An empirical modeling approach to predicting pollutant loads and developing cost-effective stormwater treatment strategies for a large urban watershed

Highlights: • Empirical stormwater quantity and quality models were developed and validated. • Zeroth-order regularization with a L-BFGS-B algorithm was used in the models. • Two subwatersheds with high leverages were identified in a large urban watershed. • Transportation landuse was revealed as a high leverage pollutant emitter. • Using an optimal strategy, the Zn water quality goal can be achieved at 75% less cost. Stormwater treatment strategies were evaluated for the upper Ballona Creek Watershed in Los Angeles, CA using an empirical model of stormwater runoff quantity and quality with zeroth-order regularization and a limited memory Broyden-Fletcher-Goldfarb-Shanno Bound constrained optimization routine. The model used landuse based estimation on the runoff volume, event mean concentration (EMC) and pollutant load employing ten different landuses, including highways and local roads. The model was validated by showing that its predictions were in reasonable agreement (r² ~0.6 to 0.8) with total zinc (Zn), Total Kjeldahl Nitrogen (TKN), and Total Suspended Solids (TSS) loadings measured at the monitoring site at the bottom of the watershed. The developed model was used to demonstrate and quantify the benefits of the stormwater treatment practices (STPs) prioritized at specific landuses with high pollutant mass emission rates. For this demonstration, total Zn was selected as it is one of the most concerning pollutants in an extremely urbanized area such as the Ballona Creek Watershed. Transportation landuse including local roads and highways was found to be the best candidate for the STP applications due to their high percent load contribution per percent area. By focusing STPs for transportation landuse, the water quality goal of total Zn in the study watershed was expected be achieved at approximately 75% less cost.