In the context of stormwater, the term first flush is typically used in conjunction with runoff pollution control, and is often referenced in local and regional regulations in terms of total depth and duration (i.e. 0.75 inch, 1-hour duration). The first flush is typically used for estimating volumetric and flow rate design criteria for infrastructure targeting water quality enhancements, such as retrofits to urban areas or new development. The capital investment necessary for water quality enhancements are therefore directly related to the first flush rainfall volume.
Hundreds of references exist to approximate the first flush flow rate and volume, due to the proliferation of local, regional, and national standards targeting frequent storm hydrology for pollution control. However, these standards are predominantly based on rainfall depth statistics and the selection of a prescribed rainfall volume. Experience obtained from stormwater flow metering data suggests that there is not always a direct correlation between the volume of precipitation on a watershed and the resulting runoff volume and peak flows. In fact, runoff volume and peak flows vary continuously with rainfall volume, rainfall intensity, watershed runoff characteristics, and antecedent moisture (AM) conditions. These factors are influenced by a complex interaction of physical conditions unique to each watershed. This paper outlines an alternative method for developing a design first flush flow rate and volume using a frequency-based approach that considers flow metering data as well as publicly-available precipitation data typically available for most urban areas.
This paper includes examples of urban retrofits to address stormwater pollution control, comparing conventional sizing criteria (rainfall volume using event-based modeling) to frequency-based analysis of observed runoff characteristics using continuous modeling. These examples demonstrate that a more robust approach to determining first flush peak flows and volumes can result in more cost-efficient design and a higher confidence in the efficacy of structural controls on surface water quality.
The examples in this paper were developed using SWMM-based hydrologic and hydraulic modeling, Antecedent Moisture (AM) modeling of the hydrologic response, including calibration/validation of observed flow meter data, and frequency-based analysis of flow volumes from a continuous model developed using the validated AM model.