.contributor.authorWhiteaker, Tim
dc.contributor.authorNietch, Christopher
dc.contributor.authorMaidment, David
dc.contributor.authorBest, Elly P.H.
dc.contributor.authorYeghiazarian, Lilit
dc.date.accessioned2012-07-23T15:18:38Z
dc.date.available2012-07-23T15:18:38Z
dc.date.issued2012-05
dc.identifier.urihttps://hdl.handle.net/1813/29584
dc.description.abstractThe biggest challenges in mitigating water contamination with chemical and biological agents are (1) identification of their sources, and (2) lack of real- or near-real time assessment of environmental processes. This problem is exacerbated by the heterogeneous distribution of contaminants in time and space. Any watershed management decisions must therefore be made under conditions of uncertainty. This is the focus of the ongoing work at UC’s Multi-Scale Environmental Modeling Lab, which brings into a common, systems-based framework several aspects of watershed management. In this framework, strategic and optimized biosurveillance yields multi-scale data that include environmental information coupled with microbial concentrations, genetic sequences and host-specific information from environmental samples. These data are used in stochastic models of microbial dynamics and nutrient transport that capture their interactions with sediment transport in watersheds. Results embedded into GIS are employed to develop risk and vulnerability maps, which in turn are used to inform decisions on surveillance strategies and watershed management. We show two applications in Little Miami River’s East Fork Watershed in Southeast Ohio. The first application couples a stochastic microbial transport model with an erosion model (the Water Erosion Prediction Project – WEPP) to better understand transport and partitioning of fecal contaminants in overland and stream flow. The second develops spatial probability maps that indicate probabilities of exceeding the nitrogen standard in various hydrologic regimes. This effort is based on load-resistance models borrowed from structural engineering, which provide methodology to estimate failure in complex structures. Both models are implemented in ArcGIS’s Schematic Processor, a suite of geoprocessing tools expanded to accommodate for complexities of microbial and nutrient transport in watersheds.en_US
dc.publisherInternet-First University Pressen_US
dc.titleA7. Decision Making and Analysis Tools for Bio-surveillance and Sustainable Watershed Managementen_US


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