Modeling Copper Runoff in San Francisco Bay Area Watersheds Jim Carleton US EPA

BPP Technical Studies Copper Source Loading Estimates (Process Profiles) Physical & Chemical Characterization of Wear Debris (Clemson University) Water Quality Monitoring (ACCWP) Steering Committee, Scientific Advisory Team, and Stakeholder Involvement Process (Sustainable Conservation) Air Deposition Modeling (AER) Watershed Modeling (U.S. EPA) Bay Modeling (URS) Final Report Data Assessment Conclusions Air Deposition Monitoring (SFEI) Representative Sample of Brake Pad Wear Debris (BMC/Link Test Labs)

Copper Build-up on Impervious Surfaces

Copper on Suspended Sediment in Bay Area Streams Geometric mean 140 mg/kg Soil background 25 mg/kg

Key Questions for Watershed Modeling What are deposition rates of copper on pervious and impervious surfaces in the San Francisco Bay area? How much of the deposited copper comes from brake pads? How much of the copper that is deposited on pervious surfaces ends up transported to streams (and the bay) in runoff?

Watershed Modeling with HSPF Castro Valley watershed model used to estimate build-up and washoff parameters. –First calibrate stream flow using local precipitation data as input. –Next calibrate suspended sediment and copper against monitoring data. Using build-up/washoff parameters from Castro model, run simulation of greater Bay area (multi- watershed) to estimate total loads to Bay.

Castro Valley watershed Guadalupe River watershed Bay Area Watersheds

Castro Valley HSPF Initial Calibration Using annualized loadings from initial air deposition monitoring

Guadalupe River HSPF Initial Validation Using annualized loadings from initial air deposition monitoring

Future Activities Incorporation of modeled source loadings, and will need to look at: –Importance of suspended sediment (which is hard to model) –Uncertainties in apportioning pervious and impervious land loadings to copper in stream water column –Sensitivity of model results to choice of model parameters Approaches –Calibration using current data –Checking against historical data –Staying within literature ranges of parameters –Best professional judgment