EAPSI: Connecting Distributed Impacts in Urban Watersheds to In-stream Hydrology and Water Quality Observations through Refined Landscape Metrics for Optimal Stormwater Handling

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Stormwater moves through urban watersheds via a complex network over pervious surfaces that infiltrate runoff and impervious surfaces that quickly move runoff towards streams. Impervious areas and the degree to which they are connected by drainage pipes have been shown to play the greatest role in shifting streamflow dynamics and water quality in urban streams leading to degraded conditions worldwide. This has spurred the adoption of more natural stormwater handling with green infrastructure practices distributed throughout an urban watershed to handle runoff issues at the source. Because urban land costs are high, guidance is needed to better site these installations in order to maximize their effectiveness to meet local drainage needs and contribute to improvements in stream conditions. This project will explore novel methods of urban drainage assessment that account for natural and piped runoff pathways and identify areas that contribute more to stream degradation based on measures of impervious surface connectivity. Methodology will be explored with the guidance of Dr. Tim Fletcher at University of Melbourne and Dr. David McCarthy at Monash University using established spatial datasets that include impervious areas and their connectivity for comparison and validation. Different flowpath weighting schemes will be explored using runoff and water quality datasets and a temporal analysis will focus on the Little Stringybark experimental watershed to assess how changes in impervious connectivity over time associated with green infrastructure installations relate to observed changes in streamflow and water quality. The connectivity of impervious surfaces in urban watersheds has often been represented as binary while it has been acknowledged that connectivity actually exists on a continuum subject to storm-specific characteristics, antecedent moisture conditions, and variable flowpath disconnection. This project will utilize high-resolution spatial data representing elevation, impervious cover, and stormwater drainage networks to establish a relative connectivity index that provides both a landscape-scale weighted metric of imperviousness and spatially explicit information that identifies critical source areas within the urban watershed more closely tied to in-stream measures. Python scripting will employ a grid-based representation of watershed surface cover and runoff flowpaths that will measure connectivity by investigating weighting schema for different portions of the flowpath that runoff takes across the watershed (overland, impervious, piped, in-stream). These weighting schema will be calibrated to optimize the fit of linear models relating the associated landscape metric to in-stream observations, and differences in parameterization between watersheds will be investigated to assess how effectively they represent physical watershed characteristics and runoff transport mechanisms. This will help validate the methodology and provide guidance to establish a tool that can be used on ungauged watersheds to identify areas within any urban watershed that will be best served by green infrastructure installation to decrease impervious connectivity and impart positive changes in-stream through distributed means. This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the Australian Academy of Science.