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Watershed Integrity: Refinement and Case Study Development

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Expiration Date: 


Under EPA's Safe and Sustainable Water Resources National Program, an Index of Watershed Integrity (IWI) has been defined (Flotemersch et al. 2015) and mapped for the conterminous US (Thornbrugh et al. 2018). This research project may consist of the following four elements: (1) Enhancing the IWI map -- The original IWI map was based on hypothesized relationships between stressors and ecological functions, using first order, linear assumptions and no weighting. An approach to enhancing the IWI map by defining empirical relationships using random forests has been developed for the water quality function (Johnson et al. submitted). The research participant may be involved in enhancing the IWI map by more accurately characterizing the relationships between stressors and the remaining five functions that are incorporated into the index using literature and available data; (2) Testing the IWI map -- The IWI map has been tested by comparing results to stream condition data from case study watersheds and national surveys (Kuhn et al. 2018; Thornbrugh et al. 2018), and in conjunction with #3 below, the research participant will acquire regional data to further test the IWI maps; (3) Regional applications -- The research participant may develop one or more regional case studies demonstrating the utility of the IWI maps for aquatic resource management; and (4) Temporal change -- A map of partial change in IWI over time with respect to specific stressors may also be produced, dependent on the availability of new spatial datasets.


The applicant should have a Ph.D. in aquatic ecology, ecohydrology, watershed hydrology, or related field with a strong background in aquatic ecology, spatial analysis, and statistics.  The degree must be received within five years of the appointment start date.  Experience in watershed or statistical modeling and spatial analyses at broad spatial scales and use of aquatic monitoring data and GIS analyses is desired.

Candidates should have a strong background in landscape analysis of aquatic systems.  Experience with ArcGIS, R statistical software, random forest modeling, and large national datasets preferred, along with experience with working with watershed data and the National Hydrography Dataset Plus V2.