Skip to main content


eCommons@Cornell

eCommons@Cornell >
Internet-First University Press >
Symposia, Workshops, Conferences >
Hydrologic Discovery Through Physical Analysis Honoring the Scientific Legacies of Wilfried H. Brutsaert and Jean-Yves Parlange >
Hydrologic Discovery - Posters >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/29584
Title: A7. Decision Making and Analysis Tools for Bio-surveillance and Sustainable Watershed Management
Authors: Safwat, Amr
Teklitz, Allen
Whiteaker, Tim
Nietch, Christopher
Maidment, David
Best, Elly P.H.
Yeghiazarian, Lilit
Issue Date: May-2012
Publisher: Internet-First University Press
Abstract: The 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.
URI: http://hdl.handle.net/1813/29584
Appears in Collections:Hydrologic Discovery - Posters

Files in This Item:

File Description SizeFormat
A7_Lilit_poster.pdf22.26 MBAdobe PDFView/Open

Refworks Export

Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2014 Cornell University Library Contact Us