A Multi-Metric, Data-Driven Index to Evaluate BMP Water Quality and Hydrologic Mitigation Performance

Abstract Submission: The Los Angeles County Department of Public Works (County) annually invests over $280M for stormwater quality project planning, design and implementation to comply with NPDES permits and TMDL objectives. The magnitude of the program warrants on-going evaluation of technical solutions such as best management practices (BMPs). Pollutant treatment and runoff reduction are primary goals of BMPs; however, few BMPs can treat all concerns equally. Operating and maintenance conditions influence BMP functionality. Interpreting the success or failure of a BMP using common metrics such as %- removals requires subjective judgement of the reviewer and cannot be transferred amongst or between BMPs or sites.
To support the County, the Southern California Coastal Water Research Project (SCCWRP) developed performance indices for BMP water quality treatment and hydrology (runoff capture volume) derived from quantitative field monitoring data of influent and effluent runoff. The key attributes of the indices include customization to watershed-specific objectives, and clear interpretation of index scores to inform future planning or corrective actions. Index scores can be calculated for any water quality parameter for which monitoring data are available, as well as runoff volume management. An analytical hierarchy procedure is applied to combine individual index scores to an overall performance evaluation, according to user prioritizations of each parameter’s importance. Indices benchmark performance against specific water quality and runoff capture volumes design goals, while considering operating conditions on a storm-by-storm basis. The index value enables objective assessments across a range of pollutant types, water quality goals, design specifications and/or implementation across a wide variety of landscape settings. The unbiased and transferable metrics enable post-construction performance evaluation of a unique BMP, or amongst BMPs of the same or different types, while deconstructing metrics provide information to support maintenance and planning, for example by tracking performance over time.

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