Environmental Assessment ~~~ Modeling ~~~ Water Resources 

Modeling the Contribution of Copper from 

Brake Pad Wear Debris to the San Francisco Bay 

 

Prepared by 

A. S. Donigian, Jr. 

B. R. Bicknell 

AQUA TERRA Consultants 

2685 Marine Way, Suite 1314 

Mountain View, CA 94043 

Submitted to 

Association of Bay Area Governments 

San Francisco Estuary Project 

Oakland, CA 94612 

and 

California Department of Transportation 

Sacramento, CA 

October 2, 2007 
CONTENTS 

 Page 

Figures............................................................................................................................... iii 

Tables ................................................................................................................................ iv 

Acknowledgements .......................................................................................................... v 

1.0 Introduction................................................................................................................. 1 

 1.1 Executive Summary. ......................................................................................... 1 

 1.2 Background and Objectives. ............................................................................. 4 

 1.3 This Report........................................................................................................ 7 

2.0 Model Setup for San Francisco Bay Sub-Watersheds ............................................ 8 

 2.1 San Francisco Bay Sub-Watershed Boundaries and Hydrography .................. 8 

 2.2 Meteorologic Data Inputs ................................................................................... 10 

 2.3 Land Use, Soils, Slopes ..................................................................................... 15 

 2.4 Irrigation............................................................................................................. 19 

 2.5 Copper Sources and Release (Flux) Estimates ................................................ 20 

3.0 Model Application, Operations, and Results............................................................ 29 

 3.1 Model Application Approach.............................................................................. 29 

 3.2 Model Consistency Checks and Quality Assurance.......................................... 30 

 3.2.1 Hydrology Consistency Checks ................................................................. 30 

 3.2.2 Sediment and Copper Consistency Checks .............................................. 35 

 3.3 Model Scenario Runs and Results.................................................................... 41 

4.0 Recommendations for Model Improvements ........................................................... 53 

5.0 References................................................................................................................... 55 

 AQUA TERRA Consultants ii
 FIGURES 

Figure 1.1 Brake Pad, Anthropogenic Non-Brake Pad, and Sediment (Background) 

 Copper Contributions in Runoff to the San Francisco Bay.......................... 3 

Figure 1.2 Brake Pad Partnership Technical Studies................................................... 5 

Figure 1.3 San Francisco Bay Watershed Study Area and Brake Pad Partnership 

 Modeled Watersheds .................................................................................. 6 

Figure 2.1 Sub-Watershed and County Boundaries, Hydrography and Reservoirs ..... 9 

Figure 2.2 Precipitation Gages, Thiessen Polygons, and Isohyetal Pattern................. 13 

Figure 2.3 CIMIS Zones for San Francisco Bay Area......................................................... 14 

Figure 2.4 Model Land Use Categories Across the Bay Area ...................................... 17 

Figure 2.5 Hydrologic Soil Groups for Bay Area Counties............................................ 18 

Figure 2.6 Copper Flux Representation in the Watershed Model................................. 23 

Figure 3.1 USGS Station Locations Used for Hydrology Consistency Checks ............ 31 

Figure 3.2 Flow Duration Curve Model Results for Selected Bay Area 

 Sub-Watersheds.......................................................................................... 32 

Figure 3.3 Flow Duration Curve Model Results for Additional Bay Area 

 Sub-Watersheds.......................................................................................... 33 

Figure 3.4 TSS and Total Copper Concentrations for Castro Valley Creek ................. 37 

Figure 3.5 Identification of ‘Outliers’ as Data Points Observed at Low Flows .................. 38 

Figure 3.6 TSS and Total Copper Concentrations for Guadalupe River in San Jose... 39 

Figure 3.7 TSS and Total Copper Concentrations for San Lorenzo River.................... 40 

Figure 3.8 Total Copper Concentrations for Alameda Creek........................................ 41 

Figure 3.9 Mean Annual Copper Fluxes in the Watershed Model (1000 kg/yr)............ 47 

Figure 3.10 Model Results for the Guadalupe River........................................................... 49 

Figure 3.11 Model Results for Alameda Creek............................................................... 50 

Figure 3.12 Model Results for San Lorenzo Creek ........................................................ 51 

Figure 3.13 Model Results for the Napa River................................................................ 52 

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TABLES 

Table 1.1 Summary of Mean Annual Copper Loads In Runoff to San Francisco Bay 

 for Alternative Scenarios ............................................................................. 2 

Table 2.1 Sub-Watershed Rainfall and Evaporation Zones ........................................ 12 

Table 2.2 Land Cover Types, Model Categories, and Percent of Area....................... 15 

Table 2.3 Final Model Land Use Categories and Areas for 22 Bay Area 

 Sub-Watersheds.......................................................................................... 16 

Table 2.4 Mean Slope of Each Modeled Land Use Category within the San Francisco 

 Bay Sub-Watersheds ......................................................................................... 19 

Table 2.5 Copper Releases for the ‘Median Estimate’ Scenario....................................... 24 

Table 2.6 Copper Releases for the ‘Brakes – High’ Scenario ........................................... 25 

Table 2.7 Copper Releases for the ‘Brakes – Low’ Scenario............................................ 26 

Table 2.8 Estimated Dry and Wet Deposition Fluxes of Copper....................................... 27 

Table 2.9 Calculation of Wet Deposition Copper Concentrations in Precipitation ............ 28 

Table 3.1 USGS Stations Used in Hydrology Consistency Checks............................... 30 

Table 3.2 Model-Data Comparisons for Selected USGS Flow Stations........................ 34 

Table 3.3 Summary of Watershed Model Results for Bay Area Sub-Watersheds ....... 45 

Table 3.4 Brake Pad, Anthropogenic Non-Brake Pad, and Sediment (Background) 

Contributions of Copper from Each Bay Area Sub-Watershed ..................... 46 

Table 3.5 Brake Pad Contributions of Total Anthropogenic Copper Loads to San 

 Francisco Bay .................................................................................................... 48 

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ACKNOWLEDGEMENTS 

A number of individuals were instrumental in supporting the successful initiation, execution, and 

completion of this effort, and their contributions are sincerely acknowledged. 

The study was funded by the California Department of Transportation. Mr. Karl Dreher in the 

Division of Environmental Analysis is acknowledged for his support and ongoing interest 

throughout the study. 

Dr. Sarah Connick of Sustainable Conservation was our primary contact in initiating and scoping 

the study effort, and Ms. Kirsten Rosselot of Process Profiles performed as the Project Manager 

with considerable technical input and review, in addition to her role in developing the copper 

release inventory and estimates for the modeling. Both individuals were critical to the 

performance of the work and coordination with the Brake Pad Partnership Steering Committee. 

Contracting was performed through the Association of Bay Area Governments, and Ms Marcia 

Brockbank of the San Francisco Estuary Project was the Contract Manager. Her assistance in 

the administration of the effort is greatly appreciated. 

The following individuals provided data, information, and/or critical review comments at various 

milestones of the project: 

Ms. Jill Bicknell, Eisenberg, Olivieri, & Associates 

Ms. Arleen Feng, Alameda Countywide Clean Water Program 

Mr. Matt Fabry, San Mateo Countywide Stormwater Pollution Prevention Program 

Mr. Douglas Beyerlein, Clear Creek Solutions, Inc. 

The Brake Pad Partnership also sponsored a distinguished panel of expert reviewers, listed 

below, whose comments and reviews on the modeling approach, results, and draft report were 

insightful and greatly appreciated. 

Dr. Robert Ambrose, US EPA National Exposure Research Laboratory 

Dr. Lester McKee, San Francisco Estuary Institute 

Dr. Wayne Huber, Oregon State University 

Dr. John Sansalone, University of Florida 

Dr. Ken Schiff, Southern California Coastal Water Research Program 

Mr. Jim Carleton of the U.S. EPA, Office of Water/Office of Science and Technology, was the 

prime mover in the initial development of the watershed modeling effort, including development 

of the Watershed Modeling Work Plan. Mr. Carleton also performed model calibrations for 

Castro Valley Creek that were critical to the completion of the work effort. 

For AQUA TERRA, Mr. Tony Donigian was the Project Manager responsible for the overall 

conduct of the work and preparation of the Final Report. He was assisted by: Mr. Brian Bicknell 

who performed the model runs and results analyses; Dr. Driss Ennaanay and Mr. Marco 

Palmeri who performed various data management, analysis, and GIS tasks; Mr.Paul Hummel 

who developed the meteorologic database; and Mr. Paul Duda who performed the initial model 

setup for all 22 sub-watersheds.

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Introduction

SECTION 1.0 

INTRODUCTION

1.1 EXECUTIVE SUMMARY 

This watershed modeling effort is being conducted as part of a larger study by the Brake Pad 

Partnership (BPP) that examines the potential impact of copper from brake pad wear debris 

released to the environment. The Brake Pad Partnership's source release inventory, water 

quality monitoring, and air deposition monitoring studies were specifically prepared to provide 

input data for this watershed modeling effort. Other Brake Pad Partnership studies, such as air 

deposition monitoring, procurement of a representative sample of brake pad wear debris, and 

physical and chemical characterization of brake pad wear debris, indirectly provided information 

that supported this modeling effort. Partnership studies were completed with the cooperative 

oversight of the Brake Pad Partnership Steering Committee and were peer reviewed by the 

BPP's Scientific Advisory Team. 

The objective of the environmental transport and fate modeling is to predict the relative 

contribution of copper released from brake pads in the Bay area and how the contribution from 

brake pads affects both the short-term and long-term concentrations of copper in the Bay. This 

report describes the watershed modeling portion of those studies. 

With local data for land use, soils, topography, and meteorology, the U.S. EPA's Hydrological 

Simulation Program-FORTRAN (HSPF) model was set up for each of the 22 Brake Pad 

Partnership modeled sub-watersheds that drain to the San Francisco Bay. Model parameters and 

copper sources associated with deposition of copper onto landscape surfaces were obtained 

from the results of atmospheric deposition modeling conducted for the Brake Pad Partnership 

by AER, Inc (Pun, 2007) and from release inventory values of brake and non-brake sources 

from Rosselot (2006a, 2006b). HSPF Model runs were performed for each sub-watershed for the 

entire time period of water year 1981 through water year 2005, i.e. October 1980 through 

September 2005. 

There is a great deal of uncertainty in both the non-brake and brake release estimates, and taking 

that uncertainty into account when determining whether the contribution from brake pads is 

substantial was necessary. Thus, three cases of copper release (flux) scenarios were modeled, 

one called brakes-high, one called brakes-low, and one called median estimate. These three 

scenarios were selected because results based on them adequately represent the range of relative 

contribution of copper released from brakes, and because they take the uncertainty in both brake 

and non-brake releases into account. One scenario is based on the point value presented in the 

copper release inventories for both brake sources and non-brake sources; this scenario is called 

the median estimate case. A second scenario, called the brakes-low case, explores the source 

term estimates from the perspective that the point values in the release inventory overestimate 

brake contributions relative to non-brake sources. The third scenario, called the brakes-high case, 

explores the source terms from the perspective that the point values in the release inventory 

underestimate brake contributions relative to non-brake sources of copper. Each of these scenarios 

was modeled with and without releases from brake pads (for a total of six scenarios) in order to 

determine the relative contribution of copper from brake pads in runoff to the Bay. Standard 

uncertainties for copper release estimates in the Bay area were presented in Rosselot (2007a, 

2007b) and Pun (2007). 

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Introduction

Table 1.1 shows the mean annual loads of copper in runoff to the San Francisco Bay for each of 

the six scenarios. The total load of about 56,000 kg/y compares well with the SFEI preliminary 

estimate of 66,000 kg/y (within a range of 36,000 to 110,000 kg/y) using a relatively simple runoffcoefficient model (Davis et al, 2000). Note that even though there is a great deal of uncertainty in 

the copper release estimates that were used to produce these values, the total copper loads for the 

three cases of copper release scenarios are about the same because the high end of brake pad 

contributions to runoff is offset by the low end of non-brake pad contributions to runoff for the 

brakes-low case, and vice versa for the brakes-high case. For the three scenarios of Brakes - High, 

Median Estimate, and Brakes - Low, the brake pad contributions of copper in runoff vary from 35% 

to 10% of the total copper loads to the Bay. 

Table 1.1 Summary of Mean Annual Copper Loads in Runoff to San Francisco Bay for 

Alternative Scenarios 

Total Loads in 

Runoff*

kg Cu/y kg Cu/y % kg Cu/y %

55,907 36,360 65% 19,547 35%

56,465 43,632 77% 12,833 23%

56,769 50,914 90% 5,854 10%

*Includes background copper loading in sediment.

Non-Brake Pad 

Contribution* Brake Pad Contribution

Scenarios

 Brakes - Low

 Median Estimate

 Brakes - High

The model results for the median estimate case were analyzed to distribute and determine the 

contribution of the total copper load to the Bay from three sources -- from brake pad wear debris, 

anthropogenic non-brake pad wear debris, and sediment/background soil levels -- from each subwatershed and the total from all Bay Area sub-watersheds. Figure 1.1 graphically displays the 

contributions of these three components of the total copper load, in terms of the percentage 

distribution for each Brake Pad Partnership modeled watershed for the median estimate case. 

It shows a significant variation in these percentages among the Brake Pad Partnership modeled 

watersheds. 

Among the Brake Pad Partnership modeled watersheds, the total contribution from brake pad 

wear debris towards total anthropogenic loads of copper to the Bay for the median estimate 

case varies from 15% (for the Sonoma sub-watershed) to 57% (for the Upper Colma subwatershed). For the rural sub-watersheds, the brake pad contribution is much lower than for the 

heavily urbanized sub-watersheds, such as Peninsula Central, Upper Colma, and Santa Clara 

Valley West. 

There are six sub-watersheds whose total copper load to the bay is larger than 4,000 kg/y. 

They are Contra Costa Central, East Bay Central, Napa, Petaluma, Santa Clara Valley West, 

and Sonoma. These six sub-watersheds contribute about 60% of the total copper load to the 

Bay. It's interesting that some of these sub-watersheds have their largest contribution from 

sediment (Napa, Petaluma, Sonoma), some have their largest contribution from non-brake pad 

anthropogenic sources (Contra Costa Central, East Bay Central), and one has its largest 

contribution from brake pad sources (Santa Clara Valley West). 

 AQUA TERRA Consultants 2
Introduction

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Contra Costa Central

Contra Costa West

Coyote Creek

East Bay Central

East Bay North

East Bay South

Marin North

Marin South

Napa

Peninsula Central

Petaluma

Santa Clara Central

Santa Clara West

Solano West

Sonoma

Upper Alameda

Upper Colma

Upper Corte Madera

Upper San Francisquito

San Lorenzo Creek

BP Anthro Non-BP Sediment

Figure 1.1 Brake Pad, Anthropogenic Non-Brake Pad, and Sediment 

(Background) Copper Contributions in Runoff to San Francisco Bay 

Because a lack of data and resources precluded performing a comprehensive calibration and 

validation effort on each of the 22 sub-watersheds, consistency and quality assurance checks on as 

many sites/stations as possible with readily available data were performed. This effort involved 

hydrology simulation checks with observations at selected USGS gage stations that were located at 

a number of the sub-watershed outlets. For sediment and copper checks, simulations were 

performed with the Castro Valley sub-watershed model setup from a previous study prepared for 

the Alameda Countywide Clean Water Program (ACCWP) (AQUA TERRA Consultants, 2006), 

supplemented with sediment and copper modeling capabilities. Limited calibration runs were 

performed by Jim Carleton of EPA/OST and model results were compared with the available 

copper monitoring data for Castro Valley Creek to determine selected copper washoff parameters. 

Model results were processed for flow, sediment and copper loads; annual and mean annual loads 

were tabulated; and daily flows and concentrations (both sediment and copper, total and dissolved) 

were reviewed as a quality assurance confirmation. In addition, selected sub-watersheds with 

limited observed data were plotted, and compared as an additional reasonability assessment. Mr. 

Carleton again assisted with this final quality assurance check. 

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Introduction

1.2 BACKGROUND AND OBJECTIVES

This watershed modeling effort is being conducted as part of a larger study by the Brake Pad 

Partnership (BPP) that examines the potential impact of copper from brake pad wear debris 

released to the environment (see Figure 1.2). The Brake Pad Partnership's source release 

inventory, water quality monitoring, and air deposition monitoring studies were specifically 

prepared to provide input data for this watershed modeling effort. Other Brake Pad Partnership 

studies, such as air deposition monitoring, procurement of a representative sample of brake pad 

wear debris, and physical and chemical characterization of brake pad wear debris, indirectly 

provided information that supported this modeling effort. Partnership studies were completed 

with the cooperative oversight of the Brake Pad Partnership Steering Committee and were peer 

reviewed by the BPP's Scientific Advisory Team. 

The objective of the BPP's environmental transport and fate modeling studies is to predict the 

relative contribution of copper released from brake pads in the Bay area and how the 

contribution from brake pads affects both the short-term and long-term concentrations of copper 

in the Bay. The watershed modeling portion of the studies, described in this report, provided 

estimates of sediment and total copper loads to San Francisco Bay from neighboring 

watersheds, which are shown in Figure 1.3. 

The U.S. EPA Office of Science and Technology (OST), working with the Brake Pad 

Partnership, developed a watershed modeling work plan that was subsequently reviewed and 

revised as a result of peer review comments. AQUA TERRA Consultants was contracted to 

perform the modeling as specified in the approved work plan (Carleton, 2004) but with selected 

refinements that were subsequently approved by the BPP Steering Committee. These 

refinements included the following: 

a. The meteorologic database to drive the model was expanded to include approximately 

20 precipitation gages to (1) respond to peer reviewers concerns, (2) better represent 

the micro-climates and rainfall variability within the Bay Area, and (3) take advantage of 

an ongoing effort by EPA to expand the meteorologic database available within the 

BASINS system, including the Bay Area counties. 

b. The land uses represented in the model were expanded from a single aggregated 

pervious category to five separate categories – developed, forest, shrub/wooded, 

grassland, agriculture – to better represent copper runoff sources and to better support 

model parameterization from local modeling efforts that specifically included separate 

land use classifications. 

c. The sub-watershed boundaries were used directly as described in the Watershed 

Modeling Work Plan (Carleton, 2004). However, selected large subbasins with major 

reservoirs were subdivided to allow extraction of non-contributing areas above the 

reservoirs, and additional stream reaches were added to connect those sub-watersheds 

separated from the Bay with a direct outlet for loading inputs to the Bay. The 

watersheds shown in Figure 1.1 are referred to as the ‘Brake Pad Partnership modeled 

watersheds’ or 'sub-watersheds' to distinguish them from other local watersheds with 

similar names but different boundaries. 

The watershed modeling package selected for this application is the U.S. EPA's Hydrological 

Simulation Program-FORTRAN (HSPF) (Bicknell et al., 1997; 2005). HSPF is a comprehensive 

watershed model of hydrology and water quality that includes modeling of both land surface and 

 AQUA TERRA Consultants 4
Introduction

subsurface hydrologic and water quality processes, linked and closely integrated with 

corresponding stream and reservoir processes. It is considered a complex, high-level model 

among those currently available for comprehensive watershed assessments. HSPF has 

enjoyed widespread usage and acceptance, since its initial release in 1980, as demonstrated 

through hundreds of applications across the U.S. and abroad. HSPF is jointly supported and 

maintained by both the U.S. EPA and the USGS. In addition, HSPF is the primary watershed 

model included in the EPA BASINS modeling system (U.S. EPA. 2001), and it has recently 

been incorporated into the U.S. Army Corps of Engineers Watershed Modeling System (WMS). 

This widespread usage and support has helped to ensure the continuing availability and 

maintenance of the code for more than two decades, in spite of varying federal priorities and 

budget restrictions. HSPF is currently being used for watershed studies in more than 25 states, 

Canada, and Australia, in addition to a number of watersheds in both Northern and Southern 

California. Moreover, HSPF has been applied to selected watersheds within the Bay Area, 

supplying experience with local conditions and parameter values, and was selected as the 

internal model for the Bay Area Hydrology Model, a design system for stormwater control and 

hydromodification assessments (Clear Creek Solutions, Inc., 2007). 

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 

(AQUA TERRA & 

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)

Figure 1.2 Brake Pad Partnership Technical Studies 

 AQUA TERRA Consultants 5
Introduction

Figure 1.3 San Francisco Bay Study Area and Brake Pad Partnership Modeled 

 Sub-Watersheds

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Introduction

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1.3 THIS REPORT

This report documents the results of the watershed modeling effort using HSPF to estimate 

sediment and copper loads to San Francisco Bay Section 2 describes the model setup 

process and the data used to both characterize the watershed conditions, such as land use, 

soils, topography, etc. and the meteorologic data of precipitation and evaporation that drives the 

model operations; it also discusses the copper sources and fluxes as represented in the model 

and derived from the various Brake Pad Partnership Technical Studies. Section 3 discusses 

the overall model application and approach, the various consistency checks performed on the 

model predictions, and describes the model results and scenarios that were performed to 

assess the potential variability in the copper loading estimates to the Bay. Section 4 provides 

recommendations for further model calibration and analyses, modeling approach improvements, 

and additional modeling studies to complement and further refine the copper load estimate 

results provided through this work. 
Model Setup

 AQUA TERRA Consultants 8

SECTION 2.0 

MODEL SETUP FOR SAN FRANCISCO BAY SUB-WATERSHEDS 

Watershed modeling with HSPF requires both spatial data to characterize the land area and time 

series data for precipitation and evaporation to drive the model functions and quantify the variability 

in meteorologic conditions across the micro-climates of the Bay Area. This section discusses the 

model setup process and both the spatial and meteorologic data used to develop the model and 

perform the model simulations. 

The spatial data is used to both determine the sub-watershed boundaries and then to characterize 

the land use, soils, slopes, and hydrography within each of the separate sub-watersheds that drain 

to the San Francisco Bay. The meteorologic data include only precipitation and evaporation; 

although snow occasionally occurs on some of the mountain tops of the Bay Area, such as Mount 

Diablo, Mount Hamilton and Mount Tamalpais, it rarely remains long enough to have a significant 

impact on local hydrology, and can usually be eliminated from the required model processes. Each 

of these data types and the specific data used in the model setup are discussed below. 

2.1 SAN FRANCISCO BAY WATERSHED BOUNDARIES AND HYDROGRAPHY

As noted above, the sub-watershed boundaries were determined by EPA and approved by the 

Brake Pad Partnership Steering Committee prior to initiation of the watershed modeling effort by 

AQUA TERRA. The boundaries mostly follow hydrologic divides and are entirely appropriate for 

the scale of the modeling effort. Figure 2.1 shows the 22 sub-watersheds defined for the San 

Francisco Bay modeling effort, along with their designated names and the primary streams 

modeled within each. The watershed model represents the local contributions of runoff, 

sediment, and copper to the Bay; contributions from the Sacramento/San Joaquin River Delta 

arenot include in the watershed model, but are represented as boundary conditions in the Bay 

modeling effort (URS, 2006). 

A number of the sub-watersheds shown in Figure 2.1 deviate from their ‘true’ hydrologic 

boundaries in that they are physically separated from the Bay, i.e. their hydraulic connection to 

the Bay is not included as part of the sub-watershed's area. For example, this occurs for Upper 

Corte Madera, Upper Colma, Upper San Francisquito, Santa Clara Valley Central, and San 

Lorenzo Creek. For each of these cases we have included in the model a stream reach, 

downstream of the ‘sub-watershed outlet’ so that the location of the true outlet to the Bay is 

defined for the Bay Model. 

Note that for many of the sub-watersheds that directly border the Bay, our model setup includes 

only a single stream for calculating the Bay inputs even though these inputs are physically 

distributed among a number of small streams, creeks, and storm drains. Thus our model setup, 

by necessity, aggregates all the watershed drainage into a single outlet for calculational 

purposes; the Bay model subsequently distributes these inputs among a number of defined 

boundary input locations. 

2.1.1 Hydrography, RCHRES, and FTABLE Development 

Stream segments for the model were derived from the National Hydrography Dataset (NHD) in 

BASINS (http://www.epa.gov/waterscience/basins/b3webdwn.htm). For each of the 22 Bay 
Model Setup

 AQUA TERRA Consultants 9

Area sub-watersheds, one stream was identified as the representative reach for that subwatershed. In many cases the ‘Level’ attribute from the NHD provided an indication of the most 

appropriate 

Figure 2.1 Sub-Watershed and County Boundaries, Hydrography and Reservoirs
Model Setup

 AQUA TERRA Consultants 10

choice of representative reach within each sub-watershed, as this attribute indicates stream 

order. Within some sub-watersheds multiple streams had been assigned the same ‘Level’ value 

in NHD, and so the stream with the greatest length was chosen to be the representative reach 

in those cases. 

Ten major reservoirs, with drainage areas in the range of 20 square miles of greater, were 

identified within the Bay Area sub-watersheds, and their contributing areas were subsequently 

excluded from the modeling. These reservoirs are shown in Figure 2.1, and their exclusion was 

a needed assumption in the modeling effort due to limited time, resources, and data needed to 

fully investigate and model their operations. This approach is consistent with other local studies 

on runoff contributions by Davis et al (2000) and Olivia Chen Consultants, Inc., (2000). 

Contributing area to these reservoirs was delineated through GIS using the ‘Catchments’ layer 

from the NHDPlus dataset (http://www.horizon-systems.com/nhdplus/ ). The NHDPlus dataset 

is the latest version of NHD, developed as a joint effort between the USEPA and the USGS, 

containing more data and attributes than the NHD in BASINS. (Note that the NHDPlus does not 

contain all of the attributes of the NHD in BASINS. The ‘Level’ attribute used in the previous 

step, for instance, is not present in NHDPlus, so for that previous step the NHD in BASINS was 

used.) 

The NHDPlus ‘Catchments’ also were used to subdivide several of the largest of the Bay Area 

sub-watersheds, including Solano West, Coyote Creek, and Upper Alameda into subwatersheds at a size more consistent with the rest of the sub-watersheds of the study area. A 

representative reach was chosen from within each of these subdivided sub-watersheds. 

It many cases the outlet of a Bay Area sub-watershed is not the Bay itself, but another stream 

that then connects to the Bay. In a subset of those cases, the downstream stream segment 

was not the segment chosen as the representative stream for that downstream sub-watershed, 

and in these cases connecting reaches were added to connect the flows from these upper subwatersheds to the Bay. Connecting reaches were added for the Upper San Lorenzo, Santa 

Clara Valley Central, Upper San Francisquito Creek, and Upper Colma sub-watersheds. 

Once the GIS layers of stream segments and sub-watersheds were developed, the BASINS 4.0 

manual delineation tool was used in conjunction with the National Elevation Dataset (NED) in 

BASINS to calculate stream attributes including length, endpoint elevations, slope, and 

contributing area for each modeled stream segment. The stream length and slope values were 

applied to the corresponding HSPF hydraulic parameters. Also through the BASINS manual 

delineation tool the mean depth and mean width of each stream segment was calculated as a 

function of upstream area. A volume-stage-discharge function (FTABLE) was computed for 

each stream segment using these values through the algorithm in BASINS/WinHSPF. 

2.2 METEOROLOGIC INPUTS 

Required meteorologic inputs for HSPF for the San Francisco Bay sub-watersheds include 

precipitation and evaporation. Precipitation is required as short time interval values, usually on 

an hourly time step, whereas evaporation can be input as daily values which are then distributed 

over daylight hours using standard procedures available within BASINS. For the precipitation 

data, this project benefited from the coincident ongoing effort to update the BASINS national 

database of 500 stations, for the previous version circa 1995, to almost 6000 daily stations and 

3600 hourly stations nationwide. 
Model Setup

 AQUA TERRA Consultants 11

Figure 2.2 shows the 20 stations selected to represent the precipitation inputs to the 22 subwatersheds in the Bay Area. The map also shows the isohyetal pattern of equal rainfall lines 

and Thiessen polygons used to assign and weight the rainfall stations for each sub-watershed. 

The isohyetal coverage for the entire state of California is available online 

(http://frap.cdf.ca.gov/data/browsegraphic/rain.gif), and was originally digitized from a 

1:1,000,000 source map compiled by S. E. Rantz, U.S. Geological survey, 1969, 1972. The map 

is based on data covering the period 1900-1960. A Thiessen analysis is a standard hydrologic 

technique to define the watershed area that will receive the rainfall recorded at the gage; it 

involves constructing polygons around each gage using perpendicular bisecting lines drawn at 

the midpoint of connecting lines between each gage. The Thiessen analysis was used to assign 

the appropriate rain gages to each sub-watershed, and then develop an adjustment factor 

(MFACT in Table 2.1) to adjust the point rainfall for sub-watershed-wide effective rainfall, based 

on the isohyetal pattern. 

Table 2.1 lists the sub-watersheds and the corresponding precipitation stations selected for 

each sub-watershed. These stations were selected from a review of the available hourly and 

daily stations within the Bay area; data for selected daily stations were disaggregated using the 

distribution at neighboring or closest hourly stations with similar daily rainfall totals. These 

procedures produced a complete set of stations with hourly values for all 22 sub-watersheds 

within the Bay Area. As noted above, multipliers, shown as MFACTs inTable 2.2 are used to 

adapt and adjust the gage rainfall and evaporation to the sub-watershed mean rainfall derived 

from the isohyetal coverage shown in Figure 2.2 and the CIMIS zones (discussed below). 

For evaporation, HSPF generally uses measured pan evaporation to derive an estimate of lake 

evaporation, which is considered equal to the potential evapotranspiration (PET) required by the 

model, i.e., PET = (pan evap) X (pan coefficient.) The actual simulated evapotranspiration is 

computed by the program based on the model algorithms that calculate dynamic soil moisture 

conditions, as a function of the rainfall, model ET (evapotranspiration) parameters, and the input 

PET data. 

Pan evaporation data are only available from the Los Alamitos gage in San Jose. In order to 

adapt this evaporation data to other parts of the study watersheds we used climate zones 

defined by California Irrigation Management Information System (CIMIS) 

(http://wwwcimis.water.ca.gov/cimis/data.jsp) as shown in their WUCOLS (Water Use 

Classifications of Landscape Species) manual (CA DWR, 2000). Figure 2.3 shows the 

locations of and spatial relationships among the various CIMIS climate zones that cover the Bay 

Area. Using procedures developed in a modeling study for Alameda County (AQUA TERRA 

Consultants, 2005), the Los Alamitos evaporation data, which is located in Zone 8, was 

adjusted to other sub-watersheds by the ratio of the CIMIS values for the corresponding zones. 

Thus the evaporation for Marin North, located in Zone 5, is equal to the ratio of the Zone 5 to 

Zone 8 CIMIS values times the Los Alamitos data. Table 2.1 lists the multipliers for each subwatershed, shown as the ‘Zone 8 MFACT’ in the last column of the table. Since the CIMIS data 

is also available as a daily time series, it was used to disaggregate the monthly evaporation 

values to daily values, and then to hourly values (using capabilities within BASINS, distributing 

the daily values over ‘daylight’ hours), required by HSPF. 
Model Setup

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Table 2.1 Sub-Watershed Rainfall and Evaporation Zones 

WATERSHED 

RAINFALL 

(inches)* RAIN GAGE

CIMIS 

ZONE

ZONE 8 

MFACT

Marin North 28.9 San Rafael Civic Center 37.12 0.78 5 0.89

Petaluma 26.1 Petaluma Airport 25.07 1.04 5 0.89

Solano West 19.7 Fairfield 22.83 0.86 8 1.00

Upper Corte Madera 49.4 Kentfield 48.03 1.03 4 0.94

Marin South 32.1 Kentfield 48.03 0.67 4,5 0.92

Sonoma 28.3 Sonoma 37.12 0.76 5 0.89

North Sonoma 45.8 Sonoma 37.12 1.23 8 1.00

Napa 24.9 Napa State Hospital 25.80 0.96 8 1.00

North Napa 39.8 Saint Helena 35.00 1.14 8 1.00

East Bay North 18.0 Berkeley 24.69 0.73 1 0.67

East Bay Central 20.3 Upper San Leandro Fltr 24.42 0.83 1,6,8 0.94

East Bay South 15.8 Newark 14.54 1.09 6 1.01

Contra Costa Central 19.4 Martinez Water Plant 19.65 0.99 8 1.00

Contra Costa West 20.7 Berkeley 24.69 0.84 2,8 0.94

Santa Clara Valley West 20.3 Palo Alto 15.51 1.31 8 1.00

Upper Alameda ** 20.4 Livermore, Mount Hamilton 14.82 23.38 1.37 0.87 8, 14 1.12

Coyote Creek 19.2 San Jose 14.70 1.31 8, 14 1.08

Santa Clara Valley Central 27.6 Los Gatos 24.62 1.12 8 1.00

Upper San Francisquito 31.3 Woodside Fire Station 28.77 1.09 8 1.00

San Lorenzo Creek 23.6 Upper San Leandro Fltr 24.42 0.97 8 1.00

Peninsula Central 21.9 SF WSO AP, Redwood City - 50/50 20.18 1.09 6 1.01

Upper Colma 22.5 SF Downtown 21.37 1.05 2 0.79

Gage Annual 

Rainfall (inches) Gage MFACT

* - Derived from isohyetal coverage 

** - Two separate rain gages, each with a separate MFACT, were used to represent the rainfall 

variation in Upper Alameda Creek 

MFACT – Mulitiplication Factor for both rainfall and evaporation 

CIMIS – California Irrigation Management Information System 
Model Setup

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Figure 2.2 Precipitation Gages, Thiessen Polygons, and Isohyetal Pattern 
Model Setup

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Figure 2.3 CIMIS Zones for San Francisco Bay Area 
Model Setup

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2.3 LAND USE, SOILS, SLOPES 

Land use and land cover categories, soils characteristics, and slopes all affect the watershed 

response for both hydrologic and water quality behavior and subsequent runoff. All of these data 

types are required by the HSPF model to characterize the varying behavior of the sub-watersheds 

across the Bay Area. This section describes the data used to feed this information into the model. 

Land use affects the hydrologic response of a watershed by influencing infiltration, surface runoff, 

and water losses from evaporation or transpiration by vegetation. The movement of water through 

the system, and subsequent erosion and chemical transport, are all affected significantly by the 

vegetation, (i.e., crops, pasture, or open), soils, slopes and associated characteristics (e.g. surface 

roughness). In addition the land use also helps to better represent the non-brake pad copper 

source fluxes that are often a function of land use categories and associated activities, e.g. 

urban/developed versus agriculture. Thus, each of the ‘Model Category’ types shown inTable2.2 

are represented as separate land uses in the model. 

The USGS 1992 National Land Cover Dataset (NLCD) land use coverage 

(http://landcover.usgs.gov) was selected by the Brake Pad Partnership Steering Committee as the 

land use data for this effort (Connick and Liao, 2006). The NLCD categories were aggregated into 

the five model-simulated categories – agriculture, developed/landscape, forest, grassland, 

shrub/wooded – and were overlayed with GIS procedures onto the sub-watershed boundaries to 

determine the land use amounts within each modeled watershed. Table 2.2 shows how the NLCD 

categories were aggregated into the model categories (i.e. the individual land use categories 

simulated by the model), along with the percentage of the NLCD land use within the San Francisco 

Bay study area. 

Table 2.2 Land cover types, model categories, and percent of area 

NLCD Code Model Category Detailed-NLCD Percent of total area

61 Agriculture Orchards/Vineyards/Other 2.7

81 Agriculture Pasture/Hay 4.5

82 Agriculture Row Crops 0.7

83 Agriculture Small Grains 0.2

21 Developed/Landscape Low Intensity Residential 17.5

22 Developed/Landscape High Intensity Residential 0.2

23 Developed/Landscape Commercial/Industrial/Transportation 4.0

33 Developed/Landscape Transitional 0.0

85 Developed/Landscape Urban/Recreational Grasses 1.2

41 Forest Deciduous Forest 4.5

42 Forest Evergreen Forest 11.7

43 Forest Mixed Forest 9.2

31 Grassland Bare Rock/Sand/Clay 1.0

32 Grassland Quarries/Strip Mines/Gravel Pits 0.2

71 Grassland Grasslands/Herbaceous 29.9

51 Shrub/Wooded Shrubland 6.7

11 Water Open Water 3.4

91 Wetlands Woody Wetlands 0.1

92 Wetlands Emergent Herbaceous Wetlands 2.3

Due to the importance of impervious surfaces in an urban environment in contributing both 

stormwater volumes and contaminants, the ‘developed/landscape’ category was represented in the 
Model Setup

 AQUA TERRA Consultants 16

model as both a pervious portion and a ‘directly connected impervious area’ (DCIA) portion. This 

division of the ‘developed’ category was performed using the DCIA fractions developed by URS for 

each of the sub-watersheds, as shown in Table 10 of their report (Dufour and Cooke, 2006). Table 

2.3 shows the final model categories and land areas used in the model for each of the 22 subwatersheds. Figure 2.4 shows the distribution of the model land use categories across the Bay 

Area along with the sub-watershed boundaries. 

Table 2.3 Final model land use categories and areas for each of the 22 Bay Area subwatersheds (sq mi) 

Watershed Forest

Grass/W

etland Agriculture

Shrub/W

ooded Developed Impervious

Total (no 

water)

Total (with 

water)

Contra Costa Central 39.10 97.88 2.41 14.45 80.40 15.89 250.12 252.53

Contra Costa West 24.76 35.77 0.69 5.71 27.01 4.32 98.26 101.26

Coyote Creek 125.54 122.92 19.48 31.43 58.15 8.23 365.74 373.35

East Bay Central 26.87 38.25 1.41 10.07 78.88 27.57 183.04 200.50

East Bay North 2.33 2.63 0.00 1.31 15.47 13.02 34.77 35.32

East Bay South 3.49 23.59 1.59 2.64 21.58 8.02 60.91 74.84

Marin North 22.97 22.33 6.01 2.36 14.18 1.66 69.52 71.61

Marin South 10.19 6.49 0.00 3.11 17.77 5.04 42.60 43.68

Napa 34.73 69.59 34.63 12.25 27.00 3.47 181.67 197.84

North Napa 117.05 35.60 43.26 17.31 3.83 0.03 217.08 218.62

North Sonoma 36.81 8.27 8.72 3.10 1.68 0.02 58.60 58.67

Peninsula Central 19.12 20.64 0.02 9.62 53.81 20.72 123.93 134.59

Petaluma 20.17 58.04 54.79 2.26 9.30 0.28 144.84 147.58

Santa Clara Valley Central 56.89 19.82 1.76 14.60 39.96 8.30 141.33 142.61

Santa Clara Valley West 34.43 20.79 0.89 8.52 82.36 37.35 184.34 193.64

Solano West 49.20 180.60 48.00 13.35 30.56 0.34 322.05 346.14

Sonoma 20.75 49.48 28.42 2.50 5.52 0.11 106.78 107.76

Upper Alameda 191.11 308.30 28.88 71.71 36.45 0.82 637.26 641.74

Upper Colma 0.46 1.77 0.05 0.59 5.12 2.92 10.91 10.91

Upper Corte Madera 9.63 2.83 0.00 1.01 4.70 0.12 18.28 18.30

Upper San Francisquito 20.92 5.95 0.45 2.75 7.43 0.13 37.63 37.77

San Lorenzo Creek 13.03 20.20 0.00 2.94 8.08 1.03 45.27 45.28

Total 879.56 1151.73 281.45 233.59 629.24 159.39 3334.95 3454.54

For soils information, SSURGO soils data was obtained from the USDA Soil Data Mart 

(http://soildatamart.nrcs.usda.gov/) for the Bay Area counties, and processed to identify soil 

textural classifications and correlate those classes with the SCS hydrologic soil groups (HSG). 

Thus, sand and sandy loam type soils were assigned to the HSG A, silt and silty loam soils were 

assigned to HSG B, etc. until all soil units were assigned to one of the four HSG. The only area 

of missing data was for Western Santa Clara County whose soil survey was ongoing and would 

not be completed for another few years. Fortunately we were able to obtain a comparable HSG 

map for that region developed as part of the Santa Clara Valley Urban Runoff Pollution 

Prevention Program (SCVURPPP) (P. Randall, EOA Inc. personal communication, February 16, 

2007). This coverage was merged with the processed SSURGO data for the other counties to 

produce the HSG map shown in Figure 2.5. 

The primary HSG for each of the 22 sub-watersheds was determined through GIS processing of 

this data layer and provided the basis for assigning model parameters to each sub-watershed and 

each model land use category. 
Model Setup

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Figure 2.4 Model Land Use Categories across the Bay Area
Model Setup

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Figure 2.5 Hydrologic Soil Groups for Bay Area Counties 

For slope information, a 30-meter digital elevation model (DEM) was obtained for the Bay Area 

counties from the USGS Seamless data web site (http://seamless.usgs.gov/). The data were 

processed to calculate mean slopes by land use within each of the 22 sub-watersheds; these 

data are directly input to the model as a physical attribute of the land surface for each land use 

category. Table 2.4 shows the mean slope, in percent, for each land use category for each of 

the sub-watersheds; the variation in topography and slope across the study area is shown in 

Figure 1.1 in Section 1. 
Model Setup

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Table 2.4 Mean Slope of Each Modeled Land Use Category within the San Francisco 

Bay Sub-Watersheds 

Watershed Agriculture

Developed/L

andscape Forest

Grassland/W

etlands

Shrub/ 

Wooded

Marin South 11.98 19.06 14.23 16.61

Upper Corte Madera 13.15 19.08 17.95 18.60

Marin North 1.50 8.07 18.30 11.99 13.58

Petaluma 6.30 4.28 16.09 9.12 9.30

North Sonoma 8.54 4.78 17.45 12.49 17.85

Sonoma 4.39 2.92 15.91 6.40 12.61

North Napa 4.94 6.31 18.23 14.88 17.06

Napa 6.08 4.77 14.92 9.26 12.86

Solano West 3.16 4.11 15.79 7.04 9.30

East Bay North 7.03 18.83 11.00 14.45

Contra Costa West 9.67 9.23 17.47 14.82 15.34

Contra Costa Central 10.13 6.56 18.12 13.09 16.20

East Bay Central 0.13 4.08 17.98 11.23 11.64

San Lorenzo Creek 9.21 18.72 16.80 16.86

East Bay South 4.65 1.86 16.55 8.62 9.13

Upper Alameda 3.66 3.59 19.04 15.06 17.48

Coyote Creek 2.54 2.84 19.08 15.53 17.40

Santa Clara Valley Central 2.67 3.13 20.24 14.69 18.52

Santa Clara Valley West 8.70 2.28 19.26 9.78 13.00

Upper San Francisquito 6.72 10.81 17.95 11.33 14.75

Peninsula Central 9.25 5.87 16.49 8.22 13.30

Upper Colma 15.60 11.35 16.32 15.97 16.63

Mean Slope (percent) per Land Use Type

2.4 IRRIGATION 

The developed urban and agricultural land use within the study area comprise about 23% of the 

total land area. This is the area that receives irrigation applications. In semi-arid climates, such as 

we have in the Bay Area, supplemental irrigation can and does have a significant impact on the 

hydrologic regime and stormwater runoff, potentially changing ephemeral streams into perennial 

ones. Often the irrigation applications will exceed annual rainfall by 50% to 100% or more. 

The approach to include urban and agricultural irrigation applications was based on prior 

modeling studies both in the Bay Area in Alameda County (AQUA TERRA Consultants, 2006) 

and in Ventura County of Southern California (AQUA TERRA Consultants, 2005). The 

approach assumes that irrigation systems would be used, and amounts applied to make up 

monthly lawn and crop evapotranspiration (ET) demands that exceed available rainfall. ET 

demands are computed based on the landscape coefficient method described in the WUCOLS 

III (Water Use Classifications of Landscape Species) manual (CA DWR, 2000). Daily reference 
Model Setup

 AQUA TERRA Consultants 20

ET is given by month for each climate zone in the state. Figure 2.3 show the seven climate 

zones – 1, 2, 4, 5, 6, 8, 14 -- that occur within the Bay Area sub-watersheds being modeled. 

The daily crop irrigation need is calculated as the difference between lawn ET demand and 

rainfall. For the Bay Area sub-watersheds, the annual supplemental irrigation amounts to 15 to 

30 inches, depending on local ET and rainfall contributions. This irrigation demand is divided 

into three hourly applications for 6-7am, 7-8am, and 8-9am (to represent automated sprinklers 

on a daily schedule), and an irrigation efficiency factor is applied to increase the actual 

application. The WUCOLS manual suggests an efficiency of 0.85 for a well-designed urban 

irrigation system (mostly microjet/drip nozzles), and a value of 0.75 has been used for 

agricultural systems based on literature for Ventura County, The model currently uses 0.80 for 

this factor, which represents a midpoint between the urban and agricultural efficiency values 

since the same irrigation timeseries are applied to both land uses within each sub-watershed, 

i.e. a different tiem series for each sub-watershed but applied to both urban and agricultural 

lands. Additional details on the irrigation calculations and approach can be found in the 

WUCOLS manual and the above cited studies. 

2.5 COPPER SOURCES AND RELEASE (FLUX) ESTIMATES 

This study benefited greatly from the prior and ongoing supporting technical studies sponsored by 

the Brake Pad Partnership. As shown in the Introduction and Figure 1.2, all of these studies were 

specifically designed to provide direct input to the watershed model in order to quantify the copper 

loads from brake pad wear and debris, and all other known sources. The primary studies that 

provided these input fluxes included the assessment of copper released by brake pad and nonbrake pad sources by Rosselot (2006, 2007), and the air deposition estimates from Pun et al (2006) 

and Pun (2007). 

Figure 2.6 provides a schematic of how the various copper flux estimates are represented in the 

HSPF watershed model for the Bay Area sub-watersheds. There is a great deal of uncertainty in 

both the non-brake and brake release estimates, and taking that uncertainty into account when 

determining whether the contribution from brake pads is substantial was necessary. Thus, three 

cases of copper flux scenarios were modeled, one called brakes-high, one called brakes-low, and 

one called median estimate. These three scenarios were selected because results based on them 

adequately represent the range of relative contribution of copper released from brakes, and 

because they take the uncertainty in both brake and non-brake releases into account. One 

scenario is based on the point value presented in the copper release inventories for both brake 

sources and non-brake sources; this scenario is called the median estimate case. A second 

scenario, called the brakes-low case, explores the source term estimates from the perspective that 

the point values in the release inventory overestimate brake contributions relative to non-brake 

sources. The third scenario, called the brakes-high case, explores the source terms from the 

perspective that the point values in the release inventory underestimate brake contributions relative 

to non-brake sources of copper. 

Tables 2.5 through 2.7 show the flux estimates provided by Rosselot for three scenarios, Median 

Estimate, Brakes - High, and Brakes –Low, for both brake pad wear debris roadway releases and 

non-brake pad wear debris releases to various land uses included in the model. Rain-independent 

releases to storm drains and surface waters include copper in domestic water discharged to storm 

drains, copper released from pool, spa, and fountain algaecides, and copper used in algaecides 

used in non-agricultural rights of way, recreation areas, and for public health. Rain-dependent 

releases to storm drains and surface waters include architectural releases of copper and copper in 
Model Setup

 AQUA TERRA Consultants 21

industrial runoff. Releases to agricultural land include copper in algaecides applied to agricultural 

water areas as well as copper in fertilizers and pesticides applied to agricultural land. Releases to 

developed land include copper in pesticides applied to urban land and copper in non-farm fertilizers 

as well as copper from pressure-treated wood used in residential and commercial construction. 

Table 2.8 shows the wet and dry deposition fluxes supplied by Pun (2007) for each of the subwatersheds. Releases of copper from brake pads are responsible for the vast majority of these 

deposition estimates. The values in these tables provided the basis for quantifying the various 

copper contributions shown in Figure 2.6. 

The mapping of the copper sources to the various watershed runoff pathways is derived as follows: 

a. The wet and dry deposition fluxes provided by Pun (Table 2.8) are handled separately in 

HSPF. The wet deposition fluxes were derived from emissions within each sub-watershed 

but without regard to actual rainfall rates and variability across the Bay Area. Since HSPF 

can accommodate concentrations of copper in precipitation as the wet deposition 

contribution, the wet deposition rates were converted to a concentration value for input to 

the model, as shown in Table 2.9. Since the deposition rates were based on a 5-year 

simulation from March 2000 through February 2005, Table 2.9 includes the mean annual 

precipitation for that time period as the basis for calculating the concentration value. The 

overall long-term mean annual precipitation is also shown to demonstrate how that mean 

varies from the 5-year mean. The precipitation concentrations shown in Table 2.9 are 

generally consistent with data from Tsai et al (2001) for land-based monitoring sites (North 

and South Bay) in the range of 0.38 to 0.90 μg/l. The wet deposition contribution is added 

to each land use as shown in Figure 2.6. 

b. Dry deposition rates shown in Table 2.8 are used directly in the model, after being 

converted to model units, and are applied to each land use as a daily accumulation rate, as 

shown in Figure 2.6, designated as ACQOP in the model. 

c. Brake pad wear debris roadway releases are supplied directly to the ‘Impervious’ land 

category; those releases are shown in Tables 2.5 through 2.7 for the three scenarios. 

These were adjusted by Rosselot (Personal communication, May 7, 2007) to account for 

losses due to street sweeping. Copper removed via street sweeping was approximately a 

tenth of the copper released direct to roadways. Street sweepings are securely 

landfilled and the copper they contain is not expected to be entrained in runoff. 

d. Non-brake pad wear debris releases are shown in RED in Figure 2.6. The values for these 

fluxes are provided in Tables 2.5 through 2.7, and include the following: 

i. Releases to ‘Developed’ land 

ii. Releases to ‘Agricultural" land 

iii. Copper releases not dependent on storm events 

iv. Architectural and Industrial ‘rain-dependent’ releases 

v. Sediment associated losses from pervious and impervious land representing 

‘background’ copper sorbed to sediment and solids particulates. 

e. Rain-dependent releases are represented in the model as originating from impervious 

surfaces based on a calculated concentration and the runoff volume from those surfaces for 

each sub-watershed. These represent the ‘architectural and industrial’ releases (noted in d 

above); they are included in the model as a function of the impervious runoff so that the 

timing of their inflows will correspond to storm events. 
Model Setup

 AQUA TERRA Consultants 22

f. Releases NOT dependent on storm events are included as a constant ‘point load’ to the 

stream reach within each sub-watershed, based on the values in Tables 2.5 through 2.7. 

The three scenarios are intended to investigate the possible range of copper contributions from 

brake pads if the sources are varied within plus and minus one standard deviation of the median 

estimate. Thus, the three scenarios are represented as follows: 

1. Median Estimate: This scenario represents the best estimate of all copper contributions, 

both brake pad wear debris and non-brake pad wear debris, given the various uncertainties 

in calculating the components of the copper sources. 

2. Brakes – High: This represents the median estimate for the fluxes plus one standard 

deviation of the estimate for all brake pad wear debris sources, and minus one standard 

deviation for all non-brake pad wear debris sources. The wet and dry deposition fluxes are 

also increased (by 67% as noted in the footnote in Table 2.8). 

3. Brakes – Low: This represents the median estimate for the fluxes minus one standard 

deviation of the estimate for all brake pad wear debris sources, and plus one standard 

deviation for all non-brake pad wear debris sources. The wet and dry deposition fluxes are 

also decreased (by 67% as noted in the footnote in Table 2.8). 
AQUA TERRA Consultants 

brake pad 

wear debris 

Road Way

( ) Street Sweeping Removed

Impervious 

Figure 2.6 Copper Flux Representation in the Watershed Model

Grassland Developed Pervious Agriculture Shrubland

Woodland

RCHRES 

Wet (by precipitation) : Dry by daily accumulation (ACQ

Non-brake 

pad wear 

debris 

Release to 

Developed 

PF PF PF PF P

Non-brake pad 

wear debris 

Release to 

Agriculture 

Non-bra

Non- debris 

Architectural & Industrial Runoff 

(Rain-dependent) 

(calculated from IMPLND SURO 

and concentration) 

(Not Depe

Pseudo “

brake pad 

wear debris: 

Wet/Dry
Model Setup

Table 2.5 Copper Releases for the ‘Median Estimate’ Scenario 

BPP Modeled Watershed

RainIndependen

t Releases 

to Storm 

Drains and 

Surface 

Waters

RainDependent 

Releases to 

Storm 

Drains and 

Surface 

Waters

Releases to 

Agricultural 

Land

Releases to 

Developed 

Land

(kg/yr) (kg/yr) (kg/yr) (kg/yr) (kg/yr)

Castro Valley 226 18 13 0 708

Contra Costa Central 3,253 759 400 96 10,225

Contra Costa West 1,165 270 182 35 3,660

Coyote 4,128 301 467 1,235 11,602

East Bay Central 6,002 498 836 69 17,736

East Bay North 1,676 219 178 0 5,059

East Bay South 1,272 106 246 95 3,751

Marin North 648 41 66 97 1,668

Marin South 1,007 64 86 0 2,591

Napa 1,377 255 193 2,988 4,830

North Napa 171 27 19 7,312 666

North Sonoma 64 5 3 1,242 206

Peninsula Central 3,697 240 485 0 9,464

Petaluma 449 35 72 848 1,439

Santa Clara Valley Central 2,513 196 168 85 7,062

Santa Clara Valley West 5,201 363 633 72 14,525

Solano West 1,157 246 251 2,321 3,657

Sonoma 210 17 22 3,111 680

Upper Alameda 1,508 195 239 728 4,514

Upper Colma 682 44 23 1 1,745

Upper Corte Madera 204 13 6 0 526

Upper San Francisquito 90 6 14 45 231

Upper San Lorenzo 238 18 17 0 702

Watershed Total (Parts of 8 Counties) 36,939 3,936 4,620 20,380 107,245

Releases From Non-Brake Sources

Release to 

Roadway 

From Brake 

Wear 

(adjusted 

for street 

sweeping)

 AQUA TERRA Consultants 24
Model Setup

Table 2.6 Copper Releases for the ‘Brakes – High’ Scenario 

BPP Modeled Watershed

RainIndependen

t Releases 

to Storm 

Drains and 

Surface 

Waters

RainDependent 

Releases to 

Storm 

Drains and 

Surface 

Waters

Releases to 

Agricultural 

Land

Releases to 

Developed 

Land

(kg/yr) (kg/yr) (kg/yr) (kg/yr) (kg/yr)

Castro Valley 312 7 11 0 413

Contra Costa Central 4,933 289 333 45 6,241

Contra Costa West 1,766 103 151 17 2,233

Coyote 6,262 119 388 531 6,786

East Bay Central 9,111 220 695 25 10,354

East Bay North 2,540 107 148 0 3,002

East Bay South 1,927 47 204 34 2,187

Marin North 982 16 55 42 989

Marin South 1,526 24 72 0 1,536

Napa 2,087 121 161 1,637 3,101

North Napa 260 8 16 4,006 449

North Sonoma 96 2 3 670 122

Peninsula Central 5,607 91 404 0 5,605

Petaluma 681 13 60 456 853

Santa Clara Valley Central 3,809 86 140 36 4,130

Santa Clara Valley West 7,892 136 527 31 8,503

Solano West 1,753 79 209 1,166 2,197

Sonoma 319 6 18 1,677 403

Upper Alameda 2,286 111 199 274 2,662

Upper Colma 1,033 17 19 0 1,033

Upper Corte Madera 310 5 5 0 312

Upper San Francisquito 136 2 12 22 137

Upper San Lorenzo 361 7 14 0 410

Watershed Total (Parts of 8 Counties) 55,989 1,617 3,842 10,669 63,658

Release to 

Roadway 

From Brake 

Wear(adjust

ed for street 

sweeping)

Releases fron Non-Brake Sources

 AQUA TERRA Consultants 25
Model Setup

Table 2.7 Copper Releases for the ‘Brakes – Low’ Scenario 

BPP Modeled Watershed

RainIndependen

t Releases 

to Storm 

Drains and 

Surface 

Waters

RainDependent 

Releases to 

Storm 

Drains and 

Surface 

Waters

Releases to 

Agricultural 

Land

Releases to 

Developed 

Land

(kg/yr) (kg/yr) (kg/yr) (kg/yr) (kg/yr)

Castro Valley 139 29 15 0 1,004

Contra Costa Central 1,574 1,229 467 146 14,209

Contra Costa West 564 438 212 54 5,087

Coyote 1,995 482 545 1,940 16,417

East Bay Central 2,893 777 977 113 25,117

East Bay North 812 330 208 0 7,116

East Bay South 616 165 287 156 5,314

Marin North 314 67 77 151 2,348

Marin South 488 104 101 0 3,647

Napa 667 390 226 4,338 6,558

North Napa 83 45 22 10,618 884

North Sonoma 31 8 4 1,815 289

Peninsula Central 1,788 389 567 1 13,322

Petaluma 218 57 84 1,239 2,024

Santa Clara Valley Central 1,216 307 197 133 9,993

Santa Clara Valley West 2,510 590 739 112 20,547

Solano West 560 412 293 3,475 5,116

Sonoma 102 27 25 4,546 957

Upper Alameda 730 279 279 1,183 6,366

Upper Colma 330 72 27 1 2,456

Upper Corte Madera 99 21 8 0 740

Upper San Francisquito 44 9 17 69 325

Upper San Lorenzo 115 29 20 0 995

Watershed Total (Parts of 8 Counties) 17,889 6,255 5,397 30,090 150,833

Release to 

Roadway 

From Brake 

Wear 

(adjusted 

for street 

sweeping)

Releases From Non-Brake Sources

 AQUA TERRA Consultants 26
Model Setup

Table 2.8 Estimated dry and wet air deposition fluxes of copper. (Pun, 2007) 

Sub-Watershed 

Dry 

deposition 

(μg/m2

/day)

Uncertainty 

in Dry 

deposition

(μg/m2

/day)

Wet 

deposition 

(μg/m2

/day) 

Uncertainty 

in Wet 

deposition

(μg/m2

/day)

Upper Alameda 1.84 1.24 1.02 0.68

Santa Clara Valley Central 7.45 4.99 0.95 0.64

Castro Valley 17.30 11.59 1.15 0.77

East Bay North 18.71 12.54 1.15 0.77

Upper Colma 24.41 16.35 1.27 0.85

Marin South 9.51 6.37 1.00 0.67

Coyote 4.99 3.35 0.93 0.62

East Bay Central 12.02 8.06 0.98 0.65

East Bay South 7.22 4.84 0.96 0.64

Solano West 2.13 1.43 0.94 0.63

Napa 3.46 2.32 0.92 0.62

North Napa 1.15 0.77 0.91 0.61

North Sonoma 1.23 0.82 0.87 0.58

Marin North 4.23 2.83 0.92 0.61

Contra Costa Central 5.67 3.80 0.93 0.62

Petaluma 1.98 1.33 0.90 0.60

Santa Clara Valley West 10.89 7.30 0.97 0.65

Upper San Lorenzo 3.08 2.06 0.90 0.60

Contra Costa West 5.16 3.45 0.93 0.62

Peninsula Central 11.14 7.47 0.99 0.67

Sonoma 1.57 1.05 0.89 0.59

Upper San Francisquito 1.72 1.15 0.88 0.59

Upper Corte Madera 5.03 3.37 0.93 0.62

City of San Francisco 24.42 16.36 1.23 0.82

San Francisco Bay 0.81 0.54 0.85 0.57

* uncertainty estimates due to uncertainty in the brake pad wear debris source term copper 

content (±67%), which was determined to be a dominant source of uncertainty in the air 

deposition modeling results (Pun et al., 2006) 

 AQUA TERRA Consultants 27
Model Setup

Table 2.9 Calculation of Wet Deposition Copper Concentrations in Precipitation

Sub-Watershed Area Area 

 Mean 

Annual 

Precip 

(MAP) 

 Mean 

Annual 

Precip 

Mar '00-

Feb '05 

Midpoint 

Wet 

Deposition

Midpoint 

Wet 

Deposition 

in MAP

m2 (sq mi) (in) (in) ug/m2/day (ug/L)

Castro Valley 14,236,852 5.50 20.00 15.70 1.15 1.053

Contra Costa Central 654,086,768 252.54 19.40 18.46 0.93 0.724

Contra Costa West 262,249,739 101.25 20.70 24.27 0.93 0.551

Coyote 967,003,363 373.36 19.20 16.07 0.93 0.832

East Bay Central 519,305,379 200.50 20.30 22.70 0.98 0.620

East Bay North 91,479,037 35.32 18.00 17.27 1.15 0.957

East Bay South 193,835,163 74.84 15.80 12.83 0.96 1.075

Marin North 185,472,023 71.61 28.90 29.21 0.92 0.453

Marin South 113,136,276 43.68 32.10 36.74 1.00 0.391

Napa 512,428,261 197.85 24.90 25.88 0.92 0.511

North Napa 566,230,920 218.62 39.80 34.34 0.91 0.381

North Sonoma 151,942,501 58.67 45.80 27.08 0.87 0.462

Peninsula Central 348,556,751 134.58 21.90 20.16 0.99 0.706

Petaluma 382,253,589 147.59 26.10 20.59 0.90 0.628

Santa Clara Valley Centr 369,367,983 142.61 27.60 22.04 0.95 0.619

Santa Clara Valley West 501,538,211 193.64 20.30 14.71 0.97 0.948

Solano West 896,499,671 346.14 19.70 22.94 0.94 0.589

Sonoma 279,100,257 107.76 28.30 24.07 0.89 0.531

Upper Alameda 1,662,135,211 641.75 20.40 15.24 1.02 0.962

Upper Colma 28,260,219 10.91 22.50 18.44 1.27 0.990

Upper Corte Madera 47,394,139 18.30 49.40 37.85 0.93 0.353

Upper San Francisquito 97,804,648 37.76 31.30 25.58 0.88 0.494

Upper San Lorenzo 103,003,367 39.77 23.60 24.24 0.90 0.534

 San Lorenzo Ck (incl CV 117,240,219 45.27 23.16 23.20 0.93 0.576

Total 8,947,320,329 3454.56

 AQUA TERRA Consultants 28
Model Application and Operation

SECTION 3.0 

MODEL APPLICATION, OPERATIONS AND RESULTS 

3.1 MODEL APPLICATION APPROACH 

Typical calibration and validation procedures for HSPF involve a ‘weight-of-evidence’ approach 

with multiple graphical and statistical comparisons of observed and simulated quantities for flow, 

sediment and water quality constituents; these procedures have been well established over the 

past 25 years as described in the HSPF Application Guide (Donigian et al., 1984) and recently 

summarized by Donigian (2002, 2003). For the 22 San Francisco Bay sub-watersheds, this 

type of effort would have required extensive observed data within each sub-watershed, which 

were not available, along with extensive calibration and validation efforts. 

Due to the project constraints noted above, an expedited approach was needed based on a 

sound foundation of prior modeling efforts within the Bay Area, and the ongoing data 

development efforts of the EPA BASINS system. The following studies, in addition to those 

noted in Section 2, provided the technical basis for the HSPF model development and initial 

parameterization for the study watersheds: 

a. HSPF parameter development for the Bay Area Hydrology Model (BAHM) design tool 

through calibration studies in Castro Valley and Upper Alameda Creeks (AQUA TERRA 

Consultants, 2006), and in two Santa Clara County creeks (Clear Creek Solutions (in 

preparation); parameters listed in Clear Creek Solutions (2007). Note that this Upper 

Alameda Creek sub-watershed is a subset of the Brake Pad Partnership modeled 

watershed with the same name. 

b. Use of HSPF for multipurpose design of detention facilities in Calabazas Creek of San 

Jose for the Santa Clara Valley Water District (Donigian, et al., 1997), which included 

copper runoff modeling. 

c. Application of HSPF to Castro Valley Creek by U.S. EPA for copper runoff modeling 

(Carleton and Cocca, 2004). 

These studies provided the knowledge base to estimate hydrology model parameters as 

extensions from the HSPF models of these calibrated watersheds to all 22 sub-watersheds, and 

the modeling efforts in Calabazas Creek and Castro Valley Creek provided comparable 

information for the sediment and copper modeling parameterization. This latter effort was 

supplemented by the Brake Pad Partnership copper source studies as discussed in Section 2. 

In particular, the BAHM effort (Item d above), in conjunction with the Alameda watershed 

modeling (Item a) provided HSPF hydrology parameter values for combinations of land 

cover/vegetation, soils groups (HSG), and slopes. Hydrology parameter values were assigned 

to the 22 sub-watersheds by land use category as the initial model parameterization based on 

the soils and slopes characterization of each sub-watershed, as shown in Section 2. With 

sediment and copper parameter values assigned from the Calabazas and Castro Valley studies, 

HSPF model runs were performed for a 25 year period covering Water Years 1981 through 

2005 (i.e. October 1980 through September 2005). Prior to performing the long-term model 

runs, consistency and quality assurance (QA) checks were performed to ensure valid and 

reasonable model results. The following section describes the various consistency and QA 

checks with available observed data, followed by the model results for each of the three loading 

scenarios – Median Estimate, Brakes – High, and Brakes – Low. 

 AQUA TERRA Consultants 29
Model Application and Operation

3.2 MODEL CONSISTENCY CHECKS AND QUALITY ASSURANCE 

Due to the lack of data and resources to perform a comprehensive calibration and validation effort 

on each of the 22 sub-watersheds, an alternative approach was selected that focused on the 

performance of consistency and QA checks on as many sites/stations as possible with readily 

available data. This effort involved hydrology simulation checks with observations at selected 

USGS gage stations that were located at a number of the sub-watershed outlets. For sediment and 

copper checks, simulations were performed with the Castro Valley model setup prepared for the 

ACCWP (AQUA TERRA Consultants, 2006), supplemented with sediment and copper modeling 

capabilities. Each of these consistency and QA checks are discussed below. 

3.2.1 Hydrology Consistency Checks 

Figure 3.1 shows the USGS station locations where many of the hydrology consistency checks 

were performed. The specific stations included in these checks are listed in Table 3.1. 

 Table 3.1 USGS Stations used in Hydrology Consistency Checks 

USGS Station No. Name

11164500 San Francisquito Creek at Stanford University CA

11169000 Guadalupe River at San Jose CA

11162720 Colma Creek at South San Francisco CA

11181040 San Lorenzo Creek at San Lorenzo CA

11458000 Napa River near Napa CA

11458500 Sonoma Creek at Agua Caliente CA

11460000 Corte Madera Creek at Ross CA

11179000 Alameda Creek near Niles, CA

The consistency checks for hydrology involved model simulations for the time period of the 

available flow data, and then comparisons of the simulated and observed flow duration curves and 

annual volumes. For the first seven sites listed in Table 3.1, a limited number of model calibration 

runs were performed (i.e. about 4 to 8 runs per site) to refine the agreement for these two 

comparisons. 

No calibration runs were performed on the last site, Alameda Creek, as it drained the Upper 

Alameda Creek sub-watershed that included a number of reservoirs whose drainage areas were 

assumed not to contribute to downstream flow and loads. In addition, other gages in the Upper 

Alameda Creek sub-watershed, shown in Figure 3.1 – Arroyo Valle, San Antonio, Calaveras – were 

also excluded for the same reason. However, upon completion of the limited calibrations on the 

seven sites, the Alameda Creek site was also run and comparisons made without calibration, i.e. 

the model parameters were assigned based only on the prior work described above and watershed 

characteristics. The Alameda Creek simulation was run to both assess the 

 AQUA TERRA Consultants 30
Model Application and Operation

 AQUA TERRA Consultants 31

 

Figure 3.1 USGS Station Locations used for Hydrology Consistency Checks 

reliability of assigning parameters without calibration, and assessing the impact of ignoring 

contributions from the reservoirs in the Upper Alameda Creek sub-watershed. Figures 3.2 and 3.3 

show the flow duration curves for the seven USGS stations at which limited calibration was 

performed, along with the Alameda Creek station for which no calibration was done. 
Model A

Figure 3.2 Flow Duration Curve Model Results for Selected Bay Area Sub-Watersheds

AQUA TERRA Consultants 
Model A

AQUA TERRA Consultants 

Figure 3.3 Flow Duration Curve Model Results for Additional Bay Area Sub-Watersheds 
Model Application and Operation

Table 3.2 shows the comparison of the simulated and observed mean annual runoff volumes for 

the seven USGS flow stations where limited calibration was performed, along with the Alameda 

Creek comparison where NO calibration was performed. 

 Table 3.2 Model-Data Comparisons for Selected USGS Flow Stations 

 Mean Mean

 Mean Simulated Observed

Rainfall Flow Flow Residual Percent Period of

 (inches) (inches) (inches) (inches) Error Record

 

Napa River 35.0 13.2 12.7 0.5 4.2% 86-04

Sonoma Creek 27.5 17.2 17.1 0.1 0.7% 02-04

Guadalupe River 21.5 9.2 8.7 0.5 6.0% 86-02

Corte Madera Creek 43.2 15.0 14.3 0.7 5.0% 86-93

San Francisquito Creek 27.7 8.8 8.2 0.5 6.4% 86-04

Colma Creek 18.4 10.3 9.6 0.7 6.9% 86-94

San Lorenzo Creek 25.4 7.1 6.8 0.3 4.5% 88-04

Alameda Creek 21.0 5.4 5.9 -0.5 -9.0% 81-05

From both the flow duration curves in Figures 3.2 and 3.3 and the runoff volume comparisons in 

Table 3.2, the model provides a very good simulation of flow. Analysis of these model results as 

consistency and QA checks indicates the following: 

a. The flow duration curves show good to very good agreement consistently across all the 

gages. The gage with the largest deviations is for Sonoma Creek and that had a limited 

record of only 3 years for calibration purposes. 

b. Many of the flow duration curves showed good agreement even with the initial parameters 

assigned based on the regional experience in Alameda and Santa Clara Counties. The 

largest deviations tend to be at low flows where individual sub-watershed and hydrogeologic 

characteristics control groundwater levels and the resulting rate of baseflow recession. This 

tends to be specific to each sub-watershed. Since the focus of the consistency checks was 

on the mid-high flow range important for stormwater runoff, differences in the low end of the 

flow duration curve will not have significant impact s on sediment and copper loads to the 

Bay. 

c. The volume comparisons in Table 3.2 show percent errors of less then 10% across all the 

station sites; this corresponds to a ‘Very Good’ calibration based on tolerances presented 

by Donigian (2000). Even though there is no single statistic that can be used to establish 

model validity, the combination of the flow duration and volume results indicate confidence 

that the model is performing well for hydrology. 

d. The fact that Alameda Creek shows agreement similar to the other sub-watersheds further 

supports the use of the BAHM parameters developed for Alameda County and Santa Clara 

County. Furthermore, the high level of agreement shown in both the flow duration curve 

(Figure 3.3) and volume comparison supports the assumption that the land areas behind 

the dams in Upper Alameda sub-watershed are not significantly contributing to the flow. 

 AQUA TERRA Consultants 34
Model Application and Operation

3.2.2 Sediment and Copper Consistency Checks 

Sediment and copper concentration consistency checks were performed by Mr. Jim Carleton at 

EPA OST in addition to QA checks by AQUA TERRA staff. AQUA TERRA implemented sediment 

and copper simulation capabilities into the detailed Castro Valley Creek (CVC) model developed for 

the Alameda Countywide Clean Water Program (AQUA TERRA Consultants, 2006), and provided 

that to Mr. Carleton for calibration to available data on Castro Valley Creek (ACFCD, 2005) AQUA 

TERRA performed limited sediment ‘calibrations’ to ensure predicted concentrations, loadings and 

state variables were within reasonable ranges. All the copper fluxes discussed and listed in Section 

2 were incorporated into the CVC model, and EPA focused on adjusting the washoff parameters to 

approximate the limited available instream copper concentrations at the sub-watershed outlet. 

In HSPF, the accumulation and washoff functions applied in this study are as follows: 

Accumulation: SQO = ACQOP + SQOS * (1.0 – REMQOP) 

 where ACQOP = daily accumulation rate, kg/ha/day 

 SQO = surface storage of constituent, kg/ha 

 SQOS = SQO at start of the day 

 REMQOP = unit removal rate, per day 

Washoff: SOQO = SQO * (1.0 – e(-SURO*WSFAC)) 

 where SOQO = washoff of constituent from the surface, kg/ha/timestep 

 SURO = overland flow, cm/timestep 

 WSFAC = rate of overland flow for 90% washoff in 1 hr, cm/hr 

 exp = exponential function 

The input parameters specified by the model user are ACQOP, SQOLIM, and WSQOP, and these 

are translated into the model parameters described above as follows: 

 REMQOP = ACQOP/SQOLIM 

 WSFAC = 2.3/WSQOP 

Thus, the model internally calculates the values for REMQOP and WSFAC from the three input 

parameters. In the CVC calibration, the accumulation rates (ACQOP) were defined by the copper 

release and deposition rates and the washoff factor, WSQOP, is relatively standard at about 3.8 

cm/hr (1.5 in/hr). This leaves the accumulation limit, SQOLIM, as the primary calibration parameter 

used to adjust model results to the observed data for copper concentrations in the Castro Valley 

sub-watershed. 

Figure 3.4 shows the model results for Castro Valley Creek: the top figure is simulated and 

observed sediment concentrations, the middle figure shows simulated and observed copper 

concentrations, and the bottom figure shows a scatterplot of the copper results. These results are 

for the Median Estimate scenario, but similar results were also generated for the low and high 

scenarios. Data in these figures were available from ACCWP in various reports on both recent 

(ACFCD, 2005) and historic data (Woodward-Clyde Consultants (1996). 

The sediment results show the model generally reproduces the overall range and dynamic behavior 

of the limited observed sediment data and the overall response of the watershed. The simulated 

 AQUA TERRA Consultants 35
Model Application and Operation

copper concentrations cover much of the observed data points, and come close to the high 

concentrations but fail to achieve the highest few values approaching 100 μg/l total copper. The 

scatterplot shows overall good agreement with a slope of 0.99 and an r2

 of 0.66, when the 6 highest 

points are treated as outliers. Figure 3.5 identifies the six observed datapoints as outliers as they 

occurred at low mean daily flow conditions, and may have been due to incidental releases (e.g. 

swimming pools) that would not have been captured by the mean annual values of the copper 

release inventory. 

Additional sediment and copper concentration consistency checks were performed with the longterm model runs spanning WYs 1981 through 2005. Figures 3.6 and 3.7 show sediment and 

copper concentration comparisons for the Guadalupe River (in Santa Clara Valley Central) and the 

San Lorenzo Creek, respectively, while Figure 3.8 shows just the copper simulations results for 

Alameda Creek. The Guadalupe and San Lorenzo data include both historic USGS data and more 

recent data collected by McKee et al (2005). 

All of these results show a notable degree of consistency with the observations. Although there are 

significant differences in individual sub-watersheds and storm events, considering the results in 

Figures 3.6 through 3.8 were not generated by further calibration, the agreement with observations 

for both sediment and copper are compelling. These checks indicate that the model provides a 

reasonable representation of the limited data points and a sound technical basis for performing the 

alternate scenario runs. 

 AQUA TERRA Consultants 36
Model Application and Operation

 AQUA TERRA Consultants

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0.0 20.0 40.0 60.0 80.0 100.0 120.0

Obs. TCu (ug/L)

simulated TCu (ug/L)

Figure 3.4 TSS (mg/l) and Total Copper Concentrations (μg/l) for Castro Valley Creek 

 37
Model Application and Operation

Brakes Low Scenario

0

20

40

60

80

100

120

0 50 100 150 200 250 300

mean daily flow (cfs)

TCu (ug/L)

simulated

Obs. data

the "outliers"

Figure 3.5 Identification of ‘Outliers’ as Data Points Observed at Low Flows

 AQUA TERRA Consultants 38
Model Application and Operation

Figure 3.6 TSS (mg/l) and Total Copper Concentrations (μg/l) for Guadalupe River in 

 San Jose 

 AQUA TERRA Consultants 39
Model Application and Operation

Figure 3.7 TSS (mg/l) and Total Copper Concentrations (μg/l) for San Lorenzo Creek 

 AQUA TERRA Consultants 40
Model Application and Operation

 Figure 3.8 Total Copper Concentrations (μg/l) for Alameda Creek 

3.3 MODEL SCENARIO RUNS 

Model runs were performed for each sub-watershed for the time period of WY 1981 through 

WY2005, i.e. October 1980 through September 2005. Model results were then processed for flow, 

sediment and copper loads; annual and mean annual loads were tabulated; and daily flows and 

concentrations (both sediment and copper, total and dissolved) were reviewed as a quality 

assurance confirmation. In addition, selected sub-watersheds with limited observed data were 

plotted, as discussed above, and compared as an additional reasonability assessment. Dr. 

Carleton again assisted with this final QA check. 

As discussed in Section 2, the three scenarios are intended to investigate the possible range of 

relative copper contributions from brake pads if the sources are varied within plus and minus one 

standard deviation of the median estimate; these standard deviations were provided by AER (Pun, 

2007) for the air deposition sources, and Kirsten Rosselot (2007) for the remaining sources as an 

extension of their earlier work. 

Thus, the three scenarios are represented as follows: 

1. Median Estimate: This scenario represents the best estimate of all copper contributions, 

both brake pad wear debris and non-brake pad wear debris, given the various uncertainties 

in calculating the components of the copper sources. 

2. Brakes – High: This represents the median estimate of the fluxes plus one standard 

deviation of the estimate for all brake pad wear debris sources, and minus one standard 

deviation for all non-brake pad wear debris sources. The wet and dry deposition fluxes are 

 AQUA TERRA Consultants 41
Model Application and Operation

also increased (by 67% as noted in the footnote in Table 2.8). This provides an estimate of 

the upper bound of the relative contribution from brake pads. 

3. Brakes – Low: This represents the median estimate of the fluxes minus one standard 

deviation of the estimate for all brake pad wear debris sources, and plus one standard 

deviation for all non-brake pad wear debris sources. The wet and dry deposition fluxes are 

also decreased (by 67% as noted in the footnote in Table 2.8). This provides an estimate of 

the lower bound of the relative contribution from brake pads. 

Furthermore, each of the three scenarios were re-run with all brake pad contributions 

eliminated, so that the brake pad contributions could be determined by finding the difference in 

loadings between the ‘with’ and ‘without’ brake pad contribution scenarios. This involved 

eliminating all wet and dry air deposition and all roadway releases; all remaining sources were 

retained in these non-brake pad runs. 

Table 3.3 provides a summary of the flow, sediment and copper fluxes, in kilograms, from each of 

the sub-watersheds for each of the six scenarios. The table shows both the sub-watershed loads 

and the load to the bay. For most sub-watersheds these two are the same, but for North Napa and 

North Sonoma these loads are tributary to the Napa and Sonoma sub-watersheds from which the 

total load to the bay is presented. Table 3.3 also includes an estimate of sediment-associated 

copper in runoff to the Bay derived from copper at background levels of 25 ppm on soils throughout 

the Bay Area; this concentration value was cited by Carleton and Cocca (2004). Given the inherent 

uncertainty and variability in this assumed 25 ppm concentration throughout the Bay Area, the 

calculation shows that about 40% of the total load to the Bay may be attributable to background soil 

levels of copper. 

In Table 3.3, note that the total copper loads in runoff for the brakes-high, brakes-low, and median 

estimate scenarios are about the same, on the order of 56,000 kg. The loads for each scenario 

being about the same implies that after modeling to find copper in runoff, the high end of brake pad 

contributions is about equally offset by the low end of non-brake pad contributions, and vice versa. 

The scenarios were chosen with the intent that they explore the range of the relative contribution of 

copper from brake pads in runoff to the bay. The release estimates don't actually compensate for 

each other in the different scenarios, and it is coincidental that the three copper loadings of Table 

3.3 are so similar for the three different cases. There are sub-watersheds, such as Napa and 

Sonoma, where the loads in runoff across the three release scenarios are not similar, with a range 

of 15-20% difference from the brakes-high and brakes-low scenarios. 

The total annual load of 56,000 kg compares well with the SFEI preliminary estimate of 66,000 kg 

(within a range of 36,000 to 110,000 kg/yr) using a relatively simple runoff-coefficient model (Davis 

et al, 2000). The sediment load of 931,000 tonnes/yr also compares well with another SFEI report 

that estimates the annual TSS load from local tributaries should be in the range of 561,000 to 

1,000,000 tonnes/yr (McKee et al., 2003). Both of these reports lend further credibility to the model 

results since they are entirely consistent and of similar magnitudes. 

Figure 3.9 shows an approximate mass balance of the copper sources and runoff components 

contributing the annual load to the Bay. The brake pad wear debris contributions are shown in 

orange, the non-brake pad wear debris releases are shown in blue, and the watershed model 

calculated loadings are shown in green separately by land use sources. The mass balance shows 

the following: 

 AQUA TERRA Consultants 42
Model Application and Operation

 Copper Load (1000 kg/yr) 

Brake pad wear debris sources 55.5 

Non-brake pad wear debris sources 138.5 

Total releases to the watershed 184.0 

Total runoff loads to the streams 60.0 

Total runoff loads reaching the Bay 57.0 

The variation in copper runoff loads by land use is also shown in Figure 3.9, with impervious and 

developed land uses contributing 30,000 kg/y, which represents about 50% of the total load of 

copper in runoff to the Bay. These land use loads include both the runoff loads from the 

anthropogenic accumulation and the background sediment-associated loads. Thus, both forest and 

grassland show relatively high runoff loads compared to the accumulation releases due to the 

sediment-associated loads from background copper levels in the soil 

The model results were also analyzed to estimate the fraction of the surface accumulation that runs 

off from each land use for the San Francisco Bay watershed as a whole. The following values 

represent the percentage of the anthropogenic accumulation and deposition releases that reach 

the streams: 

Impervious 35% 

Grassland 14% 

Forest 12% 

Shrubland/Woodland 12% 

Developed Pervious 8% 

Although the ‘Developed Pervious’ land use category has the lowest runoff percentage, it 

contributes the largest land use load due to the high rate of copper accumulation from both brake 

pad wear debris and non-brake pad wear debris sources. Among the sub-watersheds, these 

values vary because of differences in slope and other factors. 

Table 3.4 shows an estimated distribution of the contribution of the total copper runoff load to the 

Bay from three sources -- from brake pad wear debris, anthropogenic non-brake pad sources of 

copper, and sediment/background soil levels. This breakdown is presented for each sub-watershed 

and for the entire San Francisco Bay watershed. For the three scenarios of Brakes - High, Median 

Estimate, and Brakes - Low, the brake pad contributions vary from 10% to 35% of the total copper 

loads to the Bay. The paper presented by Carleton and Cocca (2004) cites a report by WoodwardClyde (1994) indicating estimates of copper from brake pad wear to be in the range of 44% to 53% 

for the lower South San Francisco Bay; the values for the Peninsula and Santa Clara Valley subwatersheds are in the range of 42% to 67%, while the values for the East Bay sub-watersheds are 

48% to 53%. Thus the model results are again consistent with available literature on the brake pad 

contributions. These comparisons provide a strong ‘weight-of-evidence’ confirmation that the 

model results are reasonable and credible, and thus provide acceptable boundary condition loads 

to the Bay model. 

Table 3.5 summarizes the relative contributions of brake pads on anthropogenic sources of 

copper in runoff. For the watershed as a whole, the brake pad contribution to copper in runoff 

 AQUA TERRA Consultants 43
Model Application and Operation

 AQUA TERRA Consultants 44

varies from 17% to 60% of anthropogenic sources, with a midpoint of 39%. Table 3.5 shows a 

significant variation in these percentages among the Brake Pad Partnership modeled 

watersheds, with the median estimate brake pad contribution reaching 50% or more for some of 

the more urbanized sub-watersheds, such as Peninsula Central, Upper Colma, and Santa Clara 

Valley West. 

Among the Brake Pad Partnership modeled watersheds, the total contribution from brake pad 

wear debris towards total anthropogenic loads of copper to the Bay for the median estimate 

case varies from 15% (for the Sonoma sub-watershed) to 57% (for the Upper Colma subwatershed). For the rural sub-watersheds, the brake pad contribution is much lower than for the 

heavily urbanized sub-watersheds. There are six sub-watersheds whose total copper load to 

the bay is larger than 4,000 kg/y (for the median estimate case). They are Contra Costa 

Central, East Bay Central, Napa, Petaluma, Santa Clara Valley West, and Sonoma. These six 

sub-watersheds contribute about 60% of the total copper load to the Bay. It's interesting that 

some of these sub-watersheds have their largest contribution from sediment (Napa, Petaluma, 

Sonoma), some have their largest contribution from non-brake pad anthropogenic sources 

(Contra Costa Central, East Bay Central), and one has its largest contribution from brake pad 

sources (Santa Clara Valley West). 

Total anthropogenic releases to the watershed are much higher in the brakes-low case, where 

brake pad releases are reduced by a standard uncertainty and non-brake releases are 

increased by a standard uncertainty. It's only after these releases go through the modeling 

process that they offset each other, but it's also somewhat expected because the non-brake 

releases are to pervious surfaces where they are much less likely to be entrained in runoff. 

Figures 3.10 through 3.12 show sample model results for the period from water year 1995 

through water year 2005, a portion of the long-term simulations, for four selected sub-watershed 

and USGS gage stations – Guadalupe River (Santa Clara Valley Central), Alameda Creek, San 

Lorenzo Creek, and Napa River (outlet to Bay). These long term results are consistent with and 

similar to the earlier plots. 
Table 3.3 Summary of Watershed Model Results for Bay Area Sub-Watersheds (water years 1981 – 2005) 

Flow Sediment All Cases High Brakes High Brakes - No BP Mid Brakes Mid Brakes - No BP 

BPP Modeled Watershed

Watershed 

Load

Load to 

Bay

Copper on 

Sediment 

to Bay

Watershed 

Load Load to Bay

Watershed 

Load Load to Bay

Watershed 

Load

Load to 

Bay

Watershed 

Load

Load to 

Bay

W

(cms)

Contra Costa Central 3.8 56,452 56,452 1,411 4,421 4,421 2,715 2,715 4,669 4,669 3,564 3,564 

Contra Costa West 1.6 28,498 28,498 712 1,834 1,834 1,229 1,229 1,941 1,941 1,549 1,549 

Coyote Creek 2.2 28,135 28,135 703 3,259 3,259 1,520 1,520 3,074 3,074 1,937 1,937 

East Bay Central 3.1 41,582 41,582 1,040 5,953 5,953 2,841 2,841 5,849 5,849 3,823 3,823 

East Bay North 1.0 9,365 9,365 234 1,533 1,533 802 802 1,781 1,781 1,144 1,144 

East Bay South 0.7 5,190 5,190 130 882 882 413 413 849 849 543 543 

Marin North 1.3 17,008 17,008 425 927 927 633 633 910 910 719 719 

Marin South 1.2 18,438 18,438 461 1,390 1,390 766 766 1,345 1,345 940 940 

Napa 9.5 51,591 218,268 5,457 2,976 8,506 2,071 7,252 3,099 9,174 2,513 8,372 

North Napa 6.6 166,676 0 0 5,531 0 5,181 0 6,075 0 5,859 0 

North Sonoma 2.8 59,252 0 0 1,972 0 1,821 0 2,136 0 2,042 0 

Peninsula Central 2.2 9,580 9,580 239 3,013 3,013 1,005 1,005 2,682 2,682 1,372 1,372 

Petaluma 4.9 146,682 146,682 3,667 4,677 4,677 4,148 4,148 4,742 4,742 4,410 4,410 

Santa Clara Valley Central 2.1 39,453 39,453 986 2,730 2,730 1,576 1,576 2,645 2,645 1,899 1,899 

Santa Clara Valley West 3.3 36,101 36,101 903 4,776 4,776 2,018 2,018 4,392 4,392 2,603 2,603 

Solano West 3.0 21,317 21,317 533 1,627 1,627 1,069 1,069 1,694 1,694 1,333 1,333 

Sonoma 5.5 66,592 125,844 3,146 2,377 4,350 2,145 3,966 2,660 4,796 2,508 4,550 

Upper Alameda 4.2 76,287 76,287 1,907 3,511 3,511 2,661 2,661 3,480 3,480 2,931 2,931 

Upper Colma 0.4 2,291 2,291 57 585 585 181 181 521 521 259 259 

Upper Corte Madera 0.8 14,240 14,240 356 599 599 455 455 594 594 502 502 

Upper San Francisquito 0.9 19,584 19,584 490 652 652 570 570 646 646 594 594 

San Lorenzo Creek 0.9 16,757 16,757 419 683 683 541 541 678 678 586 586 

Totals 62 931,070 931,070 23,277 55,907 55,907 36,360 36,360 56,465 56,465 43,632 43,632 

(tonnes)

Mean Annual Copper Loads (kg)

A

Q

UA

TE

R

RA

Consultants 
Table 3.4 Brake Pad, Anthropogenic Non-brake pad, and Sediment (Background) Contribution of Copper 

Watershed (kg/y) 

Contribution 

from 

Sediment

Contribution 

from 

Sediment

Contra Costa Central 1,411 1,706 39% 1,304 29% 32% 1,105 24% 2,153 46% 30% 5Contra Costa West 712 605 33% 516 28% 39% 391 20% 837 43% 37% 1Coyote Creek 703 1,740 53% 816 25% 22% 1,137 37% 1,234 40% 23% 5East Bay Central 1,040 3,113 52% 1,801 30% 17% 2,026 35% 2,784 48% 18% 9East Bay North 234 730 48% 568 37% 15% 637 36% 910 51% 13% 2East Bay South 130 469 53% 284 32% 15% 306 36% 413 49% 15% 1Marin North 425 295 32% 207 22% 46% 191 21% 294 32% 47% 

Marin South 461 624 45% 305 22% 33% 405 30% 479 36% 34% 1Napa 5,457 1,255 15% 1,795 21% 64% 802 9% 2,916 32% 59% 3North Napa 0 0 0% 0 0% 0% 0 0% 0 0% 0% 

North Sonoma 0 0 0% 0 0% 0% 0 0% 0 0% 0% 

Peninsula Central 239 2,008 67% 765 25% 8% 1,310 49% 1,133 42% 9% 6Petaluma 3,667 529 11% 481 10% 78% 332 7% 743 16% 77% 1Santa Clara Valley Central 986 1,154 42% 590 22% 36% 746 28% 913 35% 37% 3Santa Clara Valley West 903 2,758 58% 1,115 23% 19% 1,789 41% 1,701 39% 21% 8Solano West 533 558 34% 536 33% 33% 361 21% 800 47% 31% 1Sonoma 3,146 384 9% 819 19% 72% 246 5% 1,404 29% 66% Upper Alameda 1,907 849 24% 754 21% 54% 549 16% 1,024 29% 55% 2Upper Colma 57 404 69% 124 21% 10% 263 50% 201 39% 11% 1Upper Corte Madera 356 144 24% 99 16% 59% 93 16% 146 24% 60% 

Upper San Francisquito 490 82 13% 81 12% 75% 52 8% 105 16% 76% 

San Lorenzo Creek 419 142 21% 122 18% 61% 91 13% 167 25% 62% 

Totals 23,277 19,547 35% 13,083 23% 42% 12,833 23% 20,355 36% 41% 5,8

Sediment, 

All Cases BP Contribution BP Contribution BP Co

Anthropogenic 

Non-BP 

Contribution 

 High Brakes Mid Brakes 

Anthropogenic 

Non-BP 

BPP Modeled Watershed Contribution 

Mean Annual Copper Load to Bay (kg)

A

Q

UA

TE

R

RA

Consultants 
UA TERRA Consultants 

BPWD: Wet/Dry

BPWD

Roadway Impervious Grassland Developed Pervious Agriculture Shrubland/ Woodland Forest

(60)

STREAMS

( ) Street Sweeping Removed

Wet (in precipitation); Dry (constant flux)

Non-BPWD

Release to 

Developed

Non-BPWD

Release to 

Agriculture

Non-BPWD Copper Releases

Arch. & Industrial Runoff

(Rain-Dependent)

(calculated from impervious 

runoff and concentration)

(Not Dependent on Rain)

Pseudo “point-source”

Bay Model

57

37

4.6 3.9

14

16 8.8 7.6 0.9 3.8

1.7

110

5.1

20

0.9 5.3 1.2 4.3

BPWD: Wet/Dry

BPWD

Roadway Impervious Grassland Developed Pervious Agriculture Shrubland/ Woodland Forest

(60)

STREAMS

( ) Street Sweeping Removed

Wet (in precipitation); Dry (constant flux)

Non-BPWD

Release to 

Developed

Non-BPWD

Release to 

Agriculture

Non-BPWD Copper Releases

Arch. & Industrial Runoff

(Rain-Dependent)

(calculated from impervious 

runoff and concentration)

(Not Dependent on Rain)

Pseudo “point-source”

Bay Model

57

37

4.6 3.9

14

16 8.8 7.6 0.9 3.8

1.7

110

5.1

20

0.9 5.3 1.2 4.3

Figure 3.9 Mean Annual Copper Fluxes in the Watershed Model (1000 kg/yr)

AQ
Model Application and Operation

Table 3.5 Brake Pad Contributions of Total Anthropogenic Copper Loads to San 

Francisco Bay

Brakes - High Median 

Estimate Brakes - Low

Contra Costa Central 57% 34% 14%

Contra Costa West 54% 32% 13%

Coyote Creek 68% 48% 24%

East Bay Central 63% 42% 20%

East Bay North 56% 41% 19%

East Bay South 62% 43% 21%

Marin North 59% 39% 19%

Marin South 67% 46% 22%

Napa 41% 22% 8%

North Napa 0% 0% 0%

North Sonoma 0% 0% 0%

Peninsula Central 72% 54% 29%

Petaluma 52% 31% 11%

Santa Clara Valley Central 66% 45% 22%

Santa Clara Valley West 71% 51% 27%

Solano West 51% 31% 13%

Sonoma 32% 15% 4%

Upper Alameda 53% 35% 16%

Upper Colma 77% 57% 30%

Upper Corte Madera 59% 39% 18%

Upper San Francisquito 50% 33% 13%

San Lorenzo Creek 54% 35% 16%

Totals 60% 39% 17%

BPP Modeled Watershed

Brake Pad Contribution to Total 

Anthropogenic Copper Loads to the Bay

 AQUA TERRA Consultants 48
Model Application and Operation

Figure 3.10 Model Results for the Guadalupe River

 AQUA TERRA Consultants 49
Model Application and Operation

Figure 3.11 Model Results for Alameda Creek

 AQUA TERRA Consultants 50
Model Application and Operation

Figure 3.12 Model Results for San Lorenzo Creek

 AQUA TERRA Consultants 51
Model Application and Operation

Figure 3.12 Model Results for the Napa River 

 AQUA TERRA Consultants 52
Recommendations

SECTION 4.0 

RECOMMENDATIONS FOR MODEL IMPROVEMENTS 

During the course of the modeling effort and the subsequent peer review, a number of areas 

and topics were identified where additional investigation is recommended to improve and 

support and/or refine the current model estimates of copper loads to San Francisco Bay, and 

the relative contribution from brake pad wear debris. These recommendations are as follows: 

1. Calibration and validation: The major critique of the Peer Review panel was the lack 

of a complete calibration and validation that is required in most model applications. 

Additional data was identified by selected peer reviewers that would be appropriate for 

further detailed calibration and validation efforts, such as data for the Guadalupe River, 

and such efforts are highly recommended. 

2. Further analyses of current model results: Watershed models generate a wealth of 

information that can supply useful insights into land use impacts and mass balances on 

a sub-watershed basis. Further processing of the detailed model outputs developed in 

this effort can provide additional information on copper mass balances and runoff 

processes for each of the Brake Par Partnership sub-watersheds. 

3. Deposition and scour in streams: The current modeling exercise assumes little to no 

deposition and/or scour within the rivers and streams that connect the Brake Pad 

Partnership modeled watersheds to the Bay. Resource limitations precluded an in-depth 

assessment for each of the connecting waterbodies in this effort. While the assumption 

is sound for estimating loads to the Bay over long time horizons, its impact on the timing 

of the delivery of the loads merits further investigation. Significant deposition could 

reduce or significantly delay the delivery of sediment and copper loads to the Bay, and 

channel scour could also substantially change the timing of delivered loads.

4. Reservoir representation: The current modeling effort included the drainage areas of 

each of 10 major reservoirs within the Study Area, but based on past precedents in 

similar studies, assumed that there would be no release or contributions from these 

reservoirs for flow, sediment or copper to the Bay. This assumption needs to be further 

investigated to determine its impact and validity for each of the identified reservoirs and 

any others of potential concern.

5. Sensitivity and uncertainty analyses: Current practice in watershed and water 

systems model applications has evolved to include both sensitivity and uncertainty 

analyses as part of the modeling exercise. In this effort, model scenarios were 

performed to cover a potential range of copper release estimates, but no formal or 

additional sensitivity nor uncertainty analyses were possible due to both time and budget 

constraints. These efforts should be considered in the future to provide a better 

understanding of the predicted copper loads to the Bay, the relative contributions from 

brake pad wear debris, and an improved appreciation of the potential range of 

uncertainty in the Bay Model predictions. Such analyses should, at a minimum, 

consider uncertainty in sediment erosion and loads to the Bay, native/background 

copper concentrations in surface soils and their variability across the study region, and 

copper-sediment interactions during transport and delivery to the Bay. 

 AQUA TERRA Consultants 53
Recommendations

6. Impact of buildup/washoff formulation: The daily accumulation (buildup) and 

exponential washoff by surface runoff (overland flow) is a standard algorithm in use in 

most urban and many non-urban watershed models. However, the accumulation 

function is designed to approach an asymptotic limiting value for the surface storage of 

the water quality constituent, in this case copper. Any additional accumulation is 

assumed ‘lost’ from the system when, in reality, this additional accumulation might be 

blown off impervious surfaces and added to, and possibly bound to surface soils and 

vegetation in adjacent pervious areas. Alternative model formulations can be 

implemented to account for this transfer of material and should be investigated as to its 

potential impact on the total copper loads to the Bay.

7. Increased spatial discretization: As noted in Section 2, the sub-watershed boundaries 

and the general scale of the model application were recommended by EPA and 

approved by the Brake Pad Partnership as part of the watershed model work plan. 

Although the boundaries mostly follow hydrologic divides and are entirely appropriate for 

the scale of this assessment, the spatial representation is relatively coarse with subwatershed sizes ranging from 10 sq. mi. to over 600 sq mi. in area. In addition, in many 

cases the connections to the Bay do not follow their ‘true’ hydrologic pathways. For 

these reasons, and others related to release estimates, the model results should be 

viewed as approximations for each sub-watershed and not a detailed, accurate 

representation of each of the streams that connect these sub-watersheds to the Bay. 

Further spatial discretization would be recommended for each of the Brake Pad 

Partnership modeled watersheds, into multiple sub-watersheds and multiple channel 

reaches, to produce an improved representation of local conditions within each subwatershed. The current model setup provides an ideal starting point for such an effort 

that could provide useful tools for watershed management purposes and objectives 

within the Bay Area.

7. Lag time in the watershed: From a water quality management perspective, it would be 

useful to understand how quickly the watershed and receiving waters would respond to a 

reduction in anthropogenic copper source loadings, and what the period of time may be 

following the reduction and/or elimination of copper sources for previous anthropogenic 

copper enrichment in the watershed to decrease to pre-industrialized levels. Such 

information could inform source control efforts and provide guidance for the design of 

water quality monitoring programs. Additional modeling studies could help assess the 

response time to a variety of scenarios for potential changes in releases of copper to the 

watershed. 

 AQUA TERRA Consultants 54
References

SECTION 5.0 

REFERENCES 

ACFCD, 2005. Results of the 2003-2004 Castro Valley Creek Water Quality Monitoring Project. 

Alameda County Clean Water Program, a Consortium of Local Agencies. Prepared by the 

Alameda County Flood Control District. Prepared for the Brake Pad Partnership. April 7, 

2005.(http://www.suscon.org/brakepad/pdfs/Final_Report_on_Castro_Valley_Creek_Water_Qu

ality_Monitoring_04-07-05.pdf). 

AQUA TERRA Consultants, 2006. Hydrologic Modeling of the Castro Valley Creek and 

Alameda Creek Watersheds with the U. S. EPA Hydrologic Simulation Program – 

FORTRAN. Final Report. Prepared for the Alameda Countywide Clean Water Program, 

Hayward, CA. January 20, 2006. 

AQUA TERRA Consultants, 2005. Hydrologic Modeling of the Calleguas Creek Watershed with 

the U.S. EPA Hydrologic Simulation Program – FORTRAN. Final Report. Prepared for the 

Ventura County Watershed Protection District, Ventura CA. March 10, 2005. 

Bicknell, B.R., J.C. Imhoff, J.L. Kittle Jr., A.S. Donigian, Jr. and R.C. Johanson. 1997. 

Hydrological Simulation Program - FORTRAN. User's Manual for Release 11. EPA/600/R97/080. U.S. EPA Environmental Research Laboratory, Athens, GA. 763 p. 

Bicknell, B.R., J.C. Imhoff, J.L. Kittle Jr., T.H. Jobes, and A.S. Donigian, Jr. 2005. Hydrological 

Simulation Program - FORTRAN. User's Manual for Release 12.2. U.S. EPA Ecosystem 

Research Division, Athens, GA. & U. S. Geological Survey, Office of Surface Water, Reston, 

VA. 

California Department of Water Resources 2000. “A Guide to Estimating Irrigation Water 

Needs for Landscape Plantings in California; The Landscape Coefficient Method and 

WUCOLS III”. Sacramento, CA. August 2000. 

Carleton, J.N. 2004. Watershed Modeling for the Environmental Fate and Transport of Copper 

from Vehicle Brake Pad Wear Debris. Submitted to Sustainable Conservation, San 

Francisco, CA. November 19, 2004. 

Carleton, J.N. and P.A. Cocca. 2004. Using HSPF to Model Pollutant Runoff in an Urban 

Watershed. Presented at GIS and Water Resources III, AWRA Spring Specialty 

Conference. Nashville, TN. May 17-19, 2004. CD-ROM 

Clear Creek Solutions, Inc. 2007. Bay Area Hydrology Manual – Draft User Manual. Prepared 

by Clear Creek Solutions,Inc. Prepared for Alameda Countywide Clean Water Program, 

San Mateo Countywide Stormwater Pollution Prevention Program and Santa Clara Valley 

Urban Runoff Pollution Prevention Program. June 2007. 

Connick, S. and C. Liao. 2006. Memo to the Brake Pad Partnership Steering Committee & 

Stakeholders. Re: The use of land cover and lnad use data as data inputs and its impact on 

Brake Pad Partnership technical studies. February 1, 2006. 

 AQUA TERRA Consultants 55
References

Davis, J. A., L. J. McKee, J. E. Leathebarrow, and T.H. Daum. 2000. Contaminant Loads From 

Stormwater to Coastal Waters in the San Francisco Bay Region. San Francisco Estuary 

Institute, Oakland, CA. September 2000. 77 pp. 

Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell and J.L. Kittle, Jr. 1984. Application Guide for the 

Hydrological Simulation Program - FORTRAN EPA 600/3-84-066, Environmental Research 

Laboratory, U.S. EPA, Athens, GA. 30613 

Donigian, Jr., A.S., 2000. HSPF Training Workshop Handbook and CD. Lecture #19. 

Calibration and Verification Issues, Slide #L19-22. EPA Headquarters, Washington 

Information Center, 10-14 January, 2000. Presented and prepared for U.S. EPA, Office of 

Water, Office of Science and Technology, Washington, D.C. 

Donigian, A.S. Jr. 2002. Watershed Model Calibration and Validation: The HSPF Experience. 

WEF-National TMDL Science and Policy 2002, November 13-16, 2002. Phoenix AZ, WEF2002 Specialty Conference Proceedings on CD-ROM. 

Donigian, A.S. Jr. and J.T. Love. 2003. Sediment Calibration Procedures and Guidelines for 

Watershed Modeling. WEF TMDL 2003, November 16-19, 2003. Chicago, Illinois. WEF 

Specialty Conference Proceedings on CD-ROM. 

Donigian, A.S., Jr.. R. V. Chinnaswamy, and T. H. Jobes. 1997. Conceptual Design of 

Multipurpose Detention Facilities for Flood Protection and Nonpoint Source Pollution 

Control. Prepared by AQUA TERRA Consultants, Mountain View CA. Prepared for Santa 

Clara Valley Water District, San Jose, CA. 158 p. 

Dufour, A. and T. Cooke. 2006. Memo to Sarah Connick, Sustainable Conservation from Alexis 

Dufour and Terry Cooke, Subject: Bake Par Partnership (Brake Pad Partnership), Land Use 

Land Cover Analysis for Watershed Modeling. April 4, 2006. 

McKee, L., J. Leatherbarrow, and J. Oram. 2005. Concentrations and Loads of Mercury, PCBs, 

and OC Pesticides in theLower Guidalupe River, San Jose, California: Water Years 2003 

and 2004. San Francisco Estuary Regional Watershed Program. SFEI Contribution 409. 

San Francisco Estuary Institute, Oakland, CA. July 2005. 

McKee, L., J. Leatherbarrow, S. Pearce, and J. Davis. 2003. A Review of Urban Runoff Process 

in the Bay Area: Existing Knowledge, Conceptual Models, and Monitoring 

Recommendations. A Report prepared for the RMP Sources, Pathways and Loading 

Workgroup. San Francisco Estuary Regional Monitoring Program for Trace Substances. 

SFEI Contribution 66. San Francisco Estuary Institute, Oakland, CA. September 2003. 

Olivia Chen Consultants, Inc., 2000. San Francisco International Airport EIR Hydraulic Studies. 

Technical Memorandum – Inflow Calculations for Hydrodynamic Model. Prepared for URS 

Corporation. December 2000. 

Pun, B.K.; K. Lohman, C. Seigneur, 2006. Air Deposition Modeling of Copper from Brake Pad 

Wear Debris in Castro Valley Creek Watershed, final report, July 2006. 

Pun, B.K. 2007. Memo to Brake Pad Partnership from Betty Pun, RE: Air Deposition of copper 

from Brake pad wear debris. March 6 2007. 

 AQUA TERRA Consultants 56
References

Rosselot, , K.S., 2006a. Copper Released from Brake Lining Wear in the San Francisco Bay 

Area, final report, Brake Pad Partnership, January 2006. 

Rosselot, , K.S., 2006b. Copper Released from Non-Brake Sources in the San Francisco Bay 

Area, final report, Brake Pad Partnership, January 2006. 

Tsai, P. R. Hoenicke, E. Hansen, and K. Lee. 2001. San Francisco Bay Atmospheric Deposition 

Pilot Study, Part 2: Trace Metals. San Francisco Estuary Regional Monoitoring Program for 

Trace Substances. SFEI Contribution 73. San Francisco Estuary Institute, Richmond, CA. 

September 2001. 

URS, 2006. San Francisco Bay Modeling for the Environmental Fate and Transport of Copper 

From Vehicle Brake Pad Wear Debris. Work Plan. Submitted to Sustainable Conservation, 

San Francisco, CA. Prepared by URS, Oakland, CA. August 15, 2006 

U.S. EPA. 2001. Better Assessment Science Integrating Point and Nonpoint Sources (BASINS), 

Version 3.0 User’s Manual: U. S. Environmental Protection Agency, EPA-823-C-01-001, 

Office of Water, Washington DC. 

Woodward-Clyde Consultants, 1994. Contribution of Heavy metals to Storm Water from 

Automotive Disc Barke Pad Wear. Prepared for for Santa Clara Valley Nonpoint Source 

Control Program, October 12, 1994. 

Woodward-Clyde Consultants, 1996. San Francisco Bay Area Stormwater Runoff Monitoring 

Data Analysis 1988-1995. Prepared for Bay Area Stormwater Management Agencies 

Association. October 15, 1996. 

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