a.j. · larry a. roesner, ph.d, p.e., dee colorado state university geoffbrosseau bay area...

,Stormwater Environmental Indicators Demonstration Project

Draft Report

Dan Cloak and Lucy A.J. Buchan

September, 2000

Water Environment Research FoundationProject 96-IRM-3

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Aclmowledgements,

The Stormwater Environmental Indicators Demonstration Project was conducted by the Sartta ClaraValley Urban Runoff Pollution Prevention Program with funding from the Water Environment ResearchFoundation.

Technical information in this report was compiled from 16 Interim Technical Memoranda produced bythe project team:

Interim Memorandum Authors Tide and AffiliationTechnical Memorandum #1: Preliminary Review and Dan Cloak Supervising Engineer, EOA, Inc.Prolmlffi AssessmentWater Quality Pollutant Constituent Monitoring and Terry Cooke Senior Scientist, URSExceedance Frequencies ofWater Quality Standards Phil Mineart Senior Proiect Engineer, URSToxicity Testin~ and Non-point Source Loadin~ Terrv Cooke Senior Scientist, URSIncreased Flooding Frequency Phil Mineart Senior Project Engineer, URS

TranI! Ha Staff Engineer, URSIndustrial/Commercial Pollution Prevention, BMP Carolyn cwu. Senior Engineer, EOA, Inc.Implementation and Maintenance and Industrial SiteCompliance Monitorinl!Public Involvement and Monitorin~ Mava Hayden Associate Scientist, EOA, Inc.Number of Illicit Connections and illegal Discharges Lucy A.J. Buchan Senior Scientist, EOA, Inc.Identified/CorrectedPermittinR and Compliance Luev A.I. Buchan Senior Scientist, EOA, Inc.Growth and Development Lucy A.J. Buchan Senior Scientist, EOA, Inc.

Paul Randall Associate Scientist, EOA, Inc.Public Attitude Surveys and User Perception Sheila Tucker Principal Tucker Environmental Consultin~Sediment Characteristics and Contamination Marty Stevenson Kinnetics Laboratories, Inc.

Kathleen Dorsey Kinnetics Laboratories, Inc.Stream Widening and Downcutting Paul Randall Associate Scientist, EOA, Inc.

Luev A.I. Buchan Senior Scientist, EOA, Inc.Physical Habitat Monitorinl! Maya Hayden Associate Scientist, EOA, Inc.Stream Temperature Monitorin~ Maya HaYden Associate Scientist, EOA, Inc.Fish Assemblage Analyses Francesca Demgen Wedand Biologist, DRS

Kathleen Dorsey Kinnetics Laboratories, Inc.Macro-Invertebrate Assemblage Francesca Demgen Wedand Biologist, URS

Kathleen Dorsey Kinnetics Laboratories, Inc.

The interim technical memoranda include additional detail, documentation, and data regardingimplementation of the stormwater environmental indicators which were too voluminous and vaned to beincluded in the project technical report. The memoranda are available on request from:

Santa Clara Valley Urban Runoff Pollution Prevention ProgramEOA,Inc.699 Town and Country VillageSunnyvale, CA 94086

www.scvurppp.org

A Quality Assurance Project Plan (QAPP) was prepared in two parts (part 1: Walsh Avenue Catchment,and Part 2: Coyote Creek Watershed) under the direction of Marty Stevenson. of K.i.nnetics Laboratories. TheGIS-generated maps and graphics were produced by Paul Randall and Lucy Buchan. Maya Hayden providededitorial assistance and coordination on the project report.

II

Water Environment Research Foundation Project Subcommittee:

Pat Haddon, Research Program Director William SwietlikWater Environment Research Foundation USEPA Office of Water

Larry A. Roesner, Ph.D, P.E., DEEColorado State University

Geoff BrosseauBay Area Stormwater Management AgenciesAssociation

Ben UrbonasUrban Drainage & Flood Control District(Denver, Colorado)

Project Review Committees:Project Rtview CommitteeJ ill Bicknell, SCVURPPPGail Boyd, URS/Greiner ConsultantsPhil Bobel, City of Palo AltoDebra Caldon, City ofSan JoseLorrie Gervin, City ofSunnyvaleRainer Hoenicke, San Francisco Estuary InstituteTrish Mulvey, Clean South BayTom Mumley, RWQCBRobert Pitt, University of Alabama - Birmingham

Patti Grace-JarrettLouisville and Jefferson County MunicipalSewerage District (Kentucky)

Jerry TerhuneLouisville and Jefferson County MunicipalSewerage District (Kentucky)

Guidance Document Rtview and Validation Com.Christine Anderson, City of EugeneDave Brent, City of SacramentoLori Faha, Unified Sewerage Agency,

Hillsboro, OregonDonald Freitas, Contra Costa CountyRobert Hale, Alameda County Clean Water

Program, State Water Quality Task ForceDoug Harrison, Fresno Metropolitan Flood

Control DistrictTom Lipton, City of Portland

Many of the reviewers attended project meetings in June, 1998 and February, 2000 where the utility ofthe indicators, and many of the recommended refinements, were discussed at length.

III

IV

Contents

AclaJowledgements ii

Absttact vii

F· ...19ures .•..•...••.••.••.1 II •••••••••••••••••••••••••••••• II •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• II II' ••••••• II ••••••••• IIVIII

Tables ix

Acronyms x

1. Introduction 11.1 Measuring Stormwater Program "Effectiveness" 11.2 Background on the Santa Clara Valley Urban Runoff Pollution Prevention Program _3

1.2.1 SCVURPPP Goals and Objectives 61.2.2 Comparison to Goals and Objectives of Other Stormwater Programs 61.2.3 Performance Standards 121.2.4 Santa Clara Basin Watershed Management Initiative 121.2.5 Continuous Improvement 13

2. Project Objectives : 15

3. Methods 173.1 Study Areas 17

3.1.1 Coyote Creek Watershed 173.1.2 Walsh Avenue Catchment 25

ffistory of Program Involvement in the Catchment 25Background on the City of Santa Clara's Industrial/Commercial Stormwater Inspections 27

3.2 Project Approach in Coyote Creek 283.2.1 Physical and Hydrological Indicators 30

Stream Widening and Downcutting 30Physical Habitat Monitoring 31Increased Flood Frequency 33Stream Temperature Monitoring 34

3.2.2 Water Quality Indicators 38Water Quality Pollutant Constituent Monitoring 38Exceedance Frequencies of Water Quality Standards 40Sediment Characteristics and Contamination 41

3.2.3 Biological Indicators ~ 42Fish Assemblages 42Macroinvertebrate Assemblages 44

3.2.4 Programmatic Indicators 46Number of Illicit Connections Identified/Corrected : 46Permitting and Compliance 46Growth and Development 49

3.3 Project Approach in the Walsh Avenue Catchment 513.3.1 Programmatic Indicators 51

Industrial/Commercial Pollution Prevention 51BMPs Installed, Inspected and Maintained 51Industrial Site Compliance Monitoring 51

3.3.2 Water Quality Indicators 52

Toxicity Testing 52Non-point Source Loadings 53

3.4 Application of Indicators in the Program Area 533.4.1 Social Indicators 53

Public Attitude Surveys 53User Perception 53Public Involvement and Monitoring 54

3.5 Project Data Overview 55

4. Results and Discussion 574.1 Results in the Coyote Creek Watershed 57

4.1.1 Physical and Hydrological Indicators 57Stream Widening and Downcutting 57Physical Habitat 61Increased Flood Frequency 64

4.1.2 Water Quality Indicators 72Water Quality Constituent Monitoring 72Exceedance Frequencies of Water Quality Standards 74Sediment Characteristics and Contamination 76Fish Assemblages 79Macroinvertebrate Assemblages 82

4.1.3 Programmatic Indicators 87Growth and Development 87Pennitting and Compliance 88Illicit Connections Identified/Corrected 92

4.2 Results in the Walsh Avenue Catchment 1004.2.1 Programmatic Indicators 100

Industrial/Commercial Pollution Prevention 100Number of BMPs Installed, Inspected, and Maintained 101Industrial Site Compliance Monitoring 101

4.2.2 Water-Quality Indicators 102Toxicity Testing (Indicator #2) 102Non-Point Source Loading (Indicator #3) 102

4.3 Results in the Program Area 1074.3.1 Social Indicators 107

Public Involvement and Monitoring 107Public Attitude, User Perception 108

S. Application of Results to Watershed Management and Program Implementation in theSanta Clara Basin 111

5.1 Application to Watershed Management 1115.2 Application to the Santa Clara Valley Urban Runoff Pollution Prevention Program _112

6. Utility Of Individual Indicators And Recommended Refinements 1156.1 Water Quality Pollutant Constituent Monitoring 1156.2 Toxicity Testing 1156.3 Nonpoint Source Loadings 1266.4 Exceedance Frequencies of Water Quality Standards 1266.5 Sediment Characteristics and Contamination 1276.6 [Indicator not Tested] 1286.7 Stream Widening and Downcutting 128

v

VI

6.8 Physical Habitat Monitoring 1306.9 [Indicator Not Tested] 1306.10 Increased Flood Frequency 1306.11 Stream Temperature Monitoring 1316.12 Fish Assemblage 1326.13 Macro-Invertebrate Assemblages 1336.14 [Indicator Not Tested] 1356.15 [Indicator Not Tested] 1356.16 [Indicator Not Tested] 1356.17 Public Attitude Surveys 1356.18 Industrial/Commercial Pollution Prevention 1356.19 Public Involvement and Monitoring 1366.20 User Perception 1366.21 Number of Illicit Connections Identified/Corrected 1376.22 Number ofBMPs installed, inspected, and maintained 1376.23 Permitting and Compliance 1376.24 Growth and Development 139

BMPs and Reductions in Imperviousness 140Effects on Hydrology and Ecosystems 141Summary of Suggested Refinements 141

6.25 [Indicator Not Tested] 1436.26 Industrial Site Compliance Monitoring 143

7. Usefulness OfThe Stormwater Indicator Methodology 1457.1 Watershed-related indicators 145

7.1.1 Application to I...evel 1. 1467.1.2 Application to Level II. 148

7.2 Progra:m-related Indicators 1487.2.1 Application to Level I 1487.2.2 Application to Level II 1487.2.3 Tools for Targeting Outreach 150

8. Conclusion 1St8.1 Watershed-related Indicators 1518.2 Program-related indicators 1548.3 Linking Watershed Management and Stormwater Pollution Prevention 154

9. References 155

Abstract

[fo appear in final draft]

Keywords: Stonnwater, urban, indicators, geomorphology, imperviousness, fish, macroinvertebrates, B:MPs.

o

VII

Figures

PillUre Tide Paee

1-1 SCVURPPP area, Program Co-permittee jurisdictions, and Santa Clara Basin Watershed Boundary. 41-2 Continuous Improvement diagram. 143-1 Claytor and Brown (1996) stormwater indicator methodology two levels of analysis. 18-19

3-2 SEIDP studv areas - Coyote Creek watershed and Walsh Avenue Catchment - and regional hydrologic units. 20

3-3 Geolol!V. hvdrolol!V. and land use features of Covote Creek Watershed, Santa Clara County. 233-4 Industrial facilities within the Walsh Avenue catchment illustrated bv NOI filer status. 263-5 Samplinll; Sites. 293-6 Location of rain and flow ll;all;CS. 343-7 Variability of concentrations measured. 374-1 Stream classification and hvdrological and ll;Cologicai features of Covote Creek. 584-2 Lonlritudinal profile hvdrololrical controls channel type and land use of Covote Creek. 594-3 Percent of Leve11I habitat types bv lenlrth. • 624-4 Percent of reach with available instream shelter. 634-5 Reach substrate averall;eS bv urbanization zone. 634-6 Mean percent riparian cover per reach. 644-7 Growth in population and urbanization in the Covote Creek Watershed. 654-8 Annual peak flow in Covote Creek at Edenvale Gaj!;e 1. 664-9 Floodinj!; events for the Coyote Creek Watershed (1911-1973) 67

4-10 Floodinll; events for the Coyote Creek Watershed (1978-1998) 684-11 Urban Growth in Penitencia Creek Watershed 694-12 Annual Peak Flows on Penitencia Creek at the Penitencia Gal!C and dates of £Iood events. 694-13 Annual peak £lows> 500 cfs per unit area for urbanized & nonurbanized portions of the Covote Creek Watershed. 704-14 Dailv stream temperatures recorded at 5 stations a10nll Covote Creek. 714-15 Time Trend Of Total Suspended Solids, Total Copper and Total Zinc in Coyote Creek. 724-16 Predicted versus measured TSS. 744-17 Power Analvsis Results For Total Copper Chanlles Usinll ANOVA Analysis. 744-18 Power Analvsis Results For Total Copper ChanllCs Using ANCOVA Analvsis with Hvdrolol!V Factors. 754-19 Sediment Particle Size Distributions at Each Site. . 764-20 Seasonal and spatial comparison of trace metal concentrations in Coyote Creek along a gradient from downstream 77

urban sites to upstream reference sites.4-21 Comparison of sediment trace metal concentration measured in the present study (WERF) with baseline 78

concentrations reported bv Pitt and Bozeman (PB).4-22 Percent Native Fish Species in Coyote Creek 1980 and 1999. 794-23 1999 Coyote Creek native and non-native fish species. 804-24 Native and non-native fish species from 10 studies. 814-25 Monthlv stream flow three vears prior to 1982 and 1999 samplinll. 834-26 Percent cumulative imperviousness in Covote Creek subwatersheds versus fisheries metrics. 844-27 Mean dissolved 0XVll;en concentration at the five water quality monitoMll stations. 854-28 Total number of macroinvertebrate taxa Covote Creek, Mavand lune 1999, plotted with percent clay/silt. 864-29 Percent of EPT, chironornid olillochaete and other taxa Coyote Creek 1999. 864-30 AverallC number of low-tolerance macroinvertebrate taxa. 874-31 Estimated percent imperviousness in Coyote Creek subwatersheds. 894-32 Industrial and construction sites filed under the California NPDES permit in the Coyote Creek watershed by standard 90

industrial classification sector.4-33 Industrial and construction sites filed (1998) under the California NPDES permit in the Covote Creek watershed 934-34 Pollutants monitored by NOI industrial facilities in the Coyote Creek watershed, and pollutants likely discharged by 94

construction NOI sites.4-35 Industrial facilities within the Walsh Avenue catchment illustrated by NOI filer status. 954-36 Recent (1995-1998) illegal dumping incidents reported in the Coyote Creek watershed. 964-37 Enlarged area of Coyote Creek Watershed illustrating recent illella1 dumpinll incidents in the City of San Jose. 974-38 Time trend in total suspended solids, lead, and nickel in Walsh Avenue catchment. 1034-39 Time trend in zinc and copper in Walsh Avenue catchment. 1044-40 Time trends in particulate lead copper and zinc. 1056-1 Effect of urbanization of flood peaks 1317-1 Relationship of indicators to actions 147

viii

Tables

Table Tide Palle

I-I Santa Clara Valley Urban Runoff Pollution Prevention Program Co-Permittees. 51-2 Comparison of stormwater pro~ Goals and Objectives. 7-113-1 Center for Watershed Protection Indicators Tested. 213-2 Data sources used to develop an accurate land use base map for the Coyote Creek watershed. 313-3 Sonde specifications. 363-4 Summary of Analytical Methods, Sample Handling, Detection Limits and Data Quality Objectives. 413-5 Metrics used to evaluate the fish assemblage indicator. 423-6 Metrics used to evaluate the macroinvertebrate assemblage indicator. 453-7 Pollutants monitored by NOI industrial facilities in the Coyote Creek watershed. 473-8 Data sources used to develop an accurate land use base map for the Coyote Creek watershed. 493-9 Data collected, compiled, and used to evaluate the SEIDP stormwater indicators. 554-1 Criteria used to create geomorphic classification of Coyote Creek. 604-2 Gradient and stream flow for Coyote Creek sampling stations. 624-3 Number of Storms Exceeding Coyote Creek Channel Capacities based on Flow at the Edenvale Gage before and 65

after Construction of Anderson Reservoir.4-4 Positive Pearson Correlation Water Quality and Hydrology. 734-5 Negative Pearson Correlations Water Quality and Hydrology. 734-6 Summary of Exceedance of Water Quality Standards in Coyote Creek Runoff. 754-7 Comparison of Metrics in Grouped Stations. 804-8 Rainbow trout captured in 1999 sampling events. 804-9 Significant associations between percent imperviousness and metrics calculated for fisheries data sampled in May, 81

June, and September, 2000 in Coyote Creek from stations U1 - Ref24-10 Subwatershed percent imperviousness estimated using different data sources with different spatial resolution and 87

currentness.4-11 Cumulative percent imperviousness influencing sampling stations estimated using the County Assessor land use 88

data for Coyote Creek drainage area.4-12 Relative contribution of selected land uses to percent sampling station subwatershed imperviousness. 914-13 Percent imperviousness of the riparian corridor within each sampling station subwatershed and of each entire 91

sampling station subwatershed.4-14 Percent imperviousness within the riparian corridor of the Coyote Creek mainstem from its mouth (downstream 92

of 01) through the most upstream sampling station subwatershed (Ref2).4-15 Industrial and Construction site operators in the Coyote Creek Watershed and the Walsh Avenue Catchment that 92

filed NOIs under the 1995 California NPDES permit.4-16 Pollutants monitored by NOI industrial facilities in the Coyote Creek watershed, as a requirement of the 98

California NPDES permit.4-17 NOI filer compliance with the California NPDES Permit requirement for annual report submittal) 98

within Bay Area counties during fiscal year 1997-1998.4-18 New building development completed or under construction from 1995 - 1998 in planning areas (Edenvale, 99

Evergreen, and Berryessa) within Coyote Creek Watershed.4-19 Water Quality Summary Statistics for the Walsh Avenue catchment. 1064-20 Percentage of Load Variability Explained by Different Load Components (Based on ANOVA; storms less than 106

60,000 cubic feet total flow).4-21 Percentage of Load Variability Explained by Different Load Components (Based on ANOVA, all storms). 1076-1 Claytor and Brown (1996) indicator utility and SEIDP criteria used to evaluate each indicator. 116-1216-2 Recommended Refinements to Stormwater Environmental Indicators. 122-1258-1 Summary of Indicator Usefulness 152-153

IX

ABAGANCOVAANOVABASMAABMPsCCRSCDCCDFGCFRCUPACWPDWREMCEOA,Inc.EPTESRIGISGPSHHWlliIICIDINDKLINOINPDESNPSNWSPAPIPQA/QCRBPsRWQCBSCBWMISCVURPPPSCVWDSEIDPSICSJSUSWMPSWPPPSWRCBTIEsTMDLTSSURMPUSEPAUSFSUSGSWERFWQCWQO

Acronyms

Association of Bay Area GovernmentsAnalysis of CoVarianceAnalysis of VarianceBay Area Stormwater Management Agencies AssociationBest Management PracticesCoyote Creek Riparian StationCalifornia Division of ConservationCalifornia Deparment of Fish and GameCalifornia Federal RegisterCertified Unified Program AgencyCenter for Watershed ProtectionDepartment ofWater ResourcesEvent Mean ConcentrationsEisenberg, Olivieri, and Associates, Inc.Ephemeroptera, Plecoptera, TricopteraEnvironmental Systems Research Institute.Geographic Information SystemGlobal Positioning SystemHousehold Hazardous WasteIndex of Biological IntegrityIDicit Connections/Illegal DischargesIndustrial/Commercial Discharger ControlKinnetic Laboritories, Inc.Notice ofIntentNational Pollutant Discharge Elimination SystemNon-point SourceNational Weather ServiceParticipating AgencyPublic Involvement and ParticipationQuality Assurance/Quality ControlRapid Bioassessment ProtocolsRegional Water Quality Control BoardSanta Clara Basin Watershed Management InitiativeSanta Clara Valley Urban Runoff Pollution Prevention ProgramSanta Clara Valley Water DistrictStormwater Environmental Indicators Demonstration ProjectStandard Industrial ClassificationSan Jose State UniversityStorm Water Management PlanStorm Water Pollution Prevenion PlanState Water Resources Control BoardToxicity Identification EvaluationsTotal Maximum Daily LoadTotal Suspended SolidsUrban Runoff Management PlanUnited States Environmental Protection AgencyUnited States Forest ServiceUnited States Geological SurveyWater Environment Research FoundationWater Quality CriteriaWater Quality Objective

1. Introduction

The Stormwater Environmental IndicatorsDemonstration Project (SEIDP) was part ofUSEPA's Environmental Indicators/Measures ofSuccess Project, funded under Clean Water ActSection 104(B)(3).

The first phase of USEPA's projectconsisted of a literature review and publication ofan annotated bibliography of environmentalindicator resources. In the second phase,stakeholders helped select appropriate indicatorsand helped develop of a flexible methodology forusing indicators. Results of these two phases werepublished by the Center for Watershed Protection(CWP) as Environmental Indicators to hsmStormwater Control Programs and Practices (Claytorand Brown, 1996). That report includes "indicatorprofiles", or fact sheets, describing 26 stormwaterenvironmental indicators that may beimplemented to evaluate stormwater programeffectiveness. The authors also outline a two-levelmethodology for selecting and testing indicators,and illustrate scenarios for applying the indicators.

The SEIDP was part of the third phase thatfocused on local demonstration projects andtesting of the indicator methodology described inClaytor and Brown (1996). The WaterEnvironment Research Foundation sponsored theSEIDP joindy with the Santa Clara Valley UrbanRunoff Pollution Prevention Program(SCVURPPP).

1.1 Measuring Stormwater Program"Effectiveness"

As mandated by 1987 amendments to theFederal Clean Water Act, municipal stormwaterprograms are required to:

=> Effectively eliminate non-stormwaterdischarges to storm drains.!

=> Require those engaging in activities that maycause the discharge of pollutants to stormdrains to implement "best managementpractices" (BMPs) to reduce the quantity of

! Except for some discharges (such as condensate fromcooling systems) that are "exempt" if managedproperly.

pollutants discharged to the "maximumextent practicable."

USEPA's 1990 Phase I stormwaterregulations (40 CPR 122.26) specified requiredelements for these municipal programs, includingsurveillance and enforcement to eliminate illicitconnections and illegal dumping, inspection ofindustrial facilities and construction sites, andpublic education.

The purpose of these regulations was toprotect the nation's waters for fishing, swimming,and other uses by reducing the potential effects ofstormwater pollutants.

The 1990 regulations required thatapplications for storm water NPDES permitsinclude: "Estimated reductions in loadings ofpolluu.:nts from discharges of municipal stormsewer constituents from municipal storm sewersystems expected as the result of the municipalstorm water quality management program."Accordingly, most stormwater NPDES programshave included sampling of stormwater dischargesand receiving waters and analysis of these samplesfor regulated pollutants, including nutrients andtoxics such as heavy metals.

In effect, the 1990 regulations set twostandards for "effectiveness." The first standard isto show that specified control measures have beenimplemented to the "maximum extentpracticable." The second, implied standardrequires that programs demonstrate thatmandated control measures are actually reducingthe quantity of pollutants discharged.

Extensive studies - including this one have generally been unable to show that thespecified control measures significandy reduce thequantity of pollutants discharged from municipalstorm drains. Inherent variability in stormwaterpollutant concentrations, magnified by variabilityin runoff volume, tends to confound efforts todetect any downward trend in pollutant loads. Inaddition, other sources -for example,atmospheric fallout and the natural presence oftrace metals in soils - may contribute asubstantially to the total load of many pollutants.However, these difficulties in measurement also

1

STORMWATER ENVIRONMENTAL INDICATORS DEMONSTRATION PROJECT

obscure the uncomfortable possibility thatUSEPA's mandated BMPs and control measuressimply don't have any significant effect on thequantity of pollutants in runoff.

Another, equally important, concern iswhether reductions in urban runoff pollutantloads - even if they were actually achieved would have any meaningful effect on thebeneficial uses of receiving waters. The mostimportant effects of pollutants may be 10cali2edand transitory (for example, suppressed dissolvedoxygen caused by dumping soapy water in astream) rather than cumulative and widespread(for example, chronic toxicity related to the totalamount of copper washed off annually from awhole watershed). Many typical urban runoffpollutants (e.g., lead and zinc) are rarelyconcentrated enough to cause toxicity in receivingwaters. Urban runoff pollutants' are typicallybo~nd to fine sediments and interact withsediments and organic matter, reducing theiraviillability to affect fish or other aquatic life.

Beginning in the early 1990s, as thestormwater NPDES pennit program wasimplemented, USEPA began to reassessstormwater monitoring parameters and goals withan eye towards development of comprehensivemonitoring programs that characterize overallconditions in the receiving water and providebenchmarks for assessing the success ofstormwater management efforts2 (Claytor andBrown, 1996).

1bis reassessment tends to redefine, andbroaden, what regulators expect stormwaterprograms to achieve. The fundamental intent ofthe 1990 stormwater regulations - to reduceaverage annual loading of toxic pollutants inrunoff - has been found less relevant, and amove is on to redirect stormwater programs toaddress other, more significant problems affectingwater bodies that receive urban runoff.

2 Perhaps as a result of this reassessment, USEPA'sPhase II stotffiwater regulations, issued in 1999, requitethat municipalities covered under the new stotffiwaterNPDES permits simply "evaluate program compliance,the appropriateness. of your identified best managementpractices, and progress towards achieving youridentified measurable goals."

2

As a group of storm water regulators andmanagers concluded after a 1995 series ofmeetings: "Storm water programs will only beeffective when a paradigm shift occurs in regardto our approaches to dealing with it as a pollutantsource. Perception has to move from traditional,end-of-pipe, water-chemistry, broad-spectrumpollutant monitoring, treatment and commandand-control enforcement, to a mindset focusingon receiving water body quality and the beneficialuses that the community desires for that waterbody" (Rensselaerville Institute, 1995).

Urbanization has myriad and complex effectson downst;ream water bodies. Consider, forexample, the cascading interaction between landusers and adjacent urban streams. Alteration ofthe landscape, and changes to watershedimperviousness, change the quantity and timing ofrunoff and the amount of sediment reachingstreams. As a result of these hydrologic changes,the configuration of the stream channel (width,depth, meanders, riffle/pool length) begins tochange toward a new equilibrium with itswatershed. The resulting movement of sediment(e.g. downcutting and bank erosion) affects waterquality and physical habitat quality. Bank loss andflooding then leads adjacent property owners tophysically alter the stream (e.g. riprap,channelization) in an effort to control the changesresulting from the disequilibrium. The physicalalteration damages or' eliminates the remaininghabitat.

The nature of these problems, and theirsolutions, are quite different from the pollutionprevention mandate of the 1990 stormwaterregulations. In effect, stormwater programs nowface three sets of standards for effectiveness:

1. Implementation ofB11Ps and other controlmeasures to the "maximum extentpracticable."

2. Reduction in the quantity of pollutantsdischarged from storm drains.

3. Protection and enhancement of beneficialuses.

One can hypothesize circumstances wherethese standards coincide, i.e., where storm drainpollutants (and specifically, average annualpollutant loading) have discernable effects on

beneficial uses and mandated B:MPs canmeasurably reduce the loading of these pollutants.Of Claytor and Brown's (1996) three "theoreticalscenarios to illustrate the potential application ofstormwater indicators in real world situations,"two imagine just such a situation.

However, our experience managing andmonitoring stormwater programs .suggests thatsuch situations are atypical. In our experience,implementation of the mandated B:MPs does notmeasurably reduce average pollutant loadings, andthe expected changes to those loadings would notdiscernibly improve attainment of beneficial uses.Further, the major effects of urbanization onbeneficial uses - and particularly, the effects offlow management and channel alteration appear outside the influence of stormwaterprograms as designed and mandated under theNPDES pennit program.

This incommensurability in goals andmonitoring parameters has put stormwaterprogram managers in a double bind.

As stewards of local government dollars,stormwater program managers must comply withenforceable NPDES pennit provisions atminimum cost to the public, and they must notexpand their efforts beyond what has beenapproved by their agencies' public process. Butthey must also answer to the desire of regulators,environmentalists, and the public to address thereal problems affecting the uses of local streams,lakes and estuaries.

Urban watershed management provides apotential solution to this double bind, because itcan place the stormwater pollution-preventionprogram in the context of a multi-agency,community-wide effort to protect and enhanceurban waters. It may be possible to define astormwater program role that stays within thegeneral pollution-prevention mandate, makesefficient and reasonable use of public dollars, andcontributes significandy to watershedmanagement. Achieving consensus amongmunicipal managers, regulators, and the publicregarding the appropriate role for stormwaterpollution prevention programs is a worthy earlyobjective of an urban watershed managementprogram.

With these considerations in mind, westarted this project by proposing a working

CHAPTER ONE - INTRODUCTION

definition of an "effective" stormwater programas one that:

=> meets the obligations stated in its NPDESpennit and management plan.

=> makes decisions openly and is responsive tocontributions and new ideas from regulatorsand the public.

=> is continuously improving.

=> is actively involved in broad, stakeholderbased efforts to control pollution and toassess, protect and enhance beneficial uses oflocal waters.

1.2 Background on the Santa Clara ValleyUrban Runoff Pollution Prevention Program

SCVURPPP includes 13 cities and townswithin Santa Clara County and within the SantaClara Basin, which is the watershed of South SanFrancisco Bay (Figure 1-1). SCVURPPP alsoincludes the Santa Clara Valley Water District(which provides flood management and watersupply services within the area) and Santa ClaraCounty. (fable 1-1).

The Santa Clara Basin is a broad, northwarddraining valley between the Santa Cruz Mountainsto the west and the Diablo Range to the east. Itsinterface with South San Francisco Bay is linedwith sloughs, salt ponds and salt and brackishmarshes that lead up to grasslands and woodlandhabitat above the basin floor. The Basin floor isflat and fertile. Since the mid-1950s, housingdevelopment, business and industrial parks,shopping centers, and freeways have replacedagricultural land uses. This development wastriggered by the emergence of the electronicsindustry. Stanford University in Palo Altospawned the earliest firms engaged in electronicsand further supported the growth by building theStanford Industrial Park. As available land in PaloAlto became scarce, the electronics andsemiconductor industry moved south intoMountain View and Sunnyvale, then into SantaClara and Cupertino. By the 1970s, industrieswere concentrated in the northern portion ofthe valley, with residential areas extendingsouthward. Very-low-density, affluent residential

3

Legend

o Watersheds in the Santa Clara Basin

1:1 SCVURPPP Boundary

/\/ Surface Waterbodies

_ Sau Francisco Bay

Program Co-permittees_ Campbell

g,,4«ij Cupertinoz,f.,;§£~ Los Altos

Los Altos Hills.. Los Gatos~~ Milpitas~ Monte Sereno_ Mountain ViewW1Mj, Palo Altol1li San Jose

Santa Clara~ Saratoga_ Sunnyvale

Santa Clara County

Santa Clara Valley Water District(Jurisdiction corresponds to Santa Clara County)

~

W:::ta Clara ValleyUrban RunoffPollution Prevention Program

Map Revised 12/8/99

San MateoCounty

~'-----l

s

Alameda County

Santa ClaraCounty

o 20 40 Miles~i~~~~~~~~liiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii!

Figure 1-1. SCVURPPP area, Program Co-permittee jurisdictions, and Santa Clara Basin Watershed Boundary.

Table 1-1. Santa Clara Valley Urban Runoff PoUutionPrevention Program Co-Permittees

Milpitas. San Jose. Santa Clara. SNnnyvale. MOlintainVieJl/ • Polo Alto • ClljJertino • Saratoga. CampbeU. MonteSmno. Los Gatos. Los Altos. Los Altos HiliJ. CONnty of

Santa Clara. Santa Clara Valley Water District

areas developed 10 the western foothillcommunities.

The 1998 population of the Santa ClaraCounty is nearly 1.7 million, 95 to 96 percent ofwho live within the Program area. According tothe Association of Bay Area Governments'(ABAG's) Projections 1996, the county's populationin the county will grow to about 1.9 million by2015, and the economy will add 215,000 new jobs.

The Santa Clara Basin has warm, drysummers. Total annual rainfall, almost all of whichoccurs between October and April, varies from 60inches in the Santa Cruz Mountains to about 12inches in the eastern parts of the Basin. Creeksand streams that originate in the Santa CruzMountains and the Diablo Range drain throughthe Santa Clara Basin into South San FranciscoBay. These creeks include Coyote Creek on theeast side of the valley, the Guadalupe Riverwatershed, which drains the south-central portionof the valley, and several small, relativelyurbanized watersheds that drain the west side ofthe valley. Two water pollution control plants, inSunnyvale and San Jose, discharge to tidal sloughs.A third plant, in Palo Alto, discharges to SouthSan Francisco Bay. (Ibere are no wastewaterdischarges to South Bay creeks.)

The SCVURPPP, formerly the Santa ClaraValley Non-point Source Control Program, hasbeen recognized as one of the most advancedsuch programs in the U.S.3 SCVURPPP's 15member agencies were among the first inCalifornia, and in the U.S., to begin implementingcontrol measures for urban runoff pollutionprevention.

The Program was organized in response tothe 1986 Regional Water Quality Control Plan forthe San Francisco Bay Region (Basin Plan). The15 agencies prepared a plan (CH2MHill and EOA,

3 The Program received EPA's First Place Award forBest Stormwater Program in 1994.


Inc. 1987) to characterize urban nonpoint sourcesand to identify and evaluate existing and additionalcontrols. The 15 agencies then signed aMemorandum of Understanding to jointlycontribute to a series of monitoring and BMPstudies leading to a control plan.

These materials became the basis for anNPDES permit application. In June 1990, theCalifornia Regional Water Quality Control Board(RWQCB) issued an early NPDES municipalstormwater permit jointly to the 15 agencies, orCo-permittees (RWQCB 1990). Permit provisionsrecognized that the Program had alreadyaccomplished significant work, which theRWQCB considered equivalent to municipalstormwater permitting requirements promulgatedby EPA later that year.

As part of the 5-year NPDES permit cycle,the 15 Co-permittees developed and submitted asecond SWMP to the Regional Board on June 30,1995. The Regional Board approved the SWMPand issued the second NPDES storm waterpermit on August 23, 1995. The SWMP includedmetals control measures to address a TMDL andwasteload allocation for copper and nickel inSouth San Francisco Bay. The permit alsorequired that the Program develop ''watershedmanagement measures." The 1995 Permitrequired the Program to develop a set ofPerformance Standards during 1995-1996. Thepermit defined Performance Standards as "thelevel of implementation necessary to demonstratethe control of pollutants in storm water to themaximum extent practicable."During 1996 and 1997, the Program'sManagement Committee, compns1Ogrepresentatives from each of Santa Clara County's15 cities and towns, Santa Clara County, and theSanta Clara Valley Water District, reviewed theProgram's goals and organization. The reviewprovided the context for the rewriting andresubmittal of the Program's Urban RunoffManagement Plan (SCVURPPP 1997).The stormwater program has now completed itssecond 5-year permit cycle, and has applied forwhat may be the nation's first "third generation"NPDES stormwater permit.

5


1.2.1 SCVURPPP Goals and ObjectivesSCVURPPP's mission is: "To assist in the

protection of beneficial uses of receiving watersby preventing pollutants generated from activitiesin urban service a.reas from entering runoff to themaximum extent practicable."

The mission:

=> Targets pollutant reduction measures that a.reneeded to help protect beneficial uses.

=> Focuses on urban pollutant sources (asopposed to nonpoint sources generally).

=> Sets a specific benchmark forimplementation (as opposed to doing"anything and everything" related topollutant sources).

This focused approach is consistent with theProgram's idea of working with other parties orinstitutions that are better equipped to carry outspecific pollution control strategies. The Programconcentrates its own efforts on identifying pollution sources, and implementing pollution prevention measures, that are clearly within theauthority and ability of the Co-permittees.

The SCVURPPP's approach is to work withother parties or institutions that are betterequipped to carry out specific pollution controlstrategies, focusing Program resources onidentifying pollution sources and implementingpollution prevention measures that are clearlywithin the authority and ability of the 15 Copermittees. The SCVURPPP's goals are:

GOAL 1: ComplY with Permit

=> Effectively prohibit non-stonnwaterdischa.rges (unless exempt or managedaccording to approved conditions).

=> Reduce, to the maximum extent practicable,pollutants in stonnwater runoff.

=> Comply with permit submittal requirements.

GOAL 2: Determine SIIccm

=> Periodically evaluate the attainment ofbeneficial uses in selected waterways.

=> Evaluate changes in public awareness andbehavior.

6

=> Evaluate effectiveness of specific controlmeasures at pollution reduction.

GOAL 3: AefjllstActivities to Meet Changes

=> Define what constitutes success (how muchis enough?) as it relates to programmatic andtechnical MEP.

=> Utilize what we learn to plan the next steps.

GOAL 4: Achieve Acceptance ofUrban RJlnojJManagement Activities

=> Effectively facilitate public input intoProgram planning process.

=> Integrate urban runoff goals at various intraagency levels.

=> Develop and maintain a proactiveinterrelationship with regulatory authorities.

=> Publicize the efforts of the Co-permittees(program)

GOALS: Integrate Urban RJlnojJProgramElements into other Programs

=> Promulgate an understanding of the role ofthe urban runoff program.

=> Encourage other agencies to becomeinvolved in urban runoff issues.

=> Encourage action by the appropriateagencies.

1.2.2 Companion to Goals and Objectives ofOtherStormwater Programs

To help insure broad applicability of thisstudy, we also obtained goals and objectives fromselected stormwater programs throughout the Bayarea, California, and the U.S. Table 1-2 compa.resgoals and objectives from the AlamedaCountywide Clean Water Program, San MateoCountywide Stormwater Pollution PreventionProgram, Marin County Stonnwater PollutionPrevention Program (Bay Area); Fresno-ClovisStorm Water Quality Management Program(Fresno, CA); City of Eugene StonnwaterManagement Program (Eugene, OR); HamptonRoads Planning District Regional StonnwaterManagement Program (Chesapeake, VA); and the

Table 1-2a: Com anson of Stormwater PrAlameda Countywide Cleanwater ProgramAlameda Coun , CA

Goal: Comply with the NPDES permit, maximize regulatorycertainty by participating in regulatory planningprocesses,effective program managementObjectives:1) Annual reviews and reponing of member agencies'activities2) Participation in BASMAA

Go : Continue WOr w municip maintenance to illways to optimize removal of pollutants and minimizemaintenance dischargesObjectives:1) Implementperformance standards and develop additionalones to address parking lots, sidewalks, flood controloperations, municipal swimming pools, fountains, andrecreational water bodies.

Goal: Eliminate illicit dischargesObjectives:1) Conduct field surveys of storm drainage system2) Identify sources of non-stormwater discharges3) Provide technical assistance in identifying sources w/ nonmember a encies that rovide s ill re onsetdean-uGo : PIP: ucate area resi ents an encourage esspolluting behavior.Objectives:1) Target outreach about residential yard and garden care2) Reinforce existing poll. prevention messages3) Support watershed-based approaches4) Evaluate effectiveness and update performance standards5) Assist with staff training and continue collaboration withother educational rou s

Go : To wor wi mUDlC1p pu 'cworways to optimize removal of pollutants.Objectives:1) Implement performance standards; outreach andtraining for staff/public2) Coordinate other STOPP subcommittees, otherpublic agencies/private industries3) Assist with regulatory compliance and planning

Go: ucate t e pu .c a out n to prevent stormwaterpollution and protect creek/wetland habitatObjectives:1) Distribute stormwater pollution prevention information2) notify creek-side homeowners3) Create an MCSTOPPP web page4) Stencil storm water drain inlets

Table 1-2a: 1of 2

l'able 1·2a: Com. of Stonnwater PrAlameda Countywide Cleanwater ProgramAlameda Coun t CA

Goal: Implement and ensure compliance with newdevdopment and construction controls.Objectives:1) Provide guidance on cost-effective stonnwater qualitycontrols2) Control consttUCtion related discharges3) Promote outreach4) Implement and update performance standards5) Coordinate with flood control agency

'-7\>""" •.~ULCpo utants in stormwater runoeliminate non-stormwater discharges to stonn drains fromindustrial and commercial facilitiesObjectives:1) 10 and minimize· potential pollutant soUrces throughfacility inspections, outreach activities with businesses, andappropriate follow-up/enforce

. Use monitoring s to riz.estonnwater pollutant problems and identify improvedsolutionsObjectives:1) Track and coordinate with SFEI RMP and BASMAA'sMonitoring Committee2) Continued routine monitoring3) Condua special studies to id sources of pollutants andpotential controls4) Data management

Goal: Determine the tangible water quality and aquaticresource benefits of using a focused waterShed managementapproach in urbanized watershedsObjectives:1) Participate in watershed management projects ledbyotheragencies2) Conduct paot watershed project3) Identify results, incorporate into Program

Pollution

erosion,

mlDlmlU te poten stormwaterpollution sources at industrial facilities; prohibit illicitdischargesObjectives:1) Help municipalities implement performancestandards2) Providing training and outreach materials tomunicipal staff and industries3) Provide incentives for businesses to comply4) Continuously evaluate effectiveness of STOPPP

Go . eetive BMPs tooto identify creek drainage basin-specific, water qualityissuesObjectives:1) Participate in the BASMAA Monitoring strategy2) Evaluate the eHectiveness ofBMPs3) Assess the state ofsigni6rnmly urbanized watersheds4) Evaluate effectiveness of watershed studies

Marin County Stormwater Pollution PreventionPro • Conn • CAGoal: Minimize pollutants in runoH from consuuction anddevdopmentObjectives:1) Limit potential devdopment in county2) Adopt control measures on new site devdopment3) Distribute brochures on BMPs for consuuction industry4) Participate in ABAG training inerosion and sedimentationcontrol methods5) Develop partnership with Marin Builder's Association

Go . Contro . orce stonnwaterordinancesObjectives:1) Devdop a business inspection plan for each localprogram2) Incorporate facility inspeaions into existingfireinspeetionprograms3) Prohibit non-stOrmWater discharges to creeks and stormdrains4) Require BMP implementation5) Prohibit alterations to watercourses without permission

OTHER:Goal: Use local Program administration to create financingand planning efforts for MCSTOPPPObjectives:1) Document expenditures on local programs and describelocal program budgets2) Develop plan for outreach, inspection, andenforcement atbusinesses that may pollute stormwater3) Create Enforcement Committee to devdop enforcementoptions to abate pollution .4) Fund a Flood Control naturalist

Table 1-a: 2 of 2

Table 1-2b: Comparison of Stormwater Prottram Goals and Objectives {Prottrams outside of Bay Area}City of Eugene Stormwater Management.

Program(Eugene, OR)

Goal: Meet requirements of Clean Water Aer.for nonpoint source pollutionObjeer.ives:1) Develop proactive acquisition program forexisting drainage channels2) Determine feasibility of establishing andmaintaining water quality facilities3) Create and enforce wa~er quality standardsfor new development, including postconstruer.ion4) Clarify and strengthen enforcement ofregulations to eliminate improper disposal ofpollutants

Fresno-Clovis Storm Water QualityManagement Program

(Fresno, CAfGoal: Protect resources and beneficial usesfrom degradation by urban runoffObjectives:1) Identify pollutants in urban runoff thatpose threat to natural resources 2) Controlsources of pollutants which pose greatestthreat3) Comply with federal NPDES mandate tocontrol discharge of pollutants intostormwater drainage system4) Develop cost-effeer.ive program to preventstormwater pollution

Goal: Assess Etteer.iveness of ProgramObjeer.ives:1) Use focus groups to refine and updatecommunication style and enhance culturalappropriateness2) Reassess public attitudes, perceptions andpraer.ices of storm water quality and relatedenvironmental issues

Hampton Roads Planning DistrictRegional Stormwater Management

Program (Chesapeake, VA)Goal: Satisfy VPDES stormwater permitrequirementsObjectives:1) Enhance eroSIon and sedimentationcontrol2) Manage illicit discharges, spill response,and remediation

Goal: Manage stormwater quantity and qualityto maximum extent praer.icableObjectives:1) Implement BMPs and retrofit floodcontrol projects to provide water qualitybenefits2) Suppon site planning and plan reviewaer.ivities3) Manage pesticide, herbicide, and fenilizerapplicationsGoal: Implement Regional StudiesObjeer.ives:1) Undenake regional studies to suppon localstormwater management programs2) Develop indicators of programeffeer.iveness3) Develop more cost-effective approach toexisting monitoring program

Jefferson County, KentuckyMetropolitan Sewer District

Uefferson County, KY)Underan EPA Region 4NPDESpermit., vJJit:hcontains very specifUlanguageas to what theprogram musttUhieve. Therefore, theyhavenotformally developed a set ofobjectives.

NOTE: The lastgoals/objectives listed in thistahleforJe/fenon ccnmtyaremoreromparabletothose listedfor &ry Area programs above.

Goal: E uate BMP's and each of the 5program areas.Objectives:1) Establish evaluation process _2) Derme evaluation method (performancemeasure) and repon results of assessment.3) Performance evaluation of BMP'sGoal: Implement monitoring programObjectives:1) Assess specific wet weather impaer.s2) Characterize stormwaterlnon-pointsource run-off quality from discrete landuses3) Colleer. data to identify water qualitytrends4) Utilize information from local volunteermonitoring programs

Table 1·2b: 100

Table 1·2b: Comparison of StormwaterPro~Goals and Obiectives (Programs outside of Bay Area)City of Eugene StormwaterManagement.

Program(Eu~e,OR)

~al: Educate the public about water relatedISSUesObjectives:1) Evaluate ways tranSportation authoritiescan reduce pollutant discharge2) Develop program for cleanup afterst:l'tlCtUt:l1 fires and vehicular accidents toprevent contaminants from washing intostorm drains3) Suppon Tree planting programs4) Coordinate with county government toexpand programs, which provide means forproper disposal ofcommonly used pollutants


(Fresno, CAfGoal: Educate the public to betterunderstandand participate in control of urban runoffpollutionObjectives:1) Inform the public about FresnoMetropolitan Flood ControlDistria's effortsto manage storm water quality2) Educate public about sources ofstormwater pollution3) Educate public about proper use anddisposal of "materials which contribute tostormwater pollution4) Assess effectiveness of PIE aaivities andencourage behavioral change.

Goal: Maintain and Promote SchoolEducation ProgramObjectives:1) Develop clean stormwater learningaaivities materials2) Provide teacher workshops3) Make presentations4) Panicipate in programs to promoteawareness

Goal: Condua Outreach AaivitiesObjectives:1) Expand distribution netWorks andmaterials2) Presentation program for service groups,businesses3) Implement stormwater qualitymanagement events4) Promote hotline for illegal dumping5} Enhance multi-cultural outreach

Hampton Roads Planning DistrictRegional StOrmwater Management

Program (Chesapeake, VA)Goal: Implement Public· Education andTrainingObjectives:1) Employ an Environmental EducationCoordinator2) Increase public understanding ofstormwater issues3) Augment and enhance local stormwatereducation programs4) Inaease panicipation by public inprograms and aaivities to reduce stormwaterpollution.


Oefferson County, KY)

Goal: Educate public in goodhousekeeping!pollution preventionObjectives:1} Utilize ~MPs to protect storm sewersduring street maintenance2) Clean catch basins, storm sewers andchannels regularly3) Evaluate alternative deicing chemicals4) Condua~MPMaintenance5) Provide guidance on proper disposal ofwaste materials6) Continue existing programs

Goal: Develop public outreach programsObjectives:1) Implement Adopt a Stream/CreekSweep programs2) Distribute pamphlets on non-pointsource pollution3) Synthesize annual report with stateWidecomparison4) Educate staff

Table i-2b: 2 of 3

Table t·2b: Comparison of Stormwater Program Goals and Objectives (Programs outside of Bay Area)City of Eugene Stormwater ManagemenL

Program(Eugene, OR)

Goal: Implement the West Eugene WetlandsPlanObjectives:1) Develop program to inventorypublic/private parcels used for mitigation2) Implement field program to detect!preventillegal dumping of pollutants.3) Monitor stormwater from industrialfacilities4) Implement effective erosion controlprogram

Goal: Maintain quality and effectiveness ofstormwater systemObjectives:1) Evaluate existing maintenance program toensure efficiency2) Seek modification of federal regulations fordesign and maintenance of open waterchannels3) Modify existing land use regulations

Goal: Protect public and adjoining land usefrom flood/drainage damageObjectives:1) Develop comprehensive drainage basinplans2) Maximize capacity of existing stormwaterfacilities3) Use FEMA 100-year floodway boundariesand waterway setbacks as recommendedthrough adopted plans


(Fresno, CAfGoal: Coordinate with other programsObjectives:1) Participate in statewide coordinationefforts to develop consistent messages aboutstormwater pollution prevention.2) Review state, national and regional stormwater quality program activities3) Develop materials to communicate BMPs

Goal: Provide stable and equitable fundingsourceObjectives:1) Provide funding for acquisition ofwaterway corridors related to stormwaterconveyance2) Develop program to provide financialincentives to property owners who protectnatural areas on their property

Hampton Roads Planning DistrictRegional Stormwater Management

Program (Chesapeake, VA)Goal: Legislative/ Regulatory MonitoringObjectives:1) Monitor state and federal legislative andregulatory activities that may have impact onlocal stormwater management programs2) Develop briefing materials for use bylocalities, and consideration by governingbodies

Goal: Meet needs of CitizensObjectives:1) Address flooding and drainage problems2) Maintain the stormwater infrastructure3) Protect waterways4) Provide the appropriate funding for theprogram


Oefferson County, KY)

OrnER:

Goal: Detect and Eliminate illicit dischargeand improper disposalObjectives:t) Implement an aggressive follow-upprogram2) Revisit and retest contaminated outfalls,and determine source3) Develop tracking system to estimatevolume of discharge4) Amend local ordinances

Goal: Develop aggressive program tocontrol non-point pollution sources fromconstructionObjectives:t) Require new-development to followBMPs and keep all sedimentation on-site.2) Offer training for designers, planners,developers, equipment operators3) Provide guidance materials4) Conduct scheduled inspections ofBMPs5) Require BMP maintenance schedule

Goal: Implement post-eonstruetioncontrolsObjectives:t) Initiate watershed assessments2) Initiate BMP pilot projects3) Reduction to on-site imperviousness

Table 1-2b: 3 aD


Jefferson County, Kentucky Metropolitan SewerDistrict (Jefferson County, KY).The consistency of goals among the programsreflects their common Federal pollutionprevention mandate. Most programs also showinterest in a broader approach to protecting and

. enhancing beneficial uses of urban streams. Thelatter goal seems to be emphasized more amongprograms that are operated by flood controldistricts, as opposed to those operated bymunicipalities (who may have less directresponsibility for streams).

1.2.3 Peiformance StandardsMost activities of SCVURPPP Co-permittees

- and the level of hnplementation for thoseactivities are defined in PerformanceStandards. Performance Standards describe aspecific result, or level of effort, that constitutesthe "maximum extent practicable" based oncurrent technical knowledge, available resourcesand local conditions. The Program has modelPerformance Standards for:

=> Illicit Connection and illegal DumpingElimination Activities.

=> Industrial/Commercial Discharger ControlPrograms.

=> Public Streets, Roads and HighwaysOperation and Maintenance.

=> Storm Drain System Operation andMaintenance.

=> Water Utility Operation and Maintenance.

=> Planning Procedures.

~ Construction Inspection.

, In addition, the Program prepared a PublicInformation and Participation (pIP) frameworkthat the Co-permittees have used to develop theirindividual PIP programs and the ManagementCommittee has used to develop a joint PIPprogram. The Performance Standards are updatedas part of the Program's process of continuousimprovement.

The model Performance Standards assist Copermittees to develop their local programs. Copermittees have the option of adopting the model

12

Performance Standards without changes, or maydevelop their own Performance Standard byadapting the model Performance Standard to suittheir local conditions. In developing their ownPerformance Standards, Co-permittees cite. theirspecific characteristics to justify a different degreeof implementation

1.2.4 Santa Clara Basin Watershed ManagementInitiative

SCVURPPP's June 1995 Proposed StormWater Management Plan contained fiveWatershed Management Measures, beginning withinstitutional arrangements and leading, after someyears of planning, to area-wide watershedmanagement. Since that time the Program hashelped forge a new approach that brings instakeholders at the beginning of the planningprocess. The Program's 1997 Urban RunoffManagement Plan incorporates participation inthe Santa Clara Basin Watershed ManagementInitiative (SCBWMI).

In April 1996, the U.S. EnvironmentalProtection Agency, the SWRCB and the RWQCBinitiated the SCBWMI. These regulatory agenciesrecognized that, up to that time, issues affectingwatersheds have been addressed by a "patchwork"of separate regulatory actions. There was a need tocoordinate regulatory activities on a basinwidescale.

The regulatory agencies invited various,interested parties (stakeholders) to discuss theirinterest in watershed management and how tobegin planning watershed use and protection.Program staff and Co-permittee staff haveparticipated in this Core Group, and variousSCBWMI workgroups, since their inception., A fact sheet issued by this Core Groupstates: "Many diverse factors impact the basin,including water quality, land use, flood protection,water supply and habitat protection. A holisticstrategy is required to confront and manage theseissues. By addressing all sources of pollution thatthreaten the Bay, we can achieve a sustainablebalance of human and natural uses and needs.Regulators, along with local community,environmental, agricultural and businessrepresentatives, must maintain a continuous,

productive dialogue to protect the Santa ClaraBasin."

While this study was underway, Gune 1998June 2000), SCBWMI stakeholders collaborated toproduce a Watershed Characteristics Report,which is intended to lay the groundwork for aWatershed Management Plan. A detailedassessment of three of the basin's 14 watersheds iscurrendy underway. The SCBWMI will participatein assessment of the Coyote Creek watershedduring 2000-2001; that assessment will make useof data and analyses conducted under this study.

1.2.5 Continuous ImprovementThe SCVURPPP is dedicated to a process of

continuous review and improvement, whichincludes seeking new opportunities to controlstormwater pollution and to protect beneficialu~es. When such opportunities arise, the Programrevises, updates and adds to its activities, controlmeasures, BMPs and Performance Standards. Thechanges are documented in annual reports.Regional Board staff and Co-permitteesparticipate in annual reviews of the Program'swork and the setting of priorities for the comingyear. This review is also an opportunity to checkprogress on activities required under theProgram's permit and on previous Programcommitments.

In addition, annual Co-permittee reviewmeetings facilitate in-depth review of specificactivities and documentation, and help familiarizeRegional Board staff with the issues andchallenges faced by local urban runoff programcoordinators. The reviews also provide anopportunity for local staff to comment on theProgram's work and identify additional ways thatthe Program could assist local pollutionprevention efforts. A meeting summary and list ofaction items follows up each local programreview.

As the SCBWMI assesses urban watershedsand develops a watershed management plan, theCore Group and workgroups regularly identifyspecial studies, or institutional needs, that theProgram (among SCBWMI stakeholders) is bestsuited to implement. For its part, the Program hasidentified four general areas of support for theSCBWMI:


=> Support for field work and other watershedassessment tasks.

=> Administrative support for SCBWMIworkgroups.

=> Support related to land use issues inwatershed planning.

=> Support for outreach and public education.

The Program's continuous improvementprocess is illustrated in Figure 1-2.

13

Fgure 1-2. Continuous Improvement

~~~~~~~~~~~~~~:::::::::::::::::::::::::::::::::::::::::::::~~~~~~;~~~~~~~;~ ~~~~~~~~~~~;~~~;~;~;~;~;~;~;~~~;~;~;~~~~~;~~~;~~~~~~~~~;~~~~~~~~

~ ~ ~ : ~ : ~ : ~ : ~ : ~ : Urban Runoff Management Plan j :~ :~ :~ :~ .. .Management .. :.:.:. ~ : Participation in the Santa Clara Basin i~ ~nnHnHnH ~ ~ ~ ~ ~ ~ ~ ~ : Committee 1. ~ ~ ~ ~: Wate<rshed Management Initiative ..

~ ~ ~ ~ : ~ ~ :~..~............~~87~ .. .. .. .. .. .. .. .. .. .. .. .. ................ .. .. .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. .. .. .. : : : : : : :

Program ~ ~ ~ ~ Participation with : ~ : Co-Pennittee : ~ : ~ : ~ : ~ : ~ :. Identification of : ~ : IJoint Activities .. Other Entities . : . Implementation ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . Urban Pollutant : ~: RecommendatiO~ZS

::: ::::::::: Sources or -:' for Work or Further.:-: .... ~:~:~:~:. Watershed :~: Study.:

~~~ ~~~~~~~~~~""~II!I:UIl!l~L-:~ ~"~/~:;~H:U~:;~H::~:~jj::m:::::; ~:m::):::: Y/ Impacts / ,;: .

. . . . . . . . . . . . . . . . . . . H~ ~ ~ ~ n~ ~:: Referral to SCVURPPP .. : : ~ ~ ~ ~HnHHH)UUU~ . . . . . . . :~ ~ ~ ~ ~ ~:- Management Committee :: ~ ~ ~:: ~::

!!!!!!!!!!~~!~~;!;!;...................... .. .. .. .. .. .. .. .. ........................ .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. ........................ .. .. .. .. .. .. .. .. ..

Annual Report

.. .. .. .. .. .. .. .. .. ......................:=: ::::=: =: =: =::::::.. .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. ..

:~:~ :~:~:~:~ :~: ~:~:~.. .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. .. ..

.:.:.:.:.:.:.:.:.~~-"'~' ~.~.-,.-".~~. ~'-"-"'~'~.~~.. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .... .. .. .. .. .. .. .. ... .. .. .. .. .. .. .... .. .. .. .. .. .. .. ..::::::::::::::~:

:~:L__~~~~. S··'·m·8'.r--.... :~ :.:.:::::::::::::: :


2. Project Objectives

~ The SEIDP's objectives were to:

~ Evaluate the usefulness of the two-levelStonnwater Indicator Methodology undersemi-arid conditions;

~ Evaluate the use of environmental indicatorsunder semi-arid conditions for two scales ofanalysis; within a watershed, emphasizingchemical, physical and biological indicators,and within an industrial catchmentemphasizing programmatic indicators;

~ Select, test, and refine protocols formonitoring environmental indicators in semiarid conditions;

~ Develop guidance on selection and use ofenvironmental indicators, and disseminateguidance to other stonnwater programs inCalifornia, Oregon and the west to assist invalidation of environmental indicatorsthroughout the west.

15


16

3.

Claytor and Brown's (1996) stonnwater indicatormethodology (Figure 3-1) distinguishes betweentwo levels of analysis:

=> Level I, Problem identification.

=> Level II, Assessment of stonnwatermanagement program.

The Level I methodology consists of five stepsthat include (1) establishing which entities areresponsible for implementing management of amonitoring program, (2) gathering and reviewinghistorical data to identify problems that have beenpreviously associated with stonnwater programsand effectiveness of management efforts toaddress such problems, (3) identifying receivingwater impacts, (4) inventory resources and identifyconstraints to implementing a monitoringprogram, and (5) assess baseline conditions ofreceiving waters.

The Level II methodology consists of sixsteps that include (1) stating the stonnwaterprogram goals, (2) inventorying prior and ongoingstonnwater program management activities, (3)developing and implementing program activitiesto achieve program goals, (4) developing andimplementing a monitoring program usingindicators to evaluate the success of thesto~w~ter management program, (5) assessingmorutonng results and evaluating stonnwaterprogram effectiveness, and (6) re-evaluating thestonnwater management program.To test the usefulness of the indicatormethodology, we applied both the Level I andLevel II steps to the Santa Clara Valley UrbanRunoff Pollution Prevention Program(SCVURPPP, or Program).

To evaluate individual indicators, and toselect, test, and refine protocols for implementingthose indicators, we applied 20 of Claytor andBrown's 26 indicators at three different spatialscales: within the 350 square-mile Coyote Creekwatershed, within the 28-acre industrial WalshAvenue catchment (Figure 3-2) and throughoutthe Program area. Different environmental indicators were evaluated at each scale (fable 3-1).

In Coyote Creek, environmental indicatorswere used to conduct three types of comparisons:

Methods

1) temporal comparisons in the upstreamreference areas, where land uses have notchanged recendy,

2) temporal comparisons at the stations inCoyote Creek that have transitionedfrom rural to urban and

3) spatial comparisons between theupstream reference stations and thedownstream urban stations.

In the Walsh Avenue catchment, waterquality indicators, programmatic indicators, socialindicators, and site indicators were used to gaugesuccess of Program implementation. Results fromwater quality monitoring were used to evaluate ifchanges in water quality can be correlated to theProgram's efforts.

We applied three social indicators to theSCVURPPP area.

3.1 Study Areas

3.1.1 Coyote CrrJek WatershedThe Coyote Creek watershed (Figure 3-3) is

fairly typical of an urban watershed in California.The pattern of an intact headwaters region and amore developed region in the lower gradientdownstream portions of the watershed, impactsfrom current and historic mining, grazing, andagriculture, modifications to hydrology andgeomorphology, are all factors affecting manyWestern streams and rivers today.

Coyote Creek watershed is located in theSanta Clara Basin, at the northern extent ofCalifornia's Central Coast Range, and thesouthern extent of the San Francisco Bay Area.Coyote Creek flows approximately 70 miles fromits headwaters in the Western Diablo Range,northeast of Morgan Hill, to where it dischargesto South San Francisco Bay, in the city ofMilpitas. Approximately 55 percent of thewatershed is above Anderson Dam and iscomposed primarily of rural land under thejurisdiction of Santa Clara County. The drainagearea below the dam js both rural and urban, with

17

Figure 3-1. Claytor and Brown (1996) Storrnwater Indicator Methodolo~y - two levels of analysis.

STORMWA11lR.INOICATOR METHODOLOGYl.EVBL I, PROBLEM IOnNnFlCAnON

.. l;l .. , •... ~.,...,;'.L ,: .,.~

WIlD tflU IN raporulbll/orlmpltmmt4tIDnP A lO"mlln8ll1 IIpnC)/ (ItUInl&lplll/~. _,,~. Dr ,tAu) Dr II "lHptdfl& ',,,1""':1'WI"" tJIMrpmgnrnu lIN"""glmpltmmuJ within du Wdtmhlll? CAn"''' P"'l"IIn1 hi d""loptJ 1111I1 Implnnn,uJ on II 1WJlm11111.Md, 0" blll/n bIIIu?

u

''''lIdlY.tprvgnmss lIIld .CIUlla hmr, IIlrcl1d} btat Impluntnud In du Wdtm1atd.Dnmnln, wllIIt plY1blnnlllllodllud wi'" .tomnll/un Ium 1I1mul.11wn Idmt!fllll.Aum "" "",es. 0 1IIr1/n 411'1I ,md till.

IJmtllY IUI:f 11M dull"tldnfltIa flllllth //11I.1 IN Imptld4tllr1 ItDrmMltlr I'Nntf/J.Hydrology and Hydrodynamics (Flooding. Drainage)

Blologlca1lntegrity (FIJh Divenlty, Macro. Cooununlty)Non-<ontact R.eaeation (Sporta FIIhlng)

Supply (Potable Water)Contact Recreation (Swimming)

Aqua-eulture (Shellilah Harvatlng. Food FllhIng)

a

DmrJII11UI /IIIIIIPo""" IInJfunding l/m/tatlont.IJmtlb rrgwllltDlJ'"mandllud J,lUIllnll II4J P"'l"IZIIII.

StaffingScheduling Constralnta

FundinBRegulatory CompUance

Uu 1'II"ld (""tdltIIll",) IlUlUmmt _Db ,mill JtulW 'l"lIIltlt4llll' t«hlllqllll fD IIUIU btullIM mndlUtIIU.

IndlcRtor OJlt!OD8 by RsceMnK Water Use

Concern: HydlOlogy &. Biological Non<lOntact Water Supply Contact Aqua<UltureHydrodynamlCl Integrity . R.eaeation Recn:atlon

Type of PJ!1'ICtJl BIo1tpIl WtItH Qullll!! WlltIr QuI/II!! Blo1ofItltIl BlillogItllIlindicator to be SoeIIIl Wt/Ur Q/lll1I!! Ph)'Slall Blo1DgItltIl WtlUrQuIlIl~ Wl/tIr QIUrl/~Conaldered: Pn1gnun//llltlc SocIIIl BlillogICtJl Sod#l Ph.1'laIl Ph","""

Siu Prognun_tk Sod,II ProgrrunmtItk S«IIIl SodIIlSiu Prognunllllltl& Sif4 ProgratnnIIItk Progrrtm//llltk

5ItI Siu 5ItI

BauJ on baullnl condltlom. rt30Ilrcu. lind comtrlllnU. IUflt:JI1llte gotIufur stonnwatlr n1anagmunt program In tInrU ojn11l1$1lrrWle iIdIllVnnmts.

Example: We want to increase the level and divemity of the flsh population inthe next 5 yean.

To accomplish this, WI! must impro:Ve biologicalIn uall ,and h leal conditions.

Identify prior stDnnwatlr managenunt tJforts lind _, $IIa¥U ojprior tJforts.Identify C\lrm,t storlllwatlr manllgenunt tJforts botll

wttllln the munlclptll boundarla lind thl ltuger watlrsJud.As,as lUcan ofongoing tJforts.

Incorporatl colllpkt"entary progmms lind goalJ.ldentl otmtlaI con lets.

Ana{yze Indicator monitoring raultl.What do the monitoring reaulta indicate about the success of the stormwatel management program?

Have the lndicaton accurately reflected the effectiveness of the management program?What do the lndleaton suggest about the ability of the stormwater

indicator monltorln m to meUUl'C·of overall watenhed health?

Rt-naluau raourt¥l lind constndntl.Updatl (Ifn«eUary) auasmmt ojbaseline conJItions.

lUvllw lind rmu progrllm gotIu.Review and IYIIIst Indicator monitoring progrllnl.

1m lml4nt ,.",lltd mana lilt 11m.

San Francisco Bay Region

Walsh.AvenueCat~me

. Figure 3-2. Stonnwater Environmental Indicator Demonstration Project study areas - Coyote Creek watershed andWalsh Avenue Catchment - and regional hydrologic units.

Table 3-1. Center for Watershed Protection Indicators Tested·

..d u E1Claytor and Brown ....

<II t1# Indicator Name ~ 0

Categories ... >-. lib.~

0 e<u Po.

1 Water quality pollutant constituent monitoring

"2 Toxicity testing

"Water Quality3 Non-point source loadings

"Indicators 4 Exceedance frequencies of water quality standards

"5 Sediment contamination

"6* Human health criteria

7 Stream widening/downcutting

"8 Physical habitat monitoring

"Physical andHydrological 9* Impacted dry weatherflows

Indicators 10 Increased flooding frequency "11 Stream temperature monitoring ..J

12 Fish assemblage ..J

13 Macro-invertebrate assemblage ..J

Biological Indicators 14* Single species indicator

15* Composite indicators

16* Other biological indicators

17 Public attitude surveys

"18 Industrial!commercial pollution prevention ..JSocial Indicators

19 Public involvement and monitoring ..J

20 User perception ..J

21 Number of illicit connections identified/corrected ..J

Programmatic22 Number of BMP's installed, inspected, and maintained ..J

Indicators 23 Permitting and compliance ..J ..J

24 Growth and development ..J

25* BMPperformance monitoringSite Indicators

26 Industrial site compliance monitoring ..J

* Claytor and Brown tndtcators whtch were not tmplemented as part ofthe Stormwater Environmental Indicators Demonstration Project.


approximately 36 percent of the area urbanized,under the jurisdiction of the cities of San Jose andMilpitas.

The Santa Clara Basin is situated betweentwo paxaUe1, northwest-trending mountain rangesand geological faults. The Santa Cruz Mountainsand San Andreas Fault occurs along the western

'edge and the Diablo Range and Hayward andCalaveras Faults on the eastern edge of the basin.Both ranges originated as volcanic sea floor andwere cre:tted by tectonic lifting along the majorfault zones. The exposed rocks of the two rangesare composed of the Franciscan Formation,consisting of silt, shale, sandstone and serpentinerock. 'This formation is highly susceptible tolandslides and produce moderate to high sedimentyields (DWR 1978).

The Santa Clara Valley is a large troughresulting from the tectonic activity and long-termaccumulation of sediments from the adjacentmountain ranges. During the past 30,000 years,the southern portion of. the valley has largely beenshaped by erosive processes acting on the rangesand depositing sediments at the mouths of riversforming alluvial fans. The northern portion of thevalley has been greatly influenced by deposition ofsilt and clays from glacial meltwater andsubsequent rise and fall in sea level.Transportation and deposition of gravels, sands,silts and clay continue to shape the Basin as aresult of gradual erosion as well as episodicevents, such as earthquakes, fires and floods.

The Coyote Creek Watershed is composedof five ecoregion subsections, but is dominated bytwo regions that differ primarily in precipitation,elevation and gradient (Bailey et al. 1994). TheWestem Diablo Range ecoregion is composed ofFranciscan sedimentary, volcanic andmetamorphic rocks that are intensely folded andfaulted. The plant communities are typicallycomposed of grassland, scrub or chaparral habitaton the tops of hills, and oak woodlands in thesteep valleys and canyons. The elevation range ofthe section is between 1000 and 4000 feet abovesea level. The climate ranges from 20 to 30 inchesof mean annual precipitation. The mountains aresteep with narrow canyons, water runoff is rapidand aU but the larger streams are dry throughoutthe summer season.

22

The other predominant ecoregion is theSanta Clara Valley, which is primarily an alluvialplain that was deposited during in the LateQuaternary Period. Elevation within the regionranges from sea level to 1000 feet. Thepredominant natural plant communities are valleyoak and annual grasslands. The climate rangesfrom 12-20 inches of mean annual precipitation.Fluvial erosion and deposition are the majorgeomorphic processes that occur in this region.The alluvial plain slopes gently and has a neatlylevel floodplain with alluvial fans. Runoff is rapid,and most streams are dry in the summer season.Coyote Creek, however, is sustained by groundwater and by regulated flows from two permanentdam-reservoir systems (see below), and thus flowsyear-round.

The Santa Clara Basin ground water systemis characterized by two interconnected subbasins.The larger of tile' two subbasins (the northernSanta Claxa Valley) is more important with respectto local water supply, and is composed of twodistinct geological regions. The ' permeable,unconsolidated region, located north of theCoyote Narrows (Figure 3-3), is composed ofalluvial fill that allows surface water to infiltrateand recharge the under ground aquifer. Betweenthe recharge zone and the South Bay is theunconsolidated region, which is divided verticallyinto two major water bearing zones. These twozones are located above and below a thick layer ofclay, or aquiclude, which prevents ground watermovement and exchange between the two zones.The Coyote Valley subbasin is also filled withalluvial sediments, and connects to the Santa Clarasubbasin to the north through the CoyoteNarrows. During the 1920's and 30's, pumping ofgroundwater for urban and agricultural usesresulted in significant land subsidence in the SantaClara Basin. To protect the underground aquiferand maintain a water supply to the Santa ClaraValley, dams and water diversions were built toprovide an alternative' water source to the overpumped aquifer. The Santa Claxa Valley WaterDistrict (SCVWD) currently promotes groundwater recharge by managing 393 acres ofpercolation ponds throughout the County.

As is typical of the majority of rivers andstreams in California and the Western U.S., the

·':0.

Santa Clara Valley Revised 1/25/00Urban Runoff Pollution Prevention Program

Legend

~ Historical MineDamo Percolation~ Reservoiro Seasonalo Seasonal Diversion@ Percolation Pondo Stormpipe Outfall

N Coyote CanalN Modified ChannelN Coyote Creek MainstemN Surface Waterbody

Streets and Highwayso Coyote Creek Watershed

Groundwater Recharge Potential. Consolidated

I~ BedrockConfinedUnconsolidated: Coyote ValleyNA: UplandsUnconsolidated: Santa Clara Valley

Figure 3-3. Geology, Hydrology and Land Use Features of Coyote Creek Watershed, Santa Clara County.


hydrology and geomorphology of Coyote Creekalong the valley floor has been higWy modified.At the base of the Diablo Range, the Creek isimpounded by two dams, which form Coyote andAnderson Reservoirs. Coyote Dam was built in1936 and its reservoir has a capacity of 22,925acre-feet. Two miles downstream the creekempties into Anderson Reservoir, which was builtin 1950 and has a capacity of 89,073 acre-feet.Streamflow from both dams is regulated betweenApril and October. and runoff above Coyote Damaccounts for about 75 percent of the total runofffor the entire Anderson/Coyote watershed(Iwamura 1999). Nine tributaries drain to the tworeservoirs and transport large amounts ofsediment; however the dams effectively reduce theamount of sediment transported downstream.Management of flows released from the damshave also reduced peak flows and increasedsummer flows for groundwater recharge.

About 0.5 mile below Anderson Dam, wateris diverted (April - October) by the Coyote CreekDiversion Dam into a concrete channel (CoyoteCanal) that bypasses the natural channel. Water isreintroduced to the natural channel approximatelysix miles downstream at the Coyote Narrows, justupstream of the Coyote Percolation Ponds. Suchdiversion dries the natural channel during thesummer months. A fish screen was installed in1999 to prevent downstream passage of fish intothe Coyote Canal.

Between Anderson Dam and the Creekmouth, three major percolation pond systems arelocated within or adjacent to Coyote Creek. Thepercolation ponds, located two miles belowAnderson Dam in Santa Clara County Parkproperty, were historic gravel quarry pits. Theseponds occur off the natural channel, but connectto the creek during high flows through a breach ina levee. The Coyote Percolation Ponds, justdownstream of Coyote Narrows, were originallypits created by gravel mining in the naturalchannel and are now managed by the SCVWD asa ground water recharge system. A permanentconcrete dam was built in the 1930's to increasethe size of these ponds Ooe Aguilera, SCVWD,personal communication). In 1999, a fish ladderwas constructed to allow passage over the dam.Approximately 1.5 miles farther downstream arethe Ford Percolation Ponds, three separate ponds

24

in the natural channel created by seasonal spreaderdams. These dams were removed4 in 1997 andCoyote Creek now flows unimpeded through thehistoric pond area.

Ten sand and gravel mines were operatedhistorically on Coyote Creek below AndersonDam. Evidence of the mining operations includepresence of gravel pits and exposed river floodplains, resulting from the removal of riparianvegetation. Instream gravel pits cause streamdegradation typical of gravel extraction, e.g. bankerosion, and changes in stream elevation andmorphology, that negatively impact fisheries andtheir habitats (Kondolf 1994). Increased sedimentdeposition in some sections of the Coyote Creek.below Anderson Dam is an indication that bankerosion continues to be a problem (Ken Reiller,SCVWD, pers corom, 1999).

The boundary between the mountains of theDiablo Range and the alluvial plain that forms thevalley floor is sharply defined. At least four majortributaries flow from the mountains across thisalluvial plain to Coyote Creek. Sixty-eight stormdrain outfalls from the cities of Morgan Hill,Milpitas and San Jose also contribute flow toreaches of Coyote Creek below Anderson Dam.The runoff from the City of Morgan Hill is mostlytransported from an adjacent watershed.

Much of the riparian corridor belowAnderson Dam is intact. Orchards, farmlandsand urban development have replaced the originalriparian vegetation that occurred in the highterraces of the channel. The middle terrace hasmanaged to survive, dominated by cottonwoods,with few remaining oak and sycamore trees.Much of this riparian corridor is managed by theSanta Clara County Parks and receives somerecreational use. The lower Coyote Creek isconsidered to be one of the highest qualityriparian corridors remaining in the southern SanFrancisco Bay region (U.S. Attny Corps ofEngineers 1986).

The urbanized area of Coyote Creekwatershed, has dramatically increased since the1960's. During this time, population hasincreased greatly, and agricultural and grazing landhave been converted to residential communities in

4 Reinstallation is contingent upon permit approval bythe CDFG.

the southern region of the Santa Clara Valley, andalong the base of the Western Diablo range.

The lower reaches of Coyote Creek havebeen partially modified for flood protection.Setback levees and a high bypass channel havebeen constructed in the lowest section of CoyoteCreek. In addition, several miles of tributarystream channels have been similarly modified,including the lower portions of Upper and LowerPenitencia, Berryessa, Lower and Upper SilverCreeks.

As Coyote Creek nears the South Bay atransition occurs from a freshwater environmentto an estuarine environment where the channeland adjacent baylands contain many acres ofbrackish marsh, salt marsh and mudflats.Originally, an earthen dam was constructed toprevent saltwater intrusion into agricultural lands.Recendy (1995), the SCVWD installed areplacement steel dam just upstream of theoriginal dam site. Difficulty with dam operationhas precluded installation since 1997. A study isbeing conducted (1998 - 2000) to assess itsimpact and viability of continued operation GaeAbel, SCVWD, personal communication, 1999).The reach of Coyote Creek downstream ofStandish Dam receives fresh water dischargedfrom the San Jose-Santa Clara Water PollutionControl Plant

3.1.2 Walsh Avenue CatchmentThe Walsh Avenue Catchment is a 28-acre

industrial area located on the north and southsides of Walsh Avenue between the SouthernPacific Railroad lines and Lafayette Street in theeastern portion of the City of Santa Clara.Currendy, 32 industrial and commercialbusinesses operate in the catchment area.Activities include metal plating, high-techequipment assembly, parts distribution, andwarehouse storage. Two businesses havesubmitted a Notice of Intent to the CaliforniaState Water Resources Control Board underCalifornia's General Industrial StormwaterNPDES permit (Figure 3-4). These twobusinesses also maintain pretreatment permits forindustrial sewage discharge to the San Jose/SantaClara Water Pollution Control Plant. Threebusinesses formerly covered under the statewide

CHAPTER THREE -METHODS

permit have terminated coverage, either becausethey ceased specific operations covered under thepermit or because they have closed the business.

All surface drainage from the catchmentflows to a stormdrain inlet located at the end ofWalsh Avenue (Figure 3-5) and discharges to theGuadalupe River. 1ms inlet has served as a waterquality monitoring station since 1988.

The catchment was selected as a study areafor this project because it contained onlyindustrial businesses and drained to a single stormdrain inlet. In addition, including historic waterquality sampling data was available, and theProgram had a history of outreach andinvolvement in this area.

History of Program Involvement in theCatchment

In 1992-93 the Program and the City of SantaClara implemented a pilot project in theCatchment to reduce loads of zinc, copper, leadand other pollutants in stormwater dischargethrough a comprehensive inspection programdesigned to identify pollutant sources and toprovide assistance to facility owner/operators ofsource control measures and BMPs. TheCatchment was selected for the pilot because ofits small size and because of high levels of zinc,copper, and lead found in runoff from theCatchment (Woodward-Clyde 1993).A secondary objective of the 1992-93 WalshAvenue Pilot Inspection Program was to developgeneric industrial inspection guidelines that couldbe applied by other Program participants to locateand control important industrial sources of stormwater pollution within their jurisdictions. Theseinspection ,guidelines included hands-on trainingfor inspectors, the development of writtenprocedures and forms, and the prioritization offacilities for inspection. Over the course of thispilot study, 23 facilities were inspected. Eleven ofthese 23 facilities warranted a reinspection due toproblems that were identified to likely contributepollutants to stormwater. Eighteen of the currentbusinesses in the Catchment also participated inthe 1993 pilot study.

25

Industrial NOI Filer Statusin the

Walsh Avenue catchment

NOI FUer Status_Activeo Non NOI Filer_ Terminated. 1992~ Storm Drain InletIIII Walsh Avenue Catchmento Ownership Parcels

s

Revised 3/31/99 SCVURPPP

Figure 3-4. Industrial facilities within the Walsh Avenue catchment illustrated by NOI filer status.

6.

5.

In addition to the pilot inspection program, theProgram has conducted routine sampling andanalysis of runoff from this Catchment since1988. Monitoring has involved monitoring offlow, metals, performing whole effluent toxicitytesting, and conducting Toxicity IdentificationEvaluations (TIEs). Stormwater monitoringinvolved installation of an automated flowmonitor and a water quality sampler programmedto collect flow-weighted, composite samples.

Background on the City of Santa Clara'sIndustrial/Commercial Stormwater Inspections

The City of Santa Clara adopted the Program'smodel Performance Standard forIndustrial/Commercial Discharger Control(IND), but made minor modifications to tailor themodel to the specific types of business within theCity. The model Performance Standard andsupporting documents provide for:

=> Inspections of industries which have filed aNotice of Intent (NO!) to be covered underthe SWRCB statewide NPDES permit forstormwater discharges associated withindustrial activities;

=> Investigation of other facilities that areidentified within selected Standard IndustrialClassification (SIC) codes;

=> Inspections of selected commercial facilities;

=> Distribution of information onindustrial/commercial Best ManagementPractices;

=> Action, under local authority, on allviolations of local municipal ordinances; and

=> Referral to the Regional Board of anysignificant problems which cannot beaddressed promptly and fully under localauthority.

The cities within SCVURPPP tailor theorganization and implementation of theirindustrial inspections based on the number offacilities to be inspected (i.e., the size of the city),funding resources, and available staff. Forexample, the City of San Jose (a much largermunicipality adjacent to Santa Clara) employs staffdedicated solely to urban runoff pollution-


prevention inspections. The City of Sunnyvale(also adjacent to Santa Clara) operates its ownsewage treatment plant, and has combinedstormwater and wastewater pretreatmentinspections into a single unit for consistentimplementation of discharge regulations andcommunication with industrial businesses.The City of Santa Clara coordinates a UnifiedProgram for inspections under California'smandated Certified Unified Program Agency(CUPA) law. The Unified Program is a State andlocal effort to consolidate, coordinate, and makeconsistent six existing programs regulatinghazardous waste and hazardous materialsmanagement. The six elements of the UnifiedProgram are:

1. Hazardous waste generators andonsite treatment of hazardous wastes

2. Spill prevention control and countermeasure plans for above-groundstorage tanks

3. Underground storage tanks

4. Hazardous material release responseplans and inventory

Risk management and prevention

Uniform Fire Code HazardousMaterials Management Plans andInventories

Under CUPA, a single local agency is responsiblefor the elements of the Unified Program within itsjurisdiction. Counties, cities or local agencies maybecome certified as Participating Agencies (PA) toimplement one or more of the six programelements within the PA's jurisdiction. The City ofSanta Clara's Fire Department is a PA.The Fire Department conducts annual inspectionsof industries and commercial establishmentsthroughout the City. At a minimum, facilities thathave filed an NOI are to be inspected at leastevery 3 years.Inspections are documented using a NPS InspectionViolation Notice. The City reported thatdocumentation of violations is kept on file at theFire Department (in some cases, also at the StreetDepartment/Storm Drain Maintenance Division).

27


. 3.2 Project Approach in Coyote Creek

In Coyote Creek, physical, hydrological,water-quality, and biological indicators were usedto conduct three types of comparisons:

=> Temporal comparisons in the upstreamreference areas, where land uses have notchanged recently.

=> Temporal comparisons at the stations inCoyote Creek that have transitioned fromrural to urban.

=> Spatial. comparisons between the upstreamreference stations and the downstream urbanstations.

Baseline data for this project were providedby a previous EPA-sponsored study (pitt andBozeman 1982) on the sources and effects ofurban runoff in Coyote Creek. The study,conducted from March 1977 to August 1980, wasintended to:

=> identify and describe important sources ofurban runoff pollutants

=> describe the effects of these pollutants onwater quality, sediment quality, aquaticorganisms, and the creek's associatedbeneficial uses

=> assess potential measures for controlling theproblem pollutants in urban runoff.

Pitt and Bozeman sampled locations within a16 mile "urban" area extending upstream from theconfluence of Silver Creek, and within a "nonurban" area located upstream of the urban reachbut downstream of Anderson Reservoir. Thefollowing parameters were examined:

=> basic hydrologic condition

=> water quality (including runoff and .receivingwater)

=> sediment properties and quality

=> general habitat characteristics

=> aquatic biology (including fish, benthic·organisms, attached algae, and rooted aquaticvegetation)

The study concluded that water quality,sediment and biological conditions in Coyote

28

Creek were generally degraded as a result ofurbanization. Where sampling design andmethodology were comparable, we used datacollected from the 1977-1980 study for temporalcompansons.

We collected field data from a total of 21locations along Coyote Creek (Figure 3-5).Fisheries were sampled and physical habitatassessed at 18 tOO-meter reaches) during June,July, and September 1999. At nine of these 18locations, we sampled macroinvertebrates (Mayand June 1999) and surficial sediments Qune andOctober 1999). During this period, wecontinuously monitored water temperature,dissolved oxygen, pH, and conductivity at fivelocations (two of which coincided with thefisheries and physical habitat sampling locations).All sampling stations were georeferenced in ageographic information system (GIS) (ArcView,Environmental Syste~ns Research Institute) usinggeographic coordinates obtained using a globalpositioning system (Trimble GPS Pathfinder ProXL).

Our sampling sites were a subset of the 40locations sampled by Pitt and Bozeman. Pitt andBozeman classified sites as either urban or nonurban depending on the land use composition inthe drainage areas. We modified theirclassification to include urban, rural, transition(i.e.,.in transition from predominantly rural landuses such as open space or low-density residentialthat existed in t980 to more densely developedurban land uses by 1999), and reference (e.g.,least-impacted) classes. Our transition, rural, andreference sites corresponded to their non-urbansites. We sampled eight urban sites, threetransition sites, six .rural sites, and two referencesites (Figure 3-5).

Selection of reference sites was based uponfinding locations that were minimally impacted byurbanization and representative of naturalconditions in the watershed. We used thefollowing criteria:

=> Extensive, natural, riparian vegetation,representative of the .region;

=> Representative diversity of substratematerials;

South San

Legend

SBnta Clara Valley Revised 1/17100thb.... Rum" Pollution Prevention P"'9"'m

SEtDP Sampling Stationso Urban

Transition.. Rural

ReferenceI/\I Coyote Creek Mainstem1\/ Surface Waterbodies

l 0 Coyote Creek Watershed

N' ~ClO i~ '" 0 :I

~ CICl

~.... ,.

0~ ..0 ..

~.;:8 ~

.D ....... ....,. ~ u os

.~u t: a ~1'01 ::c ~0 .... ,. ll')

'" a:l -; Cl Cl .. g:u.~ ·0 ~ u

0 ~ 8 Q., ....'"

..8 r};u t: .c >. ~ :.a.. '" .c ol u 00

00 c;: u:: Q., ~u

1-' a-Name '" ....

U-1 Trimble U 0 0

S-4 Montague 0

UpperU-2 Peniteneia/ U 0 0

Berrvessa

U-3Mouth of

USilver Creek

0 0

U-4 William U 0 0 0

U-S Derbe U 0 0 .. 0

TS-1 0

U-6 Senter U 0 0 0

U-7 Sylvandale U 0 0 0 0

T-1 Crosslees R 0 0 .. ..T-2 Tennant R 0 0

T-3 Metcalfe R 0 .. 0 0

R-1 Coyote R 0 0

R-2 Riverside R 0 0

R-3 Miramonte R .. 0 0 0

R-4 ,Burnett R 0 ..R-S Cochran R 0 0 .. 0 0

R-6 Mile 0266 .. 0

Ref-1 66 Mustang * .. .. 0

TS-S 0

Ref-2Gilroy Hot

R 0 0 .. tSprings

Figure 3-5. Sampling Sites. U = urban T = transition R = rural Ref = reference. Also shown (in the table) are names used to refer to the sites. Sites sampled by Pitt and Bozeman(1982) are noted by their designation U= Urban R= Rural.The table shows the fonowing additional sites in their relative locations:S-4 = Water Quality Sampling Site 1988-95TS = Sites for additional 1999 monitoring (temperature, dissolved oxygen, pH, conductivity).*In September station Ref-1 was dry, and no data were collectedtDue to the lack ofwater at Ref-2 during the October sediment survey,sediments were sampled at a site 25 miles downstream.


=:> Natural channel structures of the region (i.e.pools, riffles, runs, backwaters, glides);

=:> Natural hydrograph;

=:> Undisturbed banks, and banks that providecover;

=:> Natural water color and odor;

=:> Presence of representative animals, birds,mammals, amphibians and reptiles (Gibsonet. al., undated).

We did not identify sites that met thesecriteria and existed within the same ecoregionsubsection (Bailey et al. 1994) as the urban,transition, or rural sites. Although many riversand large streams exist in the ecoregion subsectionwhere all urban, transition, and all but one ruralsite are located (Santa Clara Valley Subsection,261Ae), they are similarly influenced byurbanization. The two reference sites in this studydo represent least-impacted conditions in thewatershed, but their location in the upperwatershed means that they do not fully representthe natural conditions of the downstreamsampling sites.

Reference conditions are difficult to establishin highly urbanized regions. In the San FranciscoBasin, the majority of the basin floor and foothillshave been developed, leaving only headwaterreaches relatively unimpacted by direct, urbaninfluences. The basin is also characterized bysignificant natural variation within short distancesdue to the diversity of physiography andmicroclimate. Both sets of circumstances make itdifficult to establish reference conditions withinthis region. The California Department of Fishand Game (CDF&G) has proposed to establishreference conditions using macroinvertebrates infive regions of California (Harrington et al. 1999).Under this proposal, the entire SF Bay Area iscontained in the Central Coast Region.

3.2.1 Physical and Hydrological Indicators

Stream Widening and Downcutting

Channel widening and downcutting has beensuggested as a potential indicator for detectingchanges in the magnitude and frequency ofstormflows (Claytor and Brown, 1996). Changes

30

in stream geometry can be documented over timeby measuring channel cross-sections at fixedlocations, bankfull widths and depths withinrepresentative reach types, or the percent bankscour over specified lengths of channel. Claytor·and Brown suggest that measurements must beconducted over a period of time that reflectschanges in upstream land use.

Our approach included the following steps:

=:> Develop a stream classification to characterizethe condition and geomorphologic processesthat occur in Coyote Creek and to identifyoptimal locations to apply this indicator.

=:> Evaluate the cross section data and aerialphotos at representative reaches of the creekto determine if temporal changes to channelgeometry have occurred at each section.

=:> If channel widening or downcutting isdetected, determine if channel changes can becorrelated with urbanization.

We obtained digital stream channel andwatershed information for Coyote Creek (Table 32) from a variety of sources and compiled it usinga Geographic Information System (GIS) database(ArcView, Environmental Systems ResearchInstitute)

We created a stream gradient and a. longitudinal profile of Coyote Creek, from itsmouth at Dixon Landing Rd to the headwaters ofits Middle Fork Coyote Creek, using the GIS. Weused United States Geological Survey (USGS) 7.5minute topographic digital quadrangle maps toidentify elevations where contour lines crossedCoyote Creek, then intersected these points withthe USGS 24,000 scale hydrography layer. Wecalculated percent slope by dividing the length ofeach stream segment between elevation points bythe respective change in elevation.

We classified distinct river teaches, usinginformation on the geology, hydrology, land useand channel characteristics, to describe thegeomorphic form and function of the CoyoteCreek mainstem. The relative contribution ofthese factors varied according to the positionalong the creek. For example, gradient andplanform was more important in the headwaters;geology, hydrology and channel width mostimportant in the middle sections below dam; and

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LEGEND

Coyote CreekWatershed Boundary

o Flow Gage

(L!.__R_a_i_n_G_a_g_e -'

i.

.',

. ", ~.,... "\

URS Orelner Woodward Clyde

Project No.51981183NA Coyote Creek

LOCATION OF RAIN AND FLOW GAGES Figure3-6

51981183NAOO-03000/021799/aas


Table 3-2. Data sources used to develop an accurate land use base map for the Coyote Creek watershed.

Data Source Scale SDatial Extent OwnerCounty Assessor Parcel 1:500 Covote Watershed Santa Clara CountyProtected Lands 1:24,000 Covote Watershed Green Info NetworkVacant Land Inventory 1:500 City of San Jose Jurisdiction City ofSan-'oseEasements 1:500 Santa Clara Basin Santa Clara Valley Water DistrictWater Ways Management Model 1:12,000 Santa Clara Basin Santa Clara Valley Water District1995 Land Uses 1:24,000 Coyote Watershed Association of Bav Area Governments1998 DiWtal Orthophotos 1 meter City of Sao Jose lurisdiction City of San loseDigital USGS Topographic 1:24,000 Coyote Watershed United States Geological SurveyIQuadrangles

channel type and land use most important in thelower creek and tidal area.

We reviewed existing channel cross-sectiondata at the Santa Clara Valley Water District(SCVWD) Survey and Hydrology Departments.We searched for historical cross section data thatwere repeatedly surveyed at the same locationover a time interval that would represent differentstages of urbanization. Preliminary research at theSCVWD indicated a scarcity of data for CoyoteCreek, so we broadened our search to includeUpper Penitencia Creek, a tributary to CoyoteCreek, which had been recently surveyed by theSCVWD. We investigated the feasibility ofresurveying historical cross sections for bothcreeks, to conduct new surveys when current datadid not exist. We also evaluated digital crosssection data, developed by the SCVWD for HEC2models, as an alternative source of channelmorphology information. We obtained streamdischarge measurements at two flow gaugeslocated at Coyote Creek at Edenvale and UpperPenitencia Creek at Piedmont Avenue. Inaddition, we reviewed survey files and conductedinterviews with SCVWD hydrologists to trackinformation on bankfull widths and depthmeasurements taken at representative reachesover time.

We reviewed existing aerial photographs atthe SCVWD Survey Department to determine ifthe resolution and frequency of these photos weresuitable for measuring temporal changes inchannel morphology. Specifically, we searched forphotos along Coyote and Upper Perutencia Creek,from which we could measure temporal changesin channel widths and active channel area (areas of

the channel lacking established vegetation) andcalculate the percentage of channel-bank scourover time.

Physical Habitat Monitoring

Claytor and Brown (1996) suggest thatphysical habitat evaluations be conducted todetermine the potential of water bodies to sustainaquatically healthy systems. By examining thecondition of aquatic habitats, it may be possible todetermine whether available habitat or waterquality are limiting aquatic biological health.

We used a modified version of a CaliforniaDepartment of Fish and Game (CDFG) habitattyping protocol (Flosi et al. 1998) to characterizehabitat at the 100m reaches where we sampledfish and macroinvertebrates. Gradient (percentslope) and stream flow (cubic feet per second)were also measured. Homogeneous areas ofhabitat greater than or equal to one wettedchannel width constituted separate habitat unitsand were typed to a Level IV typing designation(Flosi et al. 1998). The following creek attributeswere characterized: habitat unit dimensions(mean length, width and depth), instream shelter,substrate composition and embeddedness,riparian canopy cover, bank composition andvegetation, maximum depth, pool tail crest depth,and pool tail substrate composition.

GradientGradient (percent slope) was measured

between flag markers using a survey level, stadiarod, and hip chain (Harrelson et al. 1994).Percent slope was calculated by dividing themeasured distance between the upstream and

31

STORMWATER ENVIRONMENTAL INDICATORS DEMdNSTRATION PROJECT

downstream reach marker flags by elevationdifference. Elevation and percent gradient werealso estimated for the entire creek using a GIS toanalyze channel morphology (see methods appliedfo! the Stream Widening and DowncuttingIndicator) and used to support and verify fieldmeasurements.

Smam FlowFlow was measured at each reach using a

bucket wheel current meter (Model 1205 ~'mini"

meter, Scientific Instruments, Milwaukee,Wisconsin) attached to a wading rod. Totaldischarge was calculated using a standard 6/10method at 8 - 10 points across a transect line.

.Scientific Instruments guidelines for datacollection and total discharge calculation methodswere followed. A single flow measurement wastaken following habitat typing of each teach over athree-week period in late June - early July.Sampling did not account for temporal variationsin flow during this three-week period. CoyoteCreek flows vary with discharge at AndersonDam. Our estimates of flow are not necessarilyrepresentative of flow conditions at other timesduring late June and early July, or in May andSeptember when fish and macroinvettebrateswere sampled. They do, however, reflectconditions at the time each reach was surveyed forhabitat.

Habitat Unit MeasurementsHabitat units were numbered

chronologically, beginning at the downstream endof each reach. Side channels were noted accordingto the habitat unit from which they split and werealso typed completely. Where a habitat unitextended beyond the boundaries of the designatedreach, the entire length of the habitat unit wasincluded and typed if the majority of the unit (bylength) fell within the reach. This resulted insome variation among total lengths of reaches. Indry creek sections, only length was measured.

Habitat TypeRiffle, flatwater, and pool habitat units were

classified to the finest resolution in the protocol(Level IV) as riffles (by gradient), flatwater (bydepth and velocity), or pools ( by location in thestream channel and cause of formation, e.g.,

·32

obstruction, blockage, constriction, or mergingflows).

Habitat Unit DimensionsFor each habitat unit, mean thalweg length

was measured (ft) with a hip chain and thread.Mean width and depth was measured using astadia rod. For habitat units designated as pools,maximum depth and depth at the pool tail crestwere measured to estimate the pool's residual

, volume. The dominant pool tail substrate wasalso recorded (see substrate section forcomposition categories and size breakdown).

Instream ShelterFlosi et al. (1998) rate instream shelter (0 - 3)

based on habitat needs for salmonids. The percentof instream shelter was estimated by viewing thehabitat unit from the upstream and downstreamends (estimating instream shelter from anoverhead view, as suggested in the protoco~ wasnot feasible in many areas due to steep banks and

, vegetation (e.g., poison oak)). The percentage ofthe following shelter types were estimated:undercut banks, small woody debris (diameter <12"), large woody debris (diameter > 12"),artificial debris (e.g. tires, shopping carts, autoparts, garbage), root masses, terrestrial and aquaticvegetation, surface turbulence, and boulders (d >10").

Turbidity and depth as cover were alsoestimated based on the following criteria: noneclear (substrate clearly visible) and/or shallowwater « 1ft); low - slighdy turbid (substratevisible) and/or depth >1ft.; moderate - cloudywater (substrate visible in limited areas) and/ordepth > 2 - 3 ft; high - very cloudy water(substrate not visible), and/or depth > 3 - 4 ft.

i These data were meant only to provide qualitativei estimates of instream shelter, and were not meant: to quantify column turbidity or suspendedI sediment.i

: SubstrateHabitat unit substrate composition was

classified as silt/clay, sand (diameter < 0.08"),gravel (d = 0.08 - 2.5"), small cobble (d = 2.5

I5"), large cobble (d =5 - 10"), boulder (d > 10"),i and/or bedrock. One hundred percent of the: substrate was classified into these categories by!visual estimation. Where visibility was difficulti

due to turbidity or depth, substrate was estimatedby feel, using a stadia rod where necessary.Percent of substrate exposed was also measuredby visual estimate.

Substrate embeddedness was estimated asthe shiny, buried portion versus the duller,exposed portion of substrate between 0.08 and 10inches in diameter (gravel - large cobble). Threeto five samples of substrate were taken randomlywithin the habitat unit and averaged for anestimate of substrate embeddedness. Wheresilt/sand was the dominant substrate,embeddedness was noted as 100 and laterremoved from the overall reach analysis. Thus,calculations of mean reach embeddedness includeonly substrates generally suitable to spawning,rather than indicating mean embeddedness ofsubstrate within the entire reach.

Riparian CanopyCanopy cover was measured using a convex

densiometer at the center of each habitat unit.The percentage of horizon vegetation canopyconsisting of deciduous and non-deciduous(including woody parts of deciduous vegetation)cover was measured from both an upstream anddownstream view and averaged (% deciduous +% non-deciduous =% total canopy cover).

Bank Composition and VegetationFor each habitat unit, right and left bank

(downstream facing convention) composition wasobserved at the bankfull discharge level andcategorized as follows: bedrock, boulder,cobble/gravel, or silt/sandiclay. Dominant bankvegetation from bankfull discharge level to 20 feetupslope was recorded as either grass/herbs/forbs,brush, deciduous trees, non-deciduous trees, novegetation, and percent of bank vegetation wasestimated.

Increased Flood Frequency

To evaluate the relationship betweenflooding frequency and urbanization, we collecteddata for historic and current land development,precipitation, streamflow, and flood improvementprojects.

Urbanized area was estimated using aplanimeter to measure the edge of developed areasfrom historic US Geological Survey (USGS)


topographic maps (dated 1895, 1939, 1953, 1955,1961, 1978, and 1980) and a current street map.Developed areas were defined as those havinghigh road-density of roads, which resulted inincluding small portions of undeveloped land.Current urbanized area was estimated based onurban land use data (ABAG 1995).

Flow data were obtained for three gages inthe watershed; Coyote Creek at Edenvale, CoyoteCreek at Station S4 (from the Santa Clara ValleyNon-Point Source Control Program, SCVNPS,monitoring program) and Upper Penitencia Creekat Dora! Drive (Figure 3-6). The data werecomprised of mean daily flow and daily peak flowin cubic feet per second (efs). Rainfall data wereobtained from three gages; San Jose, Mt.Hamilton and Anderson Reservoir (Figure 3-6),and included hourly values at the San Jose gageand daily volume in inches at the other gages.Notably, the Edenvale and the Upper PenitenciaCreek flow gages are located upstream of most ofthe urbanized area. Only eight years of flow datawere available below the urbanized area drainingto Coyote Creek.

Flood frequency, location, and extent wasanalyzed by comparing historic documents offlood improvement studies and flood damagereports that were related to Silver, Penitencia,Coyote, and Berryessa Creeks. The number ofstorms exceeding channel capacity wassummarized.

The influence of urbanized area on floodfrequency was examined by comparing changes inurbanized area, including the construction ofAnderson Dam, to flood, flow, and rainfallrecords (National Climatic Data Center and theSCVWD).

33

I


Stream Temperature Monitoring

We used Yellow Springs Instruments (YSI)600 XLM Sondes to monitor temperature, andother water quality parameters (dissolved oxygen(DO), pH, and conductivity) at five locations onCoyote Creek (Figure 3-5). Locations wereselected to represent the urbanization gradientalong Coyote Creek and water quality conditionsat benthic macroinvertebrate sampling sites.Specific locations were chosen bas~d on field crewsafety, accessibility, and equipment security. Weselected YSI 600 XLM Sondes for this projectbecause they can record and store multiple waterquality parameters and can be deployed for longtime periods with minimal maintenance (fable 33).

Prior to deployment, all Sondes wereprepared, maintained and calibrated according tothe 600 XLM Operations manual. Upon firstdeployment, Sondes were set to sample at 15minute intervals. Subsequendy they were reset tosample at 3D-minute intervals, to accommodate alonger duration of unattended sampling due toincreased battery life.

Upon deployment, the Sondes were placed in24-in long, 2-in diameter slotted PVC well casingsthat were capped on either end with PVC endcaps perforated with holes. Sondes were securedat site locations using cable ties to preventmovement within the casing, and to allow water tomove freely past the probes while protecting the

Table 3-3: Sonde SpecificationsOptrating Bnvironmm1Medium: fresh, sea, or polluted waterTemperature: -5 to +45 oCDepth: 0 to 200 feet (61 meters)Storage Temperature:-40 to + 60 °C for sonde and all sensors except pH andpH/ORP·20 to + 60°C for pH and pH/ORP sensors.MaI,nal: PVC, Stainless SteelDiameter: 1.7 inches (4.4 em)Length: 21.3 inches (54.1 em)Weight: 1.48 pounds (0.67 kg) with batteriesCompll1" In1n:fatt: RS-232, SDI-12Internal Logging Memory Size: 384 Kilobytes (150,000individual parameter reaclings)POlllit': 4 AA-size Alkaline Batteries or External 12 VDCBa11ttyUjt: 25 to 30 days at 20 deg. C with a 15-minutelogging interval and a 4O-minute dissolved oxygen warmup.

34

Sonde from physical damage. Sonde casings weresecured lengthwise to a cinder block (16 in x 8 inx 8 in) using a stainless steel hose clamp and twoplastic cable ties to keep the probes from cominginto contact with the creek bottom. This setupallowed the Sonde to extend over the edge of thecinder block approximately four inches on eitherend. For security reasons the cinder block wastethered and locked to a fixed object (exposed treeroots, trees, etc.) on shore. The Sonde waspositioned on the substrate perpendicular to thebank so that its sensors extended toward thecenter of the creek.

Field maintenance of the probes wasscheduled every two weeks for the duration of asampling event The primary purpose of thismaintenance was to ensure that the Sondes werenot accumulating sediment and algae on thesensors. Maintenance procedures consisted ofextracting the Sonde from its housing, visuallyinspecting the sensors and Sonde for damage oraccumulation of debris and algae, rinsing thesensors in clean water to shake loose any algaeand/or silt that may have accumulated in oraround the probes, brushing and cleaning theslotted PVC wen casing, reinserting the Sondeinto the casing and re-deploying it.

At the end of each sampling period, theSondes were extracted from their housings, anddata were downloaded immediately in the fieldusing a portable computer. When this was notpossible, data were downloaded upon returning tothe laboratory. All Sondes were placed in clean

· water during transport to the laboratory to keepi the probes hydrated. Once data were downloaded: and backed up, the data were reviewed toI determine if any significant problems had, occurred during the deploymentI Once data were downloaded from the· Sondes, the probes were checked for· measurement accuracy. These QA/QC checks\ consisted of run$g the Sonde in real time: sampling mode using known calibration standards

"; for each parameter. Any difference between the: actual measurement and the known standard wasi recorded as drift Calibration standards were the! following: pH 7.0 and pH 10.0 buffer solutions;specific conductance, lmS/em conductivitysolution (typical of levels measured in the lower

reaches of Coyote Creek); DO was based uponwater-saturated air. DO calibration involvedfilling a cup with 0.125 inches of water, andattaching it to the probe so that the sensor wasenclosed but still vented to the atmosphere. Theprobe was allowed to sit for 10 minutes to allowthe air to become water saturated and thetemperature to equilibrate. Percent DO was thenrecorded and compared to pre-deployment valuesfor each parameter.

During all phases of this project, samplingrelated activities were recorded in a single binder.Documentation included logs for calibration,dissolved oxygen membrane replacement, anddeployment drift measurements, maps,coordinates and sketches of monitoring stationsites, photographs, and miscellaneous notes.

We calculated minimum, maximum, andmean daily temperature for each station.Temperatures recorded on dates of Sondedeployment and retrieval were typically notincluded in the analysis since they did not recordwhen daily minima and maxima were likely tooccur. Minima and maxima were also plottedagainst daily air temperature maxima and minimaobtained from two weather stations (San Jose andMorgan Hill). We also correlated the dailytemperature range with sampling date.

Riparian canopy distribution at, andupstream of, each station was recorded andassessed together with observations made duringhabitat typing. Finally, temperature stations weremapped along with known percolation ponds,reservoirs, and tributaries to help identify factorscontributing to differences between stations.Historical temperature data sources wereidentified through a literature search and bycontacting agencies and professionals involved inthe Coyote Creek watershed, including the SantaClara Valley Water District (SCVWD), USEnvironmental Protection Agency (USEPA),United States Geological Survey (USGS), San JoseState University (SJSu), and the Coyote CreekRiparian Station (no longer in operation, but dataavailable through SCVWD).


3.2.2 WaterQuali!) Indicators

Water Quality Pollutant ConstituentMonitoring

We examined the usefulness of water qualitymonitoring data collected 1988-1995 to detecttrends in concentrations of constituents instonnwater runoff and in exceedance frequenciesof water quality standards.

Detecting Trends in WaterQuality ConstituentsTrend analysis can be approached in several

ways. A simple approach is the time-trend analysiswhere data are plotted with date sampled alongthe x-axis and concentrations along the y-axis. Alinear regression can be fitted to the data todetermine if a significant time trend is present.The difficulty with using runoff water quality datain trend analysis is its high variability. Figure 3-7shows the variability of copper, lead, nickel, zincand total suspended solids (TSS) relative to theirmean value. On the figure a concentration of 1.0is the mean concentration; a concentration of 2.0would be twice the mean concentration, etc. It isnot unusual to measure concentrations that aretwo, three, or more times the mean concentration.Without being able to explain some of this

16

5I ···0·················································· .

i 0i 4 ·················0···································· .

I 3 ~ .Q o ~ ..o

2

o

ICopper Lead Nickel Zinc TSS

Metal

Figure 3-7. Variability of Concentrations Measured

37


variability, it is difficult to identify a trend in data.In addition, variability in concentration increasesthe uncertainty in estimates of the mean (average)concentrations and loads.

Much of the variability in urban runoff waterquality is random, but some variability can beexplained by hydrologic and antecedentconditions for each storm event Seasonal effectsmay be more apparent in semi-arid climates suchas found in the Southwestern United States whichtypically have dry summers and wet winters.Seasonal effects are often exacerbated by E1 Ninoevents or prolonged droughts, which can causelarge variations in seasonal rainfall and runoff.

The existing Coyote Creek hydrology andwater quality database was used to determine if apredictable relationship between water quality andhydrology could be devdoped. A relationshipwhich explains a significant amount of. thevariably in observed water quality could be used tobetter detect changes in water quality due to BMPimplementation.

Data SourmWater quality and stream flow in Coyote

Creek Were monitored during 38 storm eventsbetween April 1988 and April 1995. In addition,rainfall has been continuously measured since1948 in the watershed. However, complete data(both flow water quality and rainfall) are availablefor only 32 of the 38 events.Hydrology

Rainfall data from· the San Jose Gage (Alertgage No. 1453, National Weather Service Gage(NWS) # 7821) was used to represent rainfall foreach monitored storm event in the Coyote Creekwatershed. Stream flow data was collected at thewater quality monitoring station using water depthsensors calibrated to stream flow rates using arating curve.Water Quality

Water quality data were collected using flowcomposite samplers programmed to collect adiscrete sample after a given volume of flow(trigger volume) had passed the station. Thetrigger volume was adjusted prior to each eventbased on the forecasted rainfall amounts. Thediscrete samples are pumped into a largecomposite bottle contained within the samplerand transported back to the laboratory for

38

analysis. Results from the composite sampleanalysis are representative of the flow-weightedaverage concentration and are called event meanconcentrations (EMq.Data Summary MethodsHydrology

Hydrology data (rainfall and flow) for bothevent and antecedent conditions were summarizedfor each monitored storm event in Coyote Creekwatershed. Rainfall event statistics included totalstorm volume, average intensity, and peakintensity. Antecedent rainfall conditions wererepresented by total rainfall that occurred duringthe previous 14 days and 28 days, total rain to dateand dry period days. The SYNOP computerprogram was used to summarize the rainfallrecord for the San Jose gage.

In SYNO}> the definition of a storm event isbased upon an inter-event time and minimumprecipitation. The inter-eveQt time is the timebetween storm mid-points. An inter-event timeof 24 hours was used with a minimum of 0.1 inchvolume to calculate the statistics.

About nine years of flow data were available(two years before 1990 and seven after) from theCoyote Creek Station S4 flow gage. For eachstorm event the average, peak and total flow wereobtained. To represent antecedent conditions thetotal flow that occurred 14 and 28 days prior tothe storm event plus season to date were extractedfrom the data.Water Quality

Water quality data from Station S4 wasrecendy summari2ed as a part of a regional effortto collect data from the San Francisco Bay Areainto a single database (BASMAA 1996). Generalstatistics for parameters of concern weresummari2ed from the water quality database. Datawere excluded if the percent capture was less than75%. The percent capture is the percentage oftime the water quality sampler was sampling for agiven monitoring event. Events with low captureare not considered representative of a given event.Events 2,5,6 and 7 had low capture and were notused in the analysis. .The percent capture forevent #38 was not used due to an extreme (100year) event preceding this storm.

Metal concentrations associated withparticulates were calculated from the total and

dissolved metals and TSS data using the followingrelationship:

Particulate (:g/g) = {[fotal (:g/l) - Diss (:g/l)]!TSS (mg/l)} * 1000 :g/mg.

The percentage of metal present as dissolvedwas also calculated.

WaterQuality Trend DetectionWe examined temporal trends in key water

quality parameters using linear regression,correlation, stepwise regression, and analysis ofvariance and analysis of covariance with poweranalysis.

Simple Time TrendAnaJy.risSimple time-trend linear regression analysis

was used to determine if a water quality changedduring the monitoring period. Linear trends wereevaluated for flow-weighted concentrations oftotal and particulate copper (Cu), total andparticulate zinc (Zn), and total suspended solids(fSS). Analysis of variance was used to determineif the linear trend was significant.

AnaJysis ofVariance on Grouped YearsAnalysis of variance (ANOVA) was used to

determine if one group of data were significandydifferent from another group and to performpower analysis. Data grouping was necessary touse the ANOVA and power analysis techniques.Data from each water year (winter- spring) weregrouped into two groups (wet or dry) based onthe annual rainfall amounts. Average EMCs foreach group were compared using analysis ofvariance (ANOVA) to determine if they weresignificandy different from one another. ANaVAtests were performed using total copper asindicator variable using log-transformed databecause the data were log-normally distributedbased on the Shapiro-Wilks test for normality.

PowerAnaJysisA power analysis was performed to

determine how large of a change in water qualitycould be reliably detected for a given sample size.Power analysis determine the probability ofdetecting a given change in concentration (ifpresent) for a particular data set and takes intoaccount the amount of variability in the data.


Hydrology-WaterQuality ModelThe hydrology and water quality data were

examined to develop hydrology-water qualitymodel for Coyote Creek for use in the trendanalysis.

The relationship between individualhydrologic parameters and water quality was firstexamined by calculating the Pearson correlationcoefficient. One hundred times the square of thecorrelation coefficient indicates how much of thewater quality variability is explained by eachhydrology parameter assuming they are linearlyrelated.

Step-wise linear regression was then used todetermine the form of the relationship. For thisanalysis the hydrologic data were separated intotwo classes: rainfall and flow. The stepwiseregression analysis was conducted separately foreach parameter (fotal, Dissolved, and Particulate,Copper, Lead, Nicke~ and Zinc and TotalSuspended Solids) with each class because rainfalland flow are highly correlated which wouldotherwise confound the analysis. A mixedstepwise regression (forwards and backwards) wasperformed using a selection and removalsignificance criteria of p <0.2. The besthydrology-water quality model was then chosenfor inclusion in the grouped water year in ananalysis of covariance (ANCOVA). A poweranalysis was performed on the ANCOVA forcomparison with the ANOVA results.

Exceedance Frequencies of Water QualityStandards

We analyzed rainfall and water quality data(see methods described above) and compared thestormwater monitoring data to two different waterquality standards: Water Quality Objectives andWater Quality Criteria. The San Francisco BayBasin Plan Water Quality Objectives (\VQOs) arethe current regulatory objectives for theprotection of aquatic life in the San Francisco BayBasin. For freshwater systems such asstormwater, the objectives are hardnessdependent and are to be compared to total metalconcentrations. In recognition that the dissolvedmetals fraction is more representative of metalbioavailability, USEPA adopted Interim Final

39


Water Quality Criteria (WQC) which are to becompared to dissolved metal fractions.

The freshwater WQOs are expressed as:

e<A*In (l1-J) +B)

where TH =total hardness as CaC03 (mg/L),and A and B are metal-specific factors for acuteand chromic toxicity.

The freshwater WQC are expressed as:

e<A*1n (l1-J) +B) *CF

where CF refers to the acute or chronicconversion factors for dissolved metals.

Two different exposure durations areincluded in the WQOs and WQC: acute, whichare based on a minimum I-hour exposureduration; and chronic, which are based on aminimum 4-day exposure duration. SYNOPanalysis was used to determine the distribution ofstorm event duration's during the monitoringperiod. The number of exceedances of acute andcMonic objectives and criteria were evaluated foreach water year to determine if trends wereapparent.

Sediment Characteristics and Contamination

Many pollutants found in stormwater runoffattach to sediments and can adversely impactaquatic communities. Benthic organisms feed andinhabit deposited sediments, while nonbenthicorganisms may be exposed to contaminatedsediments through re-suspension, by ingestingbenthic organisms, and by exposure to thesediment as it settles.

We slUllpled surficial sediments to assesssediment contamination during two field surveysconducted at the completion of the storm seasonOune 28 - 29, 1999) and again just prior to thestart of the subsequent storm season (October 21,1999). This slUllpling regime was also selected tocoincide with the periods sampled in the 1978 79 baseline study (pitt and Bozeman 1982).

We sampled nine sites in Coyote Creek(Figure 3-5), each consisting of 100-meter reachesthat corresponded to the reaches sampled for fishand macroinvertebrate assemblages. Thesediment sampling sites were chosen based on thequantity of historical data from each location, thelocation of each site relative to macroinvertebrate

40

sampling locations, and the location of each siterelative to land use patterns (e.g., urban, rural,transition, and reference) along the main stem ofCoyote Creek.

We collected surficial sediments, (top 2 em)and composited samples to limit the influence oflocal variability. A total of five samples of equalvolume were collected from locations throughoutthe reach. Sampling was designed to focus onareas with finer sediments in order to ensurerepresentative sampling of potential contaminants.Each of the five samples was taken fromdepositional areas located within each reach toimprove chances of locating sites where finematerial would have an opportunity toaccum~ate. The decision to sample lhe top twocentimeters of sediment was also an effort tocollect recently deposited material. During eachsurvey, one of the field samples was split andsubmitted blind to the laboratory.

Samples were analyzed for particle size andtrace metals (copper, lead, arsenic, mercury,cadmium and chromium). Analytical methodsand data quality objectives for each analysis aresummarized in Table 3-4. Sediment particle sizeanalysis was performed acc01:ding to methodsdescribed by Plumb (1981). Sediments weredigested using EPA Method 3050 and then tracemetals were analyzed by ICP-MS using EPAMethods 7061 (arsenic), 6020 (cadmium,chromium, copper and lead), and 7471 (mercury).All sediment trace metal data are reported on adry-weight basis.

Quality assurance measures applied to thelaboratory analyses included use of laboratoryduplicates, certified reference materials and matrixspike/spike duplicates (KLI 1999). Repeatedmeasures such as field splits, laboratory duplicates,and matrix spike/spike duplicates were used toassess precision of the measurements. Recoveryof the target analytes from certified referencemat~s and spikes added directly to the samplematnces were used to evaluate accuracy of theanalyses.


Table 3-4. Summary ofAnalytical Methods, Sample Handling, Detection Limits and Data Quality Objectives.

Parameter Units EPA Maximum Preservation Target Accuracy Laboratory(dry wt.) Method Holding Detection Limit (% Recovery) Duplicate

Time (mg/Kg) RPD(Percent)

Particle Size % Plumb! Cool, 4°C 0.1 NA NA

Arsenic mg/kg 7061 6 months Cool, 4°C 0.1 ±25 30

Cadmium mg/kg 6020 6 months Coo~ 4°C 0.1 ±25 30

Chromium mg/kg 6020 6 months Cool,4°c 0.1 ±25 30

Copper mg/~ 6020 6 months Coo~4°C 0.1 ±25 30

Lead mg/kg 6020 6 months Cool, 4°C 0.1 ±25 30

Mercury mg/kg 7471 28 days Cool, 4°C 0.02 ±25 30

tplumb, R H. Jr. 1981. Procedures for Handling and Chemtcal AnalysIS of Sediment and Water Samples.AD/Al03 788 State University College at Buffalo, Buffalo, NY.

3.2.3 Biological Indicators

Fish Assemblages

SamplingWe sampled fish assemblages using the

Rapid Bioassessment Protocols (REPs) (Barbouret.al. 1997). Fish were collected from a 100-meterreach containing multiple habitat types. We setblock nets (0.25 inch mesh) at the reach ends ande1ectrofished using a Smith Root 12-B backpackunit with hand-held anode pole and trailingcathode. One fisher and two netters (0.125 inchmesh) fished one pass from downstream toupstream. Fish were transferred to buckets andlive wells equipped with aerators on hot days.Stations with the least riparian cover were fishedfirst in the morning, before hottest part of day.

All fish greater than 20 nun in length wereprocessed. The first 25 fish of each species wereweighed, measured, and anomalies noted. Theremaining fish of that species were counted andweighed as an aggregate. We took care to includea distribution of size classes in the 25 fish. Specialhandling procedures were implemented forprocessing rainbow trout (Oncorf?ynchus mykiss); alltrout were processed and returned to the creekimmediately.

A voucher collection was made, storing fishin 10% buffered formalin. This collection ishoused at Kinnetic Laboratories in Santa Cruz,California. Some cyprinid specimens were

submitted to Randall Baxter of the CaliforniaDepartment of Fish and Game for confirmation.Robert Leas provided verification of some cottidspecimens.

Fish were sampled during three events tocoincide with baseline study (pitt and Bozeman1982) sample collection schedule. The 5-personfield team completed 2 - 4 stations per day. Thefirst sampling period was from May 20 to June 4,1999 (nine workdays). The second sampling wasfrom June 21 to 29, 1999 (seven workdays). Thethird sampling was from September 14 toOctober 8, 1999 (seven workdays). The field teamrecorded data on standard forms in the REPmanual: Fish Sampling Field Data Sheet (eachsampling event), Habitat Assessment Field DataSheet (once), and Physical Characterization/WaterQuality Data Sheet (once).

Data AnalYsisThe REPs define 12 metrics, grouped into

three categories, for evaluating fish assemblages:Species Richness and Composition, TrophicComposition, and Abundance and Condition. Weselected metrics from each category based on theirapplicability to Coyote Creek, and potentially toother streams in semi-arid areas (Table 3-5). Dueto the format in which the baseline study reportedfisheries data, we could only use a subset of thesemetrics to compare to the 1999 SEIDP data.Metrics were calculated for each combination ofstation and sampling period.

41


We could not create an Index of BiologicalIntegrity (IBI) using our data because referenceconditions and scoring criteria are not availablefor this region. However, our sampling data andmetric analyses could be combined with theresults of other studies conducted in the regionand used to help build a regionally appropriateIBI.

We used Analysis of Variance (ANOVA) toanalyze the fish assemblage metrics and test (a. =0.05) the following hypotheses: the means of themonthly samples were the same; the means of thestation groups were the same. We used anunbalanced one-way ANOVA model since thenumber of observations differed for eachcombination of seasonal time period and stationgroup (Zar 1999). Differences between theobserved and predicted values (residuals) weretested for normality and homogeneity of varianceusing the Shapiro-Wilks and Levene's test,respectively. If either test was failed, we repeatedthe ANOVA using logarithmically transformedvalues. If either test again failed, we repeated theanalysis using the ranked values in a nonparametric ANOVA.

Two variables failed each test (Shapiro-Wilks,Total Number of Individuals and Total Biomass;Levenes, Number of Native Species, Number ofNative Individuals). Logarithmic transformationsnormalized only the two variables that had

previously failed the Shapiro-Wilks test.Therefore, the Number of Native Species and theNumber of Native Individuals were tested usingnonparametric ANOVAs,

In addition to the two main factors, samplingperiod and station group, we included asinteraction terms. If the critical value associatedwith the test statistic of the interaction term was >0.05, the interaction was considered to beinsignificant, and each main factor was analyzedseparately. If the interaction was significant, eachcombination of station group and samplingseason was analyzed.

We tested whether duplicate data were fromthe same population by pairing data from thestation and duplicate location immediatelyupstream (Gilbert 1987). Duplicate values weresubtracted from station values. Differences weretested for normality (a. = 0.05, Shapiro-Wtlkstest). If differences were normal we tested themusing paired t-tests. Otherwise, we examined theskewness of nonnormal differences. If theabsolute value of the skewness exceeded 1.0, weused the sign test to test if the difference waszero. Otherwise we used the signed-rank. test.

Comlating Impemousness with Aquatic HealthDegradation

We examined the association betweenpercent imperviousness and aquatic health bycomparing percent imperviousness individually tofish metrics. Metrics we tested included number

Table 3-5: Metrics used to evaluate the fish assemblage indicator. A single asterisk indicates metrics that were used to comparethe 1999 to the 1978-79 baseline (pitt and Bozeman 1982) fisheries data. A double asterisk indicates a metric that was only usedfor the comparison to the baseline study.

Metrics DescriptionSpecies Richness and Composition

la. Number of Native Species *1b Number of Native Individuals## Total Number of Species**Ja Percent of Cyprinid and Centrarchid SpeciesJb Percent of Cyprinid and Centrarchid Individuals6 Proportion of Individuals as Tolerant Species

Trophic Composition8a Number ofInsectivorous and Invertivorous Species8b Number ofInsectivorous and Invertivorous Individuals

Abundance and Condition10 Total Number ofIndividuais12 Total Number of Diseased Individuals13 Total Biomass##[01 Biomass of Native Individuals**

42

-------------------------~-----~---- ------

of native species, nonnative species, total species,tolerant species, total individuals, nativeindividuals, and diseased individuals, as well aspercent of native species, tolerant species,diseased individuals, and total biomass. Weidentified tolerant species from Moyle andMarchetti 1999, which included common carp,fYPrinis carpio, fathead minnow, Pimephales promelas,green sunfish, Lepomis fjanellus, black bullhead,Ameirurus melas, red shiner, Notropis Iutren.ri.r, andgolden shiner, Notemigonus cry.roleucas. We couldnot calculate indices of biological integrity forcomparison with imperviousness because asilieady discussed, reference conditions have notbeen established for this region, and we could notidentify reference sites with similar physicalcharacteristics as our urban and rural samplingstations.

We tested associations statistically usingsimple linear regressions (Zar 1999). Data weretested for normality and homogeneity of varianceusing the Kolmogorov-Smirnoff (K-S) test andthe Levene Median test, respectively. Ratio datawere arcsine transformed. Data failing the K-Stest were logarithmically transformed. Analysesusing the transformed data that failed the K-Sand/or Levene tests (percent tolerant species andpercent native species) were repeated usinglogistic regression (frexler and Travis 1993).Because one sampling station (R3) exhibitedunusually high values for metrics related to thenumber of individuals, we repeated analyseswithout this site. Because one of the referencesites (Refl) was not sampled in September,metrics were also calculated both without this site,and using mean values calculated from the Mayand June sampling periods. Results from thecomplete data set and the subset were compared.

Macroinvertebrate Assemblages

Field SamplingWe used the multihabitat approach described

in the revised Rapid Bioassessment Protocol(RBP) for Use in Streams and Rivers (Barbour etaI. 1997) to sample maeroinvertebrates. We choseto sample multiple habitats rather than rifflehabitat alone because gradient, substrate andvelocity variations among the stations on CoyoteCreek create a broad diversity of invertebrate


habitats and to acquire data that would becomparable to our baseline study (pitt andBozeman 1982).

Sampling Was conducted in conjunction withthe fishery work for the May sampling event onthe following dates: May 20 - 21, 25 - 27, andJune3. We sampled macroinvertebrates independentlyof fishes in June (17 - 18) to improve efficiency.Samples for each station were recorded on a"Benthic Maeroinvertebrate Field Data Sheet."We sampled using a D-frame dip net, 0.3 metershigh and wide, with 500 micrometer mesh.Cobbles and gravels were kicked, jabs were madeamong vegetation and roots, and in-stream woodydebris was rubbed to dislodge maeroinvertebrates.At each station, 20 subsamples were collected andcomposited in the field, resulting in a singlesample, preserved in formalin. The subsampleswere distributed proportionately among thehabitats in the 100 meter reach. The completedsheets showed the location and habitat type foreach of the 20 sub-sampling locations.

lAboratory Sorting and Sub.ramplingApproximately 72 hours after formalin

preservation in the field, macroinvertebratesamples were transferred to 70% ethanol atKinnetic Laboratories, Inc. in Santa Cruz,California. Subsampling and sorting of thecollected material generally followed theprocedures outlined in the Draft REPs (Barbouret al. 1997) and the Quality Assurance ProjectPlan for Stormwater Environmental IndicatorsPilot Demonstration Projects (KLI1999a).

Initially, large woody debris, large leaves, orgravel were removed from samples withconsiderable amounts of these materials(approximately half the samples). This was doneto make the material more uniform in size forsubsampling. Material from these samples wasspread out in a tray and each large leaf, twig, etc.was dip-rinsed in a large beaker and inspected toinsure no organisms remained attached. The rinsewater was sieved (0.5 mm mesh) and the debris inthe sieve and tray were returned to the sample jar.The tray and sieve were visually inspected fororganisms and rinsed thorougWy betweensamples.

With the objective of obtaining fourreplicate, 100-organism subsamples (± 20%) from

43


each original sample, the material in the jar wasgendy mixed before opening, sieve~ and s~read

as evenly as possible across a tray tnto which agrid composed of thirty-six 6.0 em by 6.0 emsquares was placed, Material ~as spread over 1.8,24, or 36 grids, depending on 1ts volume. Materialfrom four random grids was removed and placedin new, smaller jar and properly labeled inside andout. These subsamples were themselvessubsampled due the presence of high numbers oforganisms. However, spreading the much smallervolume of material in these subsamples appearedobviously destructive to the sample (andorganisms), therefore, we. placed sm~ clum~s ~f

the sample (again, well nuxed and drained) wtthingrids in the tray (ranging fro~ 1? t~ 36, grids),taking care to make them as similar tn sae andcomposition of material as possible. Materialfrom a single random grid was then removed andplaced in ethanol in a 10 em,x 1.5 em, petri dishmarked into fourths. If a dish contalned morethan 120 organisms we used low-powermicroscopes ~ 10-power) to randomly sortmaterial into the dish's quarters. In this way athird level of subsampling was easilyaccomplished, with a maximum number of four"grids" to sort. If the dish contained less than 80organisms, another random grid was placed in anew petri dish and sorted by quarter again until100 ± 20 organisms were removed. Thus, alloriginal samples were subsampled (or split) up tothree times. Annet.ids, molluscs, arthropods, andmiscellaneous taXa were placed in separate vialsand labeled on the inside, indicating the station,sample date, subsample "level", and sorter. Theremaining sorted debris were placed in a third andfinal jar for QA/QC. The four 100-organismreplicates from each station were identified to thelowest practical taxonomic level and the data wereentered into a spreadsheet.

Laboratory QA/QC procedures involvedresorting 30% of the volume of each samplesorted to check sorting efficiency and accuracy.Individuals other than those who conducted theoriginal sorting conducted the resorting ofsamples. If the number of organisms found in thedetrital remains exceeded ten percent of the totalorganisms found during the original sort, thesample was considered a QA/QC failure and was

44

completely resorted. If the 100% resort still f~edat the 90% efficiency level then a new sorttngsheet was started and the process was repeateduntil 90% efficiency was achieved. We maintainedrecords of the subsampling, sorting, and resortingprocesses for each sample.

Data AnalYsisThe RBPs group metrics for. evaluating

macroinvertebrate assemblages into threecategories: Taxa Richness; Trophic Composition,and Tolerance/Intolerance. We selected metricsbased on their applicability to Coyote Creek, andpotentially to other streams in semi-arid areas(Table 3-6). Due to the format in which thebaseline study reported fisheries data, we couldonly use a subset of these metrics to compare tothe 1999 SEIDP data. Metrics were calculated foreach combination of station and sampling period.

The tolerance/intolerance metrics evaluatedhere were based on a rating system developed bythe CDFG (Ode 1999), similar to the Hillsenhoffindex. The CDFG system rates macroinvertebrate taxa on a scale of 0 to 10 as follows:least tolerant organisms had ratings of 0, 1,2, and3 and the mo~t tolerant organisms were thoserated 8 9 and 10. This rating scale is based on, , ,

professional judgment. It does not ~ply

tolerance or intolerance to any parttcularpollutant, nor it is not mathematically relational.

We used ANOVA to analyze themacroinvertebrate assemblage metrics and test (a.=0.05) the following hypotheses: the means ofthe monthly samples were the same; the means ofthe station groups were the same. Testingprocedures were the same as ~os~ descri~ed

previously for the fish assemblage mdicator. Ftvevariables failed either both (% EPI' taxa, % LowTolerance Taxa, % Low Tolerance Individuals) orone of the tests (Number of Low Tolerance Taxa- Shapiro Wtlks; % High Tolerance Taxa Levenes). Logarithmic transformation onlynormalized one variable (% Low Tolerance Taxa).Therefore, we tested the other failed variablesusing non-parametric ANOVAs,


Table 3..6: Metrics used to evaluate the macroinvertebrate assemblage indicator. A single asterisk indicates metricsthatwete used to compare the 1999 to the 1978-79 baseline (pitt and Bozeman 1982) macroinvertebrate data. Adouble asterisk indicates a metric that was only used for the comparison to the baseline study.

M,me Catl/.Ory M,me Dmription

Taxa RichnessTotal Number of Taxa *Total Number of Ephemeroptem, Plecoptera, Tricoptera (EPT) Taxa *Percentaj!e of EPT Taxa**

Trophic CompositionPercent of EPT Individuals *Ratio of EPT to Chironomid and Olilrochaete (Co) Individuals *

Tolerance, Intolerance MeasuresNumber of Low Tolerance TaxaPercent of Low Tolerance TaxaPercent of Low Tolerance IndividualsNumber of High Tolerance TaxaPercent of High Tolerance TaxaPercent of Hil!h Tolerance Individuals

3.2.4 Programmatic Indicators

Number of Illicit ConnectionsIdentified/Corrected

We obtained records for reported ICIDincidents from annual reports (FY 93-94 throughFY 97-98) submitted to the Program by the Citiesof San Jose and Milpitas. (Reports for FY 92-93did not show ICID incidents by category.) Weentered these records into a relational databaseand summarized illegal discharge incidents bypollutant category and year and illicit connectionsby year only. Pollutants likdy associated withICID incidents were determined by reviewingmatrices that list pollutants associated with generalhuman activities (SCVURPPP 1999) and withindustrial activities (United States EnvironmentalProtection Agency 1995). For several illegaldumping categories (e.g., illegal dumping hazardous, illegal dumping - non-hazardous,parking lots, and spills), we reviewed descriptionsof events reported (throughout San Jose) bymunicipal inspectors, and summarized thepollutants involved.

The City of San Jose maintains their recentillegal dumping incident data (1995 - 1998) in arelational database. We georeferenced thelocations of such incidents reported in San Joseusing a geographic information system (GIS), andidentified those occurring only in the Coyote

Creek watershed. We also georeferenced moststormdrain outfalls draining to Coyote Creek.Mapping stormdrain outfalls and illegal dumpingincident sites illustrated which downstreamreaches may be affected by pollutants generatedfrom these sites. We could not include the City ofMilpitas' incident data in this spatial analysisbecause their inspection records are available inhardcopy only and are not cross-referenced byincident type nor by fiscal year.

We examined temporal trends in all illegaldumping incidents reported by both cities. Wesubsequendy compared the results of this trendanalysis to temporal trends observed in the threeyears of georeferenced San Jose data. We alsosummarized increases in population and in Copermittee inspection/outreach efforts andcompared them to temporal trends in the numberof reported ICID incidents.

We considered the types of pollutantsassociated with each category of ICID incident,along with the summaries of ICID incidents, toextrapolate the probable increase or decrease inthe quantity of various pollutants discharged.

Permitting and Compliance

Claytor and Brown (1996) describe thePermitting and Compliance Indicator as follows:

"Tracking the number and type of NPDESstormwater permits issued, the number ofstormwater discharges in compliance with their

45


pennits, and the number and type of BMPsimplemented in conjunction with the pennitsallows municipalities to gauge the relative impactof various pollutant sources (i.e. urban vs.industrial vs. construction), detennine ifregulatory baselines are being met, and identify theneed for additional enforcement activities."

California's statewide National PollutantDischarge Elimination System (NPDES) pennitsregulate stormwater runoff from industrialfacilities and construction sites. Industrial facilitieswith specified standard industrial classification(SIC) codes must apply for coverage under thestatewide industrial stormwater permit (StateWater Resources Control Board 1997).Construction sites that disturb more than fiveacres must apply for coverage under the statewideconstruction stormwater pennit Facilities arerequired to recognize and comply with theirresponsibility by filing a notice of intent (NOl)with the State Water Resources Control Board(SWRCB). Once covered under the statewidepermit (permit), NOI filers are required todevelop, implement, and annually update astormwater pollution prevention plan (SWPPP),sample and analyze stormwater discharges twice ayear, and submit annual reports. Facilities thatchange or cease operations may presentjustification to be removed from coverage by thePermit

The SWRCB delegates responsibility forenforcing the Permit to the nine Regional WaterQuality Control Boards (RWQCBs) (CaliforniaRegional Water Quality Control Board 1992). TheRWQCB for the San Francisco Bay Basin (Region2) does not inspect or enforce the Permit atindustrial sites, but does inspect someconstruction sites.

The SWRCB's initial list of NOI filers wasdeveloped in 1992-1993 by holding publichearings and by disseminating informationthrough trade associations. Some localgovernments also distributed. information aboutthe Permit requirements in utility bills.Approximately 5,000 industries submitted NOlsthat first year. Since then, the number of NOIfilers has fluctuated. Some sites terminatedPermit coverage because their activities, thoughassociated with a regulated SIC class, wereexempt, or because they modified their facilities.

46

The nine RWQCBs require NOI filers to developand implement SWPPPs but do not require NOIfilers to submit any proof of having done so.

SCVURPPP's Co-permittees are notresponsible for enforcing the statewide NPDESpermits. Stormwater NPDES-permitted agenciesin neighboring Alameda County developed amemorandum of understanding with the RWQCBthat provides for local enforcement of thestatewide stormwater NPDES permits. No suchagreement covers the Program Co-permittees.However, the Co-pennittees do informally assistthe RWQCB to enforce the statewide permits aspart of their activities to prevent pollutants inrunoff from reaching municipal storm drains.

We selected the following criteria to evaluateprogram effectiveness:

1. Percent of those facilities that should haveapplied for coverage the statewide Permit thathad filed NOIs with the SWRCB.

2. Percent of NOI filers that submitted annualand monitoring reports.

3. Percent ofNOI filers that maintained anannually updated Storm Water PollutionPrevention Plan (SWPPP) on-site.

In connection with the third criterion, wealso evaluated the number and type of BMPsimplemented.

Because of the large number of industrialfacilities in the Coyote Creek watershed, and thedifficulty in identifying non-filers, we evaluatedindustrial activity there for the first criterion only.We supplemented this characterization byapplying the second two criteria to the 32businesses in the Walsh Avenue catchment.

To identify the number and type ofbusinesses in the Coyote Creek watershed coveredunder California's NPDES Permit, wedownloaded from the SWRCB database(swrcb.ca.gov) records of businesses filing NOlsin the San Francisco Bay Region. We queried thedatabase by zip code to identify records within theCoyote Creek Watershed, and by address to matchthe address range within the Walsh Avenuecatchment. We subsequendy used a geographicinformation system (GIS) to georeference thesedata and identify and map their locations withinthe study areas. Mapping NOI filer locations


Table 3-7. Pollutants monitored by NOI industrial facilities in the Coyote Creek watershed, as a requirement of theNational Pollutant Discharge Elimination System permit (SWRCB 1995). Monitoring parameters are listed by standardindustrial classification (SIC) sector (digit places designated as "X.. indicate that subsectors exist, in which cases only asubset of monitoring parameters may apply).

Seeto Industria1 Activity SIC Code Monitoringr Parameters

B Paper, Allied Products Manufacturing 26XX COD

C Chemical, Allied Products Manufacturing 28XX Al;Fe;N+ N;Zn;Pb;P

D Asphalt Paving/Roofing/Lubricant Manufacturers 295X 1'8S

E Glass/Clay/ Cement/Gypsum Manufacturer 32XX AI;1'8S;Fe;Zn

F Primary Metals 33XX Al;Zn;1'8S;Cu;Fe

J Mineral Mining & Dressing 14XX 1'8S;TDS;N+N

L Landfills and Land Application Sites 4953 1'8S;Fe

M Automobile Salvage Yards 50XX 1'8S;Fe;Pb;AI

p Land Transportation Facilities 41XX;42XX none listed

R Ship and Boat Building or Repairing Yards 373X none listed

S Air Transportation Facilities 45XX BOD;COD;NH;pH

U Food and Kindred Products 20XX BOD;COD;1'8S;N+N

W Furniture and Fixtures 25XX;2434 none listed

X Printing and Publishing 27XX none listed

y Rubber/Plastic Manufacturer 30XX Zn

AA Fabricated Metal Products 34XX Zn;N+N;Fe;AI

AC E1ectronic/E1ectrical/Photographic/Optical Goods 36XX none listed

Legend'

AI Aluminum NH AmmoniaBOD Biological OXV2Cn Demand P PhosphorusCOD Chemical Oxv2Cn Demand Pb LeadCu Copper 1'8S Total Suspended SolidsFe Iron Zn ZincN+N Nitrate + Nitrite

United States Environmental Protection Agency. 1995. Federal Register Part XIV Final National Pollutant Discharge EliminationSystem Storm Water Multi-Sector General Permit for Industria1 Activities; Notice. United States Environmental Protection Agency,Washington District of Columbia.

enabled us to identify which stream reachesreceive runoff from these sites.

We evaluated construction activity in theCoyote Creek watershed for the second and thirdcriteria, but not for the first. (Municipalities issuebuilding permits only after receiving a copy of theNOr; therefore, 100% of construction sitesshould meet the first criterion.)

To identify potential pollutants in runofffrom Nor facilities, we created a lookup table that

included src codes and associated pollutants thatNOr filers are required to monitor (fable 3-7). Bylinking this lookup table to the georeferencedNOr data we developed a mechanism forpredicting which pollutants may be delivered tospecific stream reaches within Coyote Creekwatershed.

We applied all three criteria to industrialfacilities in the Walsh Avenue catchment. Weinterviewed site managers at 29 of 32 businesses

47


within the catchment and received completedsurveys from 27 (see methods forIndustrial/Commercial Pollution Prevention,Indicator #18). We also interviewed City of SantaClara inspection staff to identify their methods forinsuring that businesses are complying with filingrequirements. SIC codes were obtained frombusiness licenses filed with the City of Santa Clara.

We evll1uated the percentage· of eligiblefacilities that filed NOls by comparing SIC codesand industrilll activities noted during our surveyand site visits to the list of active filers gleanedfrom the California State Water Resource ControlBoard's (SWRCB) list. We obtained fromSWRCB staff hardcopies of annual andmonitoring reports for filers in the Catchment.We evaluated the percent of NOI filers thatsubmitted annual and monitoring reports bycalculating the percent of NOI filers that hadsubmitted annual and monitoring reports sincetheir regulation under the Permit. We comparedthe percentage of reporters to filers within theCatchment, against the corresponding percentagewithin the nine Bay Area counties. Using oursurvey results, we calculated the percentage ofNOI filers within the catchment that had current,on-site SWPPPs.

Growth and Development

Urbanization of watersheds contributes tochanges in basin hydrology, channel morphology,and water-quality constituents. Cumulativelythese changes affect instrearn habitat structure andassociated biological communities. Quantifyingthe relationship between urbanization and aquaticecosystem health is an important step towardpreserving and enhancing biological resources inurban watersheds.

Percentage of impervious watershed area hasbeen identified as a common indicator ofurbanization often used for community-level andwatershed assessment and planning (Arnold andGibbons 1996; Chiytor and Brown 1996, May etal. 1997a, Center for Watershed Protection 1998).Impervious surfaces, including roads, sidewalks,roof tops, and parking lots prevent or inhibitrainfall from infiltrating to soil and groundwater.Accumulated salts associated with runoff mayfurther reduce soil infiltration capacity. Soil

48

compaction from development can render evenlandscaped, pervious areas somewhat impervious(Booth and Jackson 1997). As developmentincreases, so typically does the percentage ofwatershed area covered by impervious surfaces.

Urbanization causes watershed-wide change,thus it is important to evaluate imperviousness atthis scale. Increased imperviousness withinriparian corridors can cause more immediate andbroad impacts to aquatic ecosystem functions.Lammert and Allan (1999) found that land usewithin riparian corridprs was a better predictor ofbiotic condition than land use withinsubwatersheds; they concluded that the scale of

. investigation influences the strength of predictivevariables. In this study we assessedimperviousness at the watershed scale, and at afiner spatial scale (within riparian corridors).

Imperviousness has most often beenestimated using variations of two techniques(Center for Watershed Protection 1998): (1)direct measurement from remotely sensed data orfrom topographic maps; and (2) estimation fromland use data, zoning, road area or density, orpopulation. Combining techniques and/or severaldata sources can improve the overall estimate ofimperviousness particularly when accuracy variesamong data sets. (By "accuracy" we meanprecision of locations, spatial resolution, andcurrency of data.) For example, the estimate ofimpervious area directly connected to storm drainsystems could be improved by combining highaccuracy road area data with lower-accuracy landuse data. The best choice of technique (orcombination of techniques) depends on theaccuracy required, on available data sources andbudget, and on watershed size.

Defining Ifydrologic UnitsWe estimated percent imperviousness for

subwatersheds draining to the 18 samplingstations on Coyote Creek (Figure 3-5). Toidentify drainage area tributary to each samplingstation, we modified an existing subwatersheddata set (Buchan 1999a) that was based on naturaltopography and the City of San jose's storm drainnetwork. We defined the "local" drainage areainfluencing each sampling site as the watershedarea from each site to the watershed of the nextupstream sampling site. We defined the

"cumulative" drainage area to each site as the totalupstream area. However, because Anderson Dam(Dam) greatly modifies the Creek's hydrologicregime and sediment transport, we omitted thearea above the Dam from the cumulative drainagearea for stations below the Dam CU1 - RS). Themajority of land above the Dam is undevelopedwith minimal impervious area that could influencesites downstream of the Dam.

Basemap DevelopmentWe developed a basemap of land uses in the

watershed using eight data sources (fable 3-8).Our primary data source was the April, 1999 SantaClara County Assessor's database, which includedinformation on ownership and land use type formost parcels in the Coyote Creek watershed.Parcels with unknown land uses (2,402 parcelscovering 23.5% of the watershed) werereclassified in a geographic information system(GIS; ArcView, Environmental Systems ResearchInstitute) using additional data sources (fable 38). We used one-meter resolution digitalorthophotographs to groundtruth parcels thatwere reclassified using the smaller scale datasources, and to identify land uses for remainingparcels with unknown land uses. We similarlygroundtruthed, and reclassified as necessary,parcels in public ownership because land usedesignations for publicly owned parcels are lessaccurate than for privately owned parcels since theCounty does not regularly assess them (BobEasley, Santa Clara County Assessor Office,personal communication, 1999). Parcels werereclassified using the categories included in the


Assessor's database with several exceptions whereadditional categories were defined.

Calculating Percent ImperviousnessA literature search was conducted to identify

studies that had the most accurate imperviousnessestimates (based on their methods and datasources), and were in regions with similar climateand land use patterns. We selected imperviousnesscoefficients from the most applicable previousstudies (Bredehorst 1981, Buchan 1999a). Mostimperviousness coefficients were drawn fromBredehorst (1981), who studied a statisticallyrepresentative random sample of land use classeswithin the Los Angeles Flood Control District'sjurisdiction. These coefficients were rounded totwo significant digits (!raj Nasseri, ChiefHydrologist, Los Angeles Flood Control District,personal communication, 1999). For land useclasses that were not sampled by Bredehorst(1981) (some subclasses of public parks, schoolsand right-of-way land uses), we used coefficientsdeveloped by Buchan (1999a). We estimatedcoefficients for the reclassified, formerly"unknown", land uses by visually assessing allsuch parcels.

To estimate watershed imperviousness wecreated a lookup table containing the imperviouscoefficients for each land use and joined it withthe land use basemap in the GIS. Imperviousnessfor land uses was estimated by multiplying landuse acreages by imperviousness coefficients.Impervious watershed area was estimated byintersecting the parcel and right-of-way coverageswith watersheds in a GIS and calculating the

Table 3-8. Data sources used to develop an accurate land use base map for the Coyote Creek watershed.

Data Source Scale Spatial Extent Owner

County Assessor Parcel 1:500 Covote Watershed Santa Clara CountyProtected Lands 1:24,000 Coyote Watershed Green Info NetworkVacant Land Inventory 1:500 City of San rose rurisdiction City of San JoseEasements 1:500 Santa Clara Basin Santa Clara Valley Water DistrictWater Wavs Mana2ement Model 1:12,000 Santa Clara Basin Santa Clara Vallev Water District1995 Land Uses 1:24,000 Coyote Watershed Association of Bay Area Governments1998 Dmital Orthoohotos 1 meter City of San rose Jurisdiction City of San JoseDigital USGS Topographic 1:24,000 Coyote Watershed United States Geological SurveyQuadrangles

49


percent imperviousness. We also calculated thecumulative percent imperviousness of eachsubwatershed.

To demonstrate the influence of dataaccuracy on estimates of percent imperviousness,we compared estimates of imperviousness derivedfrom the land use basemap to those generatedfrom an earlier study (Buchan 1999c) that usedcoarser scale (l-hectare) 1995 land use data(ABAG 1996). To demonstrate the influence ofscale on estimates of percent imperviousness, we

.estimated imperviousness in the cumulativedrainage area tributary to each station.

We estimated percent imperviousness withinthe riparian corridor by intersecting in a GIS, thebasemap of percent imperviousness with theriparian corridor area as defined by the City of SanJose (1994). Where riparian vegetation dataexisted for streamside areas within the Basin, itwas used to define riparian corridors; whereriparian vegetation data was absent, ripariancorridors were defined by a distance of 100 feeton either side of the creek centerline, (or top ofbank where available). This distance was chosento because most municipalities in the Basin havepolicies or ordinances requiring at least 100-footsetbacks from riparian vegetation or top of streambank. Multiple creek data sets were compiled toprovide comprehensive coverage of creeksthroughout the entire Basin (Buchan 1999b).

Projected imperviousness for the CoyoteCreek watershed was estimated using economicand demographic forecast data to determine theamount of land available for developmentbetween 1995 and 2020 (ABAG 1998). This dataincluded information from "local governmentgeneral plans, zoning, urban growth boundaries,and other policies specific to land development.Estimates of projected imperviousness werederived by taking the difference between existingand projected land use acreages for eachwatershed, assigning coefficients ofimperviousness to the two projected land useclasses (residential, 0.86; industrial/commercial,0.91), and multiplying the coefficients by thedifferences between existing and projected landuse acreages (Buchan 1999c). We did not estimateprojected imperviousness by sampling stationsubwatersheds for this study, although it is

50

feasible to do. Rather, the methodology wasincluded here to demonstrate its utility.

3.3 Project Approach in the Walsh AvenueCatchment

To demonstrate the effectiveness of havingimplemented an industrial pollution preventionprogram for seven years we evaluated a suite ofindicators (water quality indicators - ToxicityTesting and Non-point source loadings -, siteindicators - Industrial/Commercial PollutionPrevention, and Industrial Site ComplianceMonitoring - and a programmatic indicatorNumber ofBMPs Installed and Maintained.


Industrial/Commercial Pollution Prevention

BMPs Installed, Inspected and Maintained

Industrial Site Compliance Monitoring

To implement these indicators we reviewedthe City of Santa Clara's inspection records, andasked business owners to respond to aquestionnaire. We also accompanied a City ofSanta Clara Fire Department inspector to visit 29of the 32 (91%) businesses within the Catchment.

Surveys were sent to all the site managers ofthe businesses in the Walsh Avenue Catchment.The survey requested general informationregarding the business, their prior knowledge ofthe Program, previous inspections, implementedBMPs and associated costs.· Meetings werescheduled with site managers to collect and reviewthe completed surveys. Of the 32 businesses inthe Catchment, 29 were visited and 27 completedsurveys were collected. The meetings with eachindividual site manager were followed by a 30 - 60minute guided site visit.

The City of Santa Clara's NPS inspectionsare typically not announced The NPS Inspectorintroduces himself and the Program to sitemanagers and proceeds with the assessment.Comments and observations are documented onthe City's NPS Inspection Violation Notice form.Upon completion, the NPS inspector and the sitemanager (or a representative) review thequestions, findings and/or potential areas of

violation. The NPS Inspector usuallyrecommends actions and BMPs that may rectifythe violation and promote compliance with thestormwater objectives. The City of Santa Claradoes not track the number of BMPs alreadyinstalled and maintained on-site. However, theoperation and effectiveness of the existing BMPmay be questioned. When applicable, the NPSInspector may also ask to view management plansor regulatory documentation such as spillresponse plans, copy of NOI or' stormwaterpollution prevention plan.

3.3.2 WaterQuality IndicatorsFor both of the water quality indictors we

evaluated, we used toxicity monitoring and loadcalculations to detect water quality trends andtheir associations with BMP implementation.

Toxicity Testing

Trends in toxicity (and loads monitoring see below) may be used to measure the success ofstorm water management efforts in decreasingpollutant concentrations. Much of the variabilityin urban runoff water quality is random, but somevariability can be explained by the differenthydrologic and antecedent conditions for eachstorm event. Seasonal effects may be moreapparent in semi-arid climates such as those foundin the Southwestern United States, which typicallyhas dry summers and wet winters. Seasonaleffects are often exacerbated by El Nino events orprolonged droughts, which can cause largevariations in seasonal rainfall and runoff.

The existing Walsh Avenue catchmenthydrology and water quality database was used todetermine how much of the variability in loadswas due to changes in water quality versuschanges in total event flow. A large percentage ofthe variability due to changes in event flow wouldindicate that changes in loads might not be asensitive indicator of BMP effectiveness.

Data SourcesThirty eight storm events were monitored

for water quality and stream flow at the catchmentmonitoring station (12) between April 1988 andApril 1995. Toxicity testing was conducted on 11of these events. The monitoring station was alsosampled for five storm events from October 1998


to April 1999. Rainfall has been measuredcontinuously since 1948 in the watershed.Complete data (e.g., both flow, water quality, andrainfall) were only available for 27 of the 43events.

Rainfall data from the San Jose Gage (Alertgage No. 1453, National Weather Service Gage(NWS) # 7821) were used to represent rainfall foreach monitored storm event in the catchment.Flow data were collected at the water qualitymonitoring station using water depth sensorscalibrated to theoretical pipe flow rates using arating curve. A V-notched weir was installed inthe pipe upstream from the manhole to improvethe flow monitoring capabilities for low flows.

Water quality data were collected using flowcomposite samplers programmed to collect adiscrete sample after a given volume of flow(trigger volume) had passed the station. Thetrigger volume was adjusted prior to each eventbased on the forecasted rainfall amounts. Thediscrete samples were pumped into a largecomposite bottle contained within the samplerand transported to the laboratory for analysis.Results from the composite sample analysis arerepresentative of the flow-weighted averageconcentration and are called event meanconcentrations (EMC).

Data AnalYsisWe used simple time-trend linear regression

analysis to determine if toxicity changed duringthe monitoring period. Linear trends wereevaluated for concentrations and loads of totalcopper (Cu), total nickel (Ni), total lead (Pb), totalzinc (2n), and total suspended solids (fSS).Trends for total monitored event flow were alsoexamined. We used Analysis of Variance todetermine if the linear trend was significant.

Methods used to analyze hydrology data werethe same as those described for the ExceedanceFrequency of Water Quality Standards Indicator.Water quality data from Station L2 were recendysummarized as a part of a regional effort to collectdata from the San Francisco Bay Area into a singledatabase (BASMAA 1996). 1998-1999 monitoringdata were added to the database. Descriptivestatistics were calculated for all the data.

51


Non-point Source Loadings

We used the same data and statistical analysis(trend analysis) to evaluate this indicator as wereused to evaluate the Toxicity Testing Indicator(see above). The difficulty with using loads datain trend analysis is that loads can varyconsiderably from event to event and from year toyear. Loads are often more variable than pollutantconcentrations because they include variability inflow as well as variability in concentrations.Without being able to explain some of thisvariability it will be difficult to identify a trend inthe data. In addition, variability in concentrationincreases the uncertainty in estimates of the mean(average) concentrations and loads.

Event runoff coefficients were estimated foreach monitored event by dividing total rainfallvolume by total flow volume. Data for eventswith runoff coefficients> 1.5 were ex~ed todetermine the potential cause of the lack ofagreement between the monitored flow andrainfall data. Events with peak stages greater thanthe pipe diameter were assumed to be surcharging.Such events were excluded from the data analysis.Two additional events were excluded due toknown programming errors in the samplingequipment.

Loads were estimated for each monitoredevent by multiplying the monitored total flowvolume for each event by the event meanconcentration (EMC) for each parameter.

3.4 Application of Indicators in the ProgramArea .

3.4.1 Social IndicatorsThe geographic scope for evaluating the

social indicators included the entire Program area,the portion of Santa Clara County that drains toSan Francisco Bay (Figure 1-1).

Public Attitude Surveys

User Perception

To evaluate these indicators, the Programassessed and compared the results from twopublic opinion surveys. The first survey wasconducted in April 1996, both to establish a .

52

baseline ofattitudes and awareness of Santa ClaraValley residents on issues related to urban runoffpollution, and to gather information to guide thedevelopment of further public education effortsfor the Program and its member agencies. Asecond survey was conducted in 1999 in order todetermine the current levels of knowledge abouturban runoff pollution, and to detect any changesin public awareness or behavior over the pastthree years as a result of the Program's publiceducation "efforts. The survey also includedadditional questions to determine the public'slevel of awareness of watershed issues to aid infocusing a future watershed education andoutreach campaign.

A random digit dial telephone survey wasconducted to sample approximately 850 residents,16 years of age and older. The sample was drawnfrom zip codes representing the agencies of theProgram. To ensure that a sta~stically significantnumber of interviews were conducted in the citiesthroughout the Program, a maximum quota of285 interviews in the City of San Jose wasestablished. Response data were then weighted toreflect the City of San jose's actual percentage ofthe region's population. The survey requiredapproximately 25 minutes for the averagerespondent to complete. A Spanish languageversion of the questionnaire was administered tothose who preferred to respond in Spanish.

Results were analyzed for the County as awhole, by subregion of the County and bypopulation subgroups. The margin of error forthe sample as a whole was 3.4%. The margin oferror for subgroups of the sample was largerdepending upon the size of the subgroup.

As in 1996, several subgroups in the surveysample were formed based on responses tospecific survey questions. These subgroupsincluded:

=> respondents who said they had heard or seensomething about the storm drain system;those who said they had not;

=> those who had seen the Program's stormdrain stencil;

=> those who had witnessed someone dumpingsomething down a storm drain; and thosewho had not.

=> "do-it-yourselfers"

=> respondents who said they engage in variouskinds of behavior that avoid storm drainpollution and those who said they would bewilling to change their behavior to keeppollution out of the storm drain system

=> people who visit the Bay, the ocean, theriver, wetlands, creek trails, reservoirs, andnature centers;

=> and additional subgroups based on income,housing, education, age, gender, ethnicbackground, language preference, and city ofresidence.

Response data collected by the telephoneinterviews were analyzed to:

=> detennine the extent to which attitudes andperceptions are associated withmisinformation or lack of awareness

=> identify messages or motivations that mightbe effective in promoting behavioral changeor participation in urban runoff pollutionprevention programs

=> profile subgroups that appear to have thegreatest levels of awareness in propensity toparticipate, and those that have the lowestlevels to assist in developing strategies fortargeting information to specific audiences.

Public Involvement and Monitoring

To evaluate public involvement andmonitoring we compiled information aboutprograms and projects implemented or fundedindependently or jointly by Program Copermittees. These included urban runoffpollution prevention education, active residentparticipation (e.g. calling for information, assistingin a clean-up, attending a workshop), volunteermonitoring, or other "hands-on" activities(program Annual Reports, Annual Workplans,summaries of Co-permittee Performance Reviews,and ''PIP Updates" (monthly summaries of PublicInvolvement and Participation (PIP) activities)Coyote Creek Riparian Station's (CCRS)Integrated Community Involvement ProgramReport Oune 1997), CCRS' Permanente CreekStreamWatch Program Report (February 1999),


CCRS reports of StreamKeeper activities (August1995 - June 1997), Santa Clara Valley WaterDistrict's (SCVWD's) Annual Reports). With fewexceptions (some school education programs), theactivities described included both an educationaland hands-on component.

Specifically, we tabulated the following:Program Activities (1995-98 fiscal years):

=> Number of calls to the Program'sinformational hotline (800 #).

=> Number of presentations/events by Programstaff and amount of audience reached (whereinformation was available).

=> Number of public involvement andmonitoring organizations/projects funded bythe Program, and number of participants inthese projects (where information wasavailable).

=> Number of participants attending Programfunded weekend activities at the DonEdwards San Francisco Bay NationalWildlife Refuge Environmental EducationCenter at Alviso.

=> Number of activities of the StreamKeepersvolunteer monitoring group funded by theProgram, and number of participantsinvolved in monitoring.

Co-permittee Activities (1997-98 fiscal year):

=> Number and effectiveness of schooleducation programs.

=> Number of creek clean-up events organizedand total number of participants (whereavailable).

=> Number of creek segments adopted in theSanta Clara Valley Water District's (SCVWD)Adopt-a-Creek program.

=> Number of Co-permittees using volunteersto stencil storm drains and the amount ofvolunteer participation (where available).

=> Number of community events whichincluded Program representation andnumbers of residents reached (whereavailable).

53


=> Number ofworkshops, classes, andpresentations by Co-permittee staff whichincluded urban runoff pollution preventionmessages, and numbers of residents reached(where available).

=> Number of Household Hazardous Waste(HHW) drop off events organized and totalresident participation (where available).

=> Number of other activities that includedpublic participation and/or monitoring.

3.5 Project Data Overview

We compiled data from numerous sources toevaluate stormwater indicators in both the CoyoteCreek watershed and the Walsh AvenueCatchment. Table 3-9 lists data collected andcompiled for the SEIDP stormwater indicatorevaluation, including the geographic area coveredby each data set, and the format in which the dataexist. Historical data that existed in electronicformat prior to implementing the SEIDP andwere used in the indicator evaluations are alsolisted in Table 3-9.

54

Table 3-9. Data collected, compiled, and used to evaluate the SEIDP stonnwater indicators.

Data Set andIndicator Number

Physical & BiologicalSam lin LocationsFisheries (12)

Histori

Description

18 locations (100 m reaches) on Coyote Creek where biological and physical data were collected during May, June, and July 1999.

Metrics

B.CD.E.F.G.

Geographic DataErtent FormatCoyote GIS

using Coyote Access l

Coyote Access

Bay Area AccessCoyote, Alameda ExcelCoyote AccessCoyote ExcelCoyote ExcelCoyote ExcelCo ote ExcelCo ote Access l

Coyote Access

Co ote Access!Coyote GIS, Exce

Coyote Access!

GIS,Access

GISGIS,D ase

ase

GIS

Access2TBD

TBDExcel

?WordTables

County WordTables

Column 3indicates the geographic extent covered by each data set (Coyote - Coyote Creek Watershed; Walsh - Walsh Avenue Catchment; Records noting City ofSanJose include the urbanized area ofthe Coyote CreekWatershed but also extend beyond its boundaries; Alameda - Alameda Creek Watershed; Santa Clara County - sampled throughout the Program area; Bay Area - sampled throughout the San Francisco Bay Area).Column 4indicates the data format of each data set (GIS - resides in a geographic information system, ESRI's ArcView and PC ARC!INFO; Access - resides in Microsoft Access relational database and can be linked tothe associated GIS coverage; Excel - resides in a Microsoft Excel spreadsheet; Dbase - resides in a Dbase ftle that can be linked to the associated GIS coverage; Word - Microsoft Word Table). Associated GIScoverages are referenced by subscripts: I - physical and biological sampling locations; 2 - water quality sampling locations.

4. Results and Discussion

4.1 Results in the Coyote Creek Watershed

4.1.1 Physical and Hydrological Indicators

Stream Widening and Downcutting

Successful implementation of this indicatorusing the methods described in Claytor andBrown requires having historical data of sufficientquality for temporal comparisons of creekmorphology. We could only identify a fewrepeated cross-sections along the creek, and thesewere inadequate due to variation in surveymethods, sparse documentation of channel bedelevation, and inadequate location (e.g., locatednear channel structures such as bridges andculverts, or were built as modified channels afterdata were collected). Other potential data sourcesincluding bankfull channel widths, bank-sco~measurements, stream discharge measurements,and aerial photos, were also of insufficient qualityfor temporal comparison.

We implemented an alternative approach,developing a geomorphic stream classificationbased on natural (geology, lithology, elevation,hydrology, and creek planform), andanthropogenic (land use and modified channelcharacteristics) factors. The relative contributionof these factors varied according to position in thewatershed. For example, gradient and planformwas more important in the headwaters; geology,hydrology and channel width were mostimportant in the reaches below Anderson Dam'and channel type and land use were mos~important in the lower creek reaches and thetidally influenced area.Combining both natural and human-relatedgeomorphic influences to Coyote Creek, wedivided Coyote Creek into eight distinctgeomorphic reach types (Figures 4-1 and 4-2,Table 4-1). The upper three reaches occur in ruralareas of the watershed above Anderson Dam. Thelower five reaches are characterized as lowgradient streams that flow through a mixture ofrural and urban lands.

A geomorphic creek classification can beused to:

=> Identify the geomorphic controls thatinfluence channel response to changing flowregime;

=> Provide a means for communicating andpredicting inherent stream processes;

=> Stratify data and reduce variance;

=> Provide a framework for monitoring channelchange over time;

=> Organize data and extrapolate findings toreaches with similar geology, hydrology andland use conditions.

Reaches above Anderson Dam:

=> Reach 1 represents the headwaters of CoyoteCreek downstream to an elevation 880 feetlocated approximately one mile downstre~of Gilroy Hot Springs. This reach ischaracterized as having narrow valleys,moderately steep slopes, low channelsinuosity, and moderate stream gradients.The tributaries to this reach are generallysteep, flow over unstable rock formations andare capable of producing high levels of soilerosion (SCVWD 1978).

=> Reach 2 is the stream segment between Reach1 and Coyote Reservoir. This reach is lowgradient, maintains a wide flood plain, and hashigh sinuosity with a braided planform.

=> Reach 3 is the section of creek betweenCoyote Reservoir and Anderson Dam. Thisreach is characterized as having deep, slowmoving water and high sediment deposition.All three uppermost reaches are susceptible tomass wasting and soil erosion, but a majorityof the sediment is transported and thentrapped behind either of the two dams.

57

Legend

Geomorphic ReachN1

2.1'.:/' 3N4NsN6NT

8

N Surface Waterbodyo Coyote Watershed

Recharge Potential(:-:-:0) Consolidated

o Unconsolidated

Ecoregion Subsections

-BayAatsEast Bay Terraces &Alluvium

..Santa Clara VaDey/{;?'~" Leeward Hills

- Fremont-Uvermore Hills & VaUeys~.Western Diablo Range

~ Diablo Range

'X' Historical Mine

o 5 10 MIas!!!!!!!!!!!!!!!!!Siiiiiiiiiiiiiiiiiiiiiiiii..'

s

santa Clara VaileyOORevised 1125fOOUrban Runoff PoUution Prevention Program

Figure 4-1. Stream classification and hydrological and geological features of Coyote Creek

.,

... --'lHmolUoll...... -.••4 --->.BuraI=:.o:"-- -.~

4000 T----

I

3500Modified Channef

4 ~ ..Natural Channel

3000Gea"orphic ReiI?"..:r~ _

.. __._------------

1500

_2500-Q)

~--§ 2000+:lm>Q)

ill

1000

500

~i:i:

ECII'0 c:c: ..

~'g C. .acC. ~

1II CEc '00 C ~~ Ec: ;:l iii 0

0 .!!! Q. '"C eIl c 0;:l 0 '" c >0'" (,) .!'0 u 0 ClII...! ;:I .. 0u If III .!eIl >...~ '0 o'a 0IIIa. S 0 u >.c

g, .. oct-e (,) ell

0 0 Q.LI. (,)

706560555025 30 35 40 45

IJstance From Mouth (Mles)

2015105

o-J--=::;~:::::;:::::::~-___.__-_r______,-_____r-__r_-_,___-,.______,.-___r_-~___l

o

Figure 4-2. Longitudinal profile, hydrological controls, channel type and land use of Coyote Creek.

Table 4-1. Criteria used to create geomorphic classification of Coyote Creek.

MajorAverage

Average GroundwaterStream

River gradientPlanform

ChanndSubstrate Recharge Lithology

Geomorphic StreamFlow

reach (percentstream

Processes AlterationProcess

slope)width (ft) type

Potential

~

Bedrock,Franciscan

Mass wastingtl >-lU .- sedimentary,~g: 1 2-4 narrow NA Natural boulder, NA and fluvial NAlUll) cobble

volcanic,eroSIon

II) metamorphic

Franciscan Mass wasting~ seJlimerttary•0 2 - 0.75-1 braided NA Natural Cobble, gravel NA and fluvial NA~ volcanic,

~ metamorphiceroSIon

1-<tlcP Mass wasting~ 3 0-2.0 NA NA Impoundfd Silt NA

Miocene marineand fluvial Permanent dams

cP seJlimennII) eroSIon

4 0.4 meandering 450 -Natural Lrgcobble,unconsolidated

Late Quaternary Fluvial erosion Diversion, spreadergravel alluvium. and deposition dams, stormdrains

5 0.25 meandering 193 NatUralSmcobble,

unconsolidatedLate Quaternary Fluvial erosion Spreader dams.

gravel alluvium. and deposition stonndrainss=0....

•t::..0c.cP Late Quaternary Fluvial erosion0 6 023 meandering 107 Natural silt,sand consolidated StormdrainsE alluvium and depositioncP

~cP

II)

7 0.17 channeljzed 133 Modifiedsilt, sand,

consolidatedLate Quaternary Fluvial erosion

Stormdrainsgravel alluvium. and deposition

8 0.08 meandering 33 Natural Silt consolidatedQuaternary bay- Fluvial, coastal Seasonal damfill, silt and clay manne

Rtaches below Anderson Dam:

=> Reach 4 is the section between AndersonDam and a point 0.25 miles upstream ofMetcalf Road, an area known as the CoyoteNarrows. This 8.5-mile section of creek flowsover the Coyote Valley subbasin, which iscomposed of a 10-40 foot layer of alluvialdeposits. This reach is characterized as awide, meandering, natural stream channel,with large cobble and gravel substrate. Thisreach is subjected to water diversion at theCoyote Canal inlet, and the creek is often dewatered for half the year (April- October).This reach is mostly rural, although it containsfour stormwater outfalls that originate fromthe city of Morgan Hill.

=> Reach 5 is an 8.5-mile section of creekbetween the Coyote Narrows and thedownstream edge of the unconsolidatedregion, which is the groundwater rechargearea for the Santa Clara sub-basin. This reachis characterized as a moderately wide,meandering natural channel, withpredominately small cobble and gravelsubstrate. This creek segment receives waterinflow from Coyote Canal, Fisher Creek and16 storm water outfalls. Reach 5 is urbanizedon the western side of the creek, withpredominately rural area to the east. Thereach also contains past gravel mining sites.

=> Reach 6 is a 5.5-mile section of creek betweenthe upstream edge of the consolidated regionand the upstream edge of the modifiedchannel portion of Coyote Creek. This reachis characterized as a moderately wide,meandering natural channeL withpredominately silt and sand substrate. Thissection of creek receives additional flowfrom Upper Silver Creek and 20 stormoutfalls. Reach 6 is surrounded by a wideband of riparian vegetation in an urban area.There are several abandoned gravel miningsites in this reach as well.

=> Reach 7 is a 7.9 mile length of modifiedchannel that runs just upstream of East SantaClara St down to 0.35 mile downstream ofHwy 237. This section of creek has been

CHAPTER FOUR - RESULTS AND DISCUSSION

modified by earth excavation and setbacklevees for flood control. The channel ismoderately wide with low sinuosity, relativelyno flood plain and has a mixture of silt, sandand gravel substrate. This section is alsohighly urbanized and has a narrow band ofriparian vegetation along its banks.Additional water inflow to this reach comesfrom Upper Penitencia and Lower SilverCreeks and 32 storm water outfalls.

=> Reach 8 is the lowest section of Coyote,which is characterized as a tidally influencedstream. The reach begins at the end of themodified reach and flows into the South Bay.This section of creek is narrow, sinuous andhas predominately silt substrate. This sectionreceives additional freshwater inflow fromthe Lower Penitencia Creek watershed

Physical Habitat

Against the backdrop of streamgeomorphology, measurements of physical habitatprovided additional evidence of the complexity ofthe Coyote Creek watershed and factors affectingits habitat value. Coyote Creek benefits from achain of parks that extends mostly through theurbanized area. This protection of land usesimmediately adjacent to the creek buffers thecreek from some urban influences; however,storm drain pipes traverse this buffer area anddischarge directly to the creek.

The influence of dam releases and waterdiversions on flow was revealed during ourimplementation of this indicator. Although thesummer of 1999 was mild compared to recentyears, a warming trend in early July translated intolittle (1 cfs) to no flow at the three sites above thereservoirs (fable 4-2). Streamflow through thelower watershed varied from nearly 50 cfs justbelow the Dam to less than 5 efs in the transitionand upper urban stations, and 10 - 13 cfs in thelower, most urbanized stations. Flow reductionsobserved below the R-3 station were caused bydiversion to old quarry gravel pits via a breachedlevee. Flow was augmented from severaltributaries, resulting in flow increases observed atstations T-3 (from Fisher Creek), and the lowestthree urban stations (from Lower Silver andUpper Penitencia Creeks). The decreases

61


Table 4-2: Gradient and stream flow for Coyote Creeksampling stations.

ReaehID Date Flow Gradient Gradient(ers) (% slope) (% slope)

Field GIS

U-1 6/30/99 12.88 0.23 0.17U-2 7/1/99 12.30 0.23U-3 7/1/99 10.63 0.19U-4 7/2/99 4.00 0.02 0.23U-5 7/6/99 4.06 0.15U-6 7/6/99 2.04* 0.38U-7 7/8/99 3.70 0.82 0.25T-1 7/8/99 4.55 0.32T-2 7/7/99 21.84 0.23T-3 6/28/99 19.95 0.91R-1 7/7/99 9.12 0.16 0040R-2 7/12/99 26.71 0.39R-3 7/12/99 37.38 0.46R-4 7/13/99 38.81 0.08R-5 7/13/99 48.49 1.16R-6 7/16/99 0.83* 0.77 0.75 -1.00

Ref-l 7/16/99 0.00 0.70Ref-2 7/16/99 0.083* 0.91

*Accuracy unknown: below minimum velocitydetection limits for. flow meter

observed at urban sites correspond to a change inlithology; rural and most transition sites are

dominated by porous, gravel substrate whichchanges at the urban sites to high clay-contentsubstrate that precludes groundwater percolation.In addition, stream flow fluctuates daily due to theschedule of regulated flows from Anderson Dam.

Typically, each reach contained four habitatunits, consisting mostly of pool habitat types (byoccurrence), followed by flatwater types. Rifflehabitats were the least common by occurrenceand by length (Figure 4-3).

The distribution of poo~ flatwater, and rifflehabitat types was similar among station groupsand more correlated with streamflow, gradient,and substrate than with increased urbanization.

With several exceptions, pool and flatwaterhabitat units were typically longer and wider thanriffle units. The wetted width of the creek channelgradually increased in the upstream direction,narrowing in two sections before and after thereaches in the urban/non-urban transition zone.Mean habitat unit depth for flatwater and riffleunits was typically < 1.0 ft. Maximum pool depthranged from 1.8 to 4.6 ft. Pool tail crest depthwas typically 1.0 ft. Maximum pool depth wassimilar in all reaches.

The percent of total instream shelter was thegreatest (13 - 22%) in the middle section ofCoyote Creek between T-2 and R-4, with theexception of R-l (Figure 4-4). Sites furtherupstream (R-S, R-6, and Ref-l) had 3 - 4%

Figure 3: Percent of Level II Habitat Types by Length

II!I!I%POOI .%Flatwater a%Rlffle .%DRY I

~II~I- ~ ~ ~I- ,I-

'·.\i ~! '.i~ :·t ,;:.'- ~ ~ ~ -~~ I-~~ ~ 1-

~ 1- ~ ~ ~ ~ I- I-50%

75%

tOO%

25% ~:~ ~ 1- ~ I~ I- I- I- II-II~ ~ ~ 1'- ~ ~ I~

0% I- ...... i! II ...... I '-o-IIlu...-

V-l V-2 V-3 V-4 V-5 V·6 V-7 T-t T·2 T·3 R·l R-2 R·3 R-4 R-5 R-6 Ref-t Ref-2

62

CHAPTER FOUR- RESULTS AND DISCUSSION

Figure 4-4: Percent of Reach with Available Instream Shelter

5

25

~ 20

jjl>-

.e, 15

1'0 10

Jo III

instream shelter, while the urban sites and the firsttransition site (f-1) had 5 - 10% instream shelter.In the urban reaches, small woody debris was themost prevalent type of shelter, followed byterrestrial vegetation and root masses. Artificialdebris such as shopping carts, tires, and auto parts

were much more common in the urban sites(especially in reach U-3) than in the transition andrural reaches, and none were found in the tworeference reaches.

There was also a noticeable increase inturbidity in the downs tream direction. This

Figure 4-5: Reach Substrate Averages by Urbanization Zone

50

i' 40...j>-e 30iiII..II:'0 20i~

Q. 10

0Urban Transition Rural Reference

C% Silt/Clay 44 14 12 0

C%Sand 26 8 13 9

1l!I% Gravel 23 26 22 Z7

IlI%SmCobble 5 29 22 24

.%LgCobble 1 17 23 24

.% Boulder 1 5 7 14

.%Bedrock 0 0 0 2

63


Figure 4-6: Mean Percent Riparian Cover per Reach

• Mean % Deciduous • Mean % Non-deciduous

100

J-~

J

80

60

40

20

o, 0-, , 0-2 'U-3' 04' 0-5' O-S' 0_/' 1-1' 1-2' 1-3' R-, , R-2' rr-'3' R4' R-5' R-S' Ref' Ref'

-1 -2

observation correlates with the trend in dominantsubstrates (Figure 4-5). Substrate compositionalong the longitudinal creek profile changed fromsilt/clay dominant in the lower reaches, to a moreequal distribution of intennediate-size substratefrom approximately the transition sites through R2. The proportion of larger substrate increased inthe upper reaches.

Reflecting the protection afforded by thepark chain, and possibly the encroachment ofriparian vegetation because of the lack of scouringflows since Anderson Dam was constructed in1950, the majority of reaches had total homoncanopy cover of ~ 70% (Figure 4-6), most ofwhich was deciduous (90 - 95%). Percent ripariancanopy was high in the urban stations and tendedto decrease at upstream: sites with severalexceptions (f3, R-1, R-4, and R-5).

The stations above the reservoirs showed alarge and more. consistent decrease in ripariancover compared to reaches below the reservoirs.Because these sites are located within differentecosubsections than sites below the reservoirs,differences in soil type, precipitation and land usesare influencing the distribution and speciescomposition of riparian vegetation. In the urbanand transition zones, 50% of streambankvegetation consisted of deciduous trees, 30 - 35%was brush, and the remainder grass/herb/forb.The proportion of deciduous trees comprising

64

--------_._------------~--~-----

streambank vegetation in the rural and referencezones reflected the decrease in riparian canopycover.

Increased Flood Frequency

Urban growth in the Coyote Creekwatershed has occurred primarily along lowerCoyote Creek, below Anderson reservoir and inthe Upper Penitencia Creek watershed (Figure 47). As shown, population growth andurbanization increased dramatically in the CoyoteCreek watershed starting in the 1950's. Theaccompanying increase in impervious area withinthe watershed

Flood frequency, location, and extent alsohas been highly modified by dam and leveeconstruction (Figure 4-8). The construction ofAnderson Dam (1950) gready reduced thecontribution of flows from the portion of thewatershed above the Dam. Prior to construction,the median, annual peak flow (measured at theEdenvale gage) was 3,200 cfs. Post-construction,the median, annual peak flow was 149 cfs,reflecting a 95% decrease. Recorded flood eventsdid not correlate with peak flows measured belowthe Dam, indicating that flooding incidence due torunoff from the watershed above the urbanizedarea was gready reduced. In addition, the numberof storm events with flow exceeding channelcapacity in Coyote Creek decreased (fable 4-3).


80.0

70.0t:'

E60.0 -III

CD

50.0 ~"CI

40.0 ::c30.0

III

€:::J

20.0

- - - - - - - - - -: - - -- - - - - - - :- -- - - - - - - - -: - - - - - -- - - - ;- - - - -;- - - - -: - - - - - - - - - -.- - - - - - - - - .' - - • - - - - - - - '. - - - - - - - - - .' - - - - - - - - - - I. __ ._. .' _

I , , '. ', ., .--------. -: --------.. ~ ------.--~ --..... ---J{ -.. -..... ---------

__________, L .' • _ .. !. • .' _, , , . '

- - - - - -- .' . - - - - - . - - - '. - --- - - - - - .' - - ..~ -, . , ., .

, , , , "... -. ----. -:. ----.. ---:- -- --... -. -: . ---. -. ---:- . ---. ---.~p - . --....- - - . - - - - - -: - - - - - - . - - - ... - - - - - - - - - -,. - - - - - . - - _ ... -- -- - - -.' ... - - - - - - - - - -

1,000,000 ,-----.....----......-----.------.------.-----,- 100.0

900,000 • R:lpulation - - - - - - - -: - - - - - ... - - ~ . - - - . - - - - ~ - . - - - - .o. - 90.0

800,000 .. - - - .0- . - Urbanized Area

700,000

600,000

500,000

400,000

300,000

200,000

100,0000l_J=::$==~=~:::!~:::~==~~-----i-----l10.0

0.01880 1900 1920 1940 1960 1980 2000

Year

Figure 4-7: Growth in Population and Urbanization in the Coyote Creek Watershed

Sources: Urbanized Area, United States Geological Survey topographic maps, Population, ABAG 1996.

Figures 4-9 and 4-10 show the lower half ofthe Coyote Creek watershed on the 1980 USGStopographic map. Shaded areas in Figure 4-9depict the general locations where floodingoccurred between 1911 and 1973. This period waschosen to represent pre-urbanization because (1)accurate flood records were not available before

this time, and (2) the population and developedarea approximately doubled since then. Becausethe flood event locations were based on anecdotalevidence contained in old newspaper reports, theshaded areas represent only approximate floodlocations. These records may better representflooding that resulted in property damage and

Table 4-3: Number of Storms Exceeding Coyote Creek Channel Capacities based on Flow at the Edenvale Gage

before and after Construction of Anderson Reservoir. Source: SCVWD 1999.

Reach Reach Description Approximate Existing Storm Events Exceeding Channel CapacityReach Length Channel

(feet) Capacity

(efs) Before Anderson After Anderson

1 Old Oakland Road to 4,400 4,500 5 0Penitencia Creek Confluence

2 Maybury Road to US 101 1,600 7,500 10 2

3 Yerba Buena Ave. to US 101 8,500 3,500 15 3

65


Reach 3=8,500 n.Verba Buena Ave.to US 101

Reach 1=4,400 n.Old Oakland Rd. toPenitencia CrkConfluence

Anderson Reserwlr Completed 1950

10,000 ....-----.----------------N-(\oj----lan 0 ,... fE! • ~ l!2 • i2 I ..---:-.,.....".,:-.:-:~

9,000 C2 • ~... .~. ~ i: ~ 8l Reach 2=1,600 ft.~ N ~ ::... Maybury Rd. to US

8,000 101... .. .. .. ....7,000

_ 6,000

-ei 5,000ou: 4,000

~ 3,000

2,000

1,000

o

~ ~ ~ ~ ~ ~ ~ i ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~ ~ ~ C M ~ M M C C ~ C ~ ~ ~ ~ ~ ~M '"' M N M,... ~ ...

Figure 4-8: Annual Peak Flow in Coyote Creek at Edenvale Gage 1(Dashed lines indicate existing channel capacity for the d~noted reaches.Squares at the top of the figure indicate days when flooding occurred.)

occurred in developed areas as well as more recentflooding. Flooding in undeveloped or agriculturalareas may have been under-reported. Figure 4-10identifies the locations where flooding hasoccurred from 1978 to 1998, during the period ofincreased urbanization. These flood locationsrepresent a combination of large flood events andlocalized flooding events.

The lack of historic data and the effect offlood control facilities confounded our ability toascertain how urbanization has affected floodingfrequency along the Coyote Creek. The.FEMAand SCVWD maps indicated that the AlVlso andEvergreen neighborhoods flooded before andafter the period of increased urbanization. Themost perceptible correlation between urbanizationand flooding was near the confluence ofPenitencia and Coyote Creeks. The PenitenciaCreek watershed was urbanized prior to thedramatic growth in urbanization of surroundingareas (Figure 4-11). Therefore, flood records,even those anecdotal, are likely to be relativelycomplete for this area. Flood events for this areaincluded one in 1911 (Figure 4-12), and five from1982 to 1998. Annual peak flows, on Penitencia

Creek closely correlated with the flood events of1982, 1983, 1986, and 1995 (in contrast to thesimilar comparison for Coyote Creek below theDam).

Relationship Between Flooding and lAnd Use ChangesAs discussed above, the construction of

Anderson Dam gready reduced the flows into theurbanized area from the upper half of the CoyoteCreek watershed and increased the capacity of thecreek to accept increased flows from theurbanized area. Therefore, it is possible that evenwith increased flows from the urbanized areas thatthe flooding frequency of Coyote Creek hasdecreased from historic levels.

Comparison of the eight years of flow dataavailable below the urbanized area of CoyoteCreek to the flow data available for the relativelynonurbanized areas of Penitencia and CoyoteCreeks showed that the urbanized areacontributed more flow per unit area than thenonurbanized areas for four storms (about 62%of all flows of similar magnitude) during thisperiod (Figure 4-13).

66

o\l

Flow Gage

Rain Gage

... -.:\\l,JiJJ-'"PI.... '

... . ~ .

\'1..,.t r.·.. ;".

I'·

I .,-'

.............

l-I

'. ' ' .....~. .:

\--\,

, -,\

\"

I : \~l' \ j ,

fU~ '.\:. ItO,.

,.",li

..

'-

''"', "'-.,

8JRS Greiner Woodward fClyddJ51981183NAOO-03000/021799/oos

Project No.51981183NA Coyote Creek FLOODING EVENTS FOR THE

COYOTE CREEK WATERSHED:Locations where flooding has occurred

from 1978 to 1998

Figure4-10

,\ \-..-'

"\C·'\

I \

r \... .\

I_f, .: ,'-''''l'"''.

' .. \. \ ~

\

~1'V .. ;'".

",'"

;

1952

1(:1"·

......\

..~

• I.i~.

···_·-- .....1~.,. ~./

'r-.:S,,;~j, ""~tf::)•...•l·,"'r."T:'l..-

'::" ,14

. ",

..,i.-.

,-'/"'" (l""~1" " ~t'I~'

1'.• ~'1

',11.1-", , ....

..~.

I.........r,

Ir

',"'" -~ ..

'\'- .:~-.....

. '\ ""'-~",', ~ -

·1

I

J."

~ : .... '·1 ,

Coyote CreekWatershed Boundary

Flow Gage

Rain Gage

\.

URS Greiner Woodward Clyde

Project No.51981183NA Coyote Creek FLOODING EVENTS FOR THE

COYOTE CREEK WATERSHED:The general locations where flooding has occurred from

1911 to 1973

Figure4-9

51981183NAOO-03000/021799/aos


4.00,...-------------------------~

3.50

Q) 3.00'E&2.50I/)-Ctl 2.00Q)

~c 1.50Ctl

~ 1.00

0.50

20001990198019701960Year

195019401930

0.00 +--.--+----+1"'=----+-'---+---+----+----+----11920

Figure 4-11: Urban Growth in Penitencia Creek Watershed

Temperature MonitoringSpatial trends in daily temperatures reflected

inter-station differences in surrounding land usesand environment (Figure 4-14). Daily meantemperatures at station R-5 were typically about30 - 50% lower than at all other stations. Station

R-5 is located in the rural region of the watershedjust downstream of Anderson Dam (Figure 3-5)and is influenced by cool water that is routinelyreleased during summer months from the bottomof the reservoir. This station is also well shaded.Stations R-5 and TS-5, located in the transition

3,000

C'\I M coLO

2,500 S!!2•• S!!2 • S!!2 Q! •.... 0 C'\IQ!~ ~ :s::::

C") .... C'\I ....J? 2,000~

~ 1,5000u::.:.:.lU 1,000Q)

CL.

500

0IX) C'\I M ~ .... Ol 0 M LO .... Ol .... C") LO .... IX) 0 C'\I C") LO!Q co S2 S2 S2 S2 t::: t::: t::: t::: t::: S!!2 S!!2 S!!2 S!!2 S!!2 Q! Q! Q! ~

~Ol IX) C") .... LO ~ IX) .... ~ C'\I C") co S!!2 C") .... .... ~ co ~

~ :s:::: ~ ~ ~ ~ C'\I :s:::: ~ .... ~ :s:::: ~ C'\I :s:::: :s:::: ~ :s:::: ~ ....co M C'\I .... .... .... .... C") C'\I M .... C'\I .... LO C'\I C'\I:s::::

Year0....Figure 4-12: Annual Peak flows on Penitencia Creek at the Penitencia Gage and Dates of Flood Events

(The squares at the top of the Figure indicate the days when flooding has occurred)

69


Un-urbanized AreaFlow per Square Mile from

10 20 30 40Flow per square mile In Coyote Creek below the Urbanized area for

Years 1988-1995

50

454035

3025

2015105

oo

•• • ••

•

50

Figure 4-13. Annual peak flows> 500 ds per unit area for urbanized and nonurbanized portions of theCoyote Creek Watershed. The 500 ds flow threshold was chosen because it represented the smallest annualpeak flow 1988 -1995. The 45 degree line represents the point where the unit peak flow (in cubic feet persecond per square mile) from the nonurbanized portion of the watershed is equal to the unit peak flow fromthe urbanized portion of the watershed. For storms> 1000 ds, points on the graph below the line representstorms where the urbanized area contributed more flow per unit area than the nonurbanized areas.

and least-impacted reference regions of thewatershed, respectively, exhibited slighdy lowerdaily mean temperatures (1 - 2 oC) than stations1 and 2 located within the urbanized region of thewatershed. Temperature increased 5 - 7 oC overthe approximately 8 river miles from station R-5to T-3, the most dramatic change between any ofthe stations below the reservoirs. Such warmingis likely caused by the paucity of riparianvegetation along most of this stream segment thatincreases exposure to solar radiation. Station TS5, located in the hillslopes above the reservoirs isalso less influenced by solar radiation due to themicroclimate created by adjacent hillslope shadingand entrapped fog, as well as the presence ofriparian vegetation. Daily minimum temperatureswere lower and occurred later during the day atStation TS-5 than all other stations, except StationR-5. The relatively higher temperatures at stationsTS-1 and TS-2 may be attributed to attenuatedflows and to increased exposure to solar radiationcaused by increases in channel width. Despite thehigh shade-influence at and direcdy upstream ofStation TS-2, it consistendy exhibited the highestwater temperatures and the largest range ofdiurnal temperature over the longest period of

70

time. This is also pardy caused by the upstreampresence of several wide, minimally-shaded, inchannel percolation ponds (Habitat RestorationGroup 1995). Slighdy lower daily watertemperatures at Station TS-1 may be attriibuted toaugmented, cool flows from Silver Creek that aremaintained by riparian cover in its upper reaches.

Stream temperatures were closely correlatedwith air temperatures recorded in the cities ofMorgan Hill and San Jose. This is commonlyfound and has led to the use of air temperaturesto predict stream temperatures (pilgrim et al.1998, Crisp 1990, Crisp and Howson 1982).

The quantity and quality of historicaltemperature data for Coyote Creek precludedquantitative comparison with our data. Of sevenhistorical data sets identified (Aceituno et al. 1973,Pitt and Bozeman 1982, Sylvester 1986, Leidy1981, Habitat Restoration Group 1995, H.T.Harvey et al. 1995, SCVWD 1999), only threereported adequate information to determine datacollection methods, and most included fewlocations. Sylvester (1986) reported data (1979 1981) for two sampling locations, one of whichwas, like station R-5, highly influenced byreservoir releases. The Habitat Restoration

Figure 4-14. Daily stream temperature recorded at 5 stations along Coyote Creek

-T1

-1'2

1'3

T4~-T5

'-\. "-"\/,,,

9/1/1999

,,U/.IIIl••••~II"••IIIlI.aD'•••·.II...····c:lIl,,·.,U••dII.'",,,

8/1/1999

Stream Temperature: Daily Means

,,.... IU1•••II IUI"••••••II~DIl.If..

,

7/1/1999

---~-----------:--'------------i-----'--,, ,, ,, ,, ,, ,, ,

a.

30 -------

III 25::2'Ill

~20III

GIl!!til

l!l 15

1I1!·····'II··~II.'

106/1/1999

b, Stream Temperature: Daily Minima

-T1

-1'21'3

T4--T5

9/1/19998/1/19997/1/1999

.....C ••II.,a.III1••lIe.~<lIl!Il!1II1IlU~II••• 1!

10 +----------t-----------..,'------------t-----------l6/1/1999

15 +--..,t:-:e.---~-----~-----__:,--------i1:11._ 1I1la'•••• n.l!t~ll"~.......0 a·••• IHIClIIJ: ••lI

a",,,p.

25

30------------ -----;-----------;--------------~------------, , ,, , ,, , ,, , ,, , ,, , ,

Stream Temperature: Daily Maxima

-T1

-1'2

1'3

T4~-T5

Dall D'; r.l'Inr:" ,,:< liD _ ..,~;,,.11~ .. nCII:lll1nl:!n """l .... ::::IClt;l=Il~

,

9/1/19998/1/19997/1/1999

---.-------------------,-------------r---------------, , I, : I

: I, I

C.

30

III 25::2'1lIGi0

20IIIGIl!!01

~ 15

106/1/1999

IEAttl.. IE~ u- At I

2121191 llI22I92 4/2"''' 1\123I9DOltl

Llne.r Fit IT88 ••19.14.8 .1".,·7 O,t,

Surnmarv 01 Fit II

section (-4 river miles) of Coyote Creek. Leidy(1981) cover~d the greatest geographic extent,but measured temperature as single, spot valuesusing a handheld thermometer.

Despite these limitations, Sylvester (1986)and Habitat Restoration Group (1995)supported the downstream warming trend weobserved for sites below the reservoirs and rangeof temperatures we observed at stations T-3 andR-S. Our baseline study (pitt and Bozeman1982) reported diurnal temperature rangessi.mila.r to those we observed. Because theyreported stream temperatures as single values,they likely measured using spot sampling. Astheir methods could not be confirmed from thereport, no further site-specific comparisons weremade.

, Ratlo0.1184

ProP'O.CODI

II

I

Paramotar EDlImates

70

Analnll 01 Variance IIourtt Dr Sum of Squar.. MllIn lqulf.Madill I 35817.8 38877.0Enor 24 1191lG48.a 49948.1CTOIoI 20 1234826.7

TCu (u1l111 By Date

TSS By Dala1000

aoo

~

~400

:lOO

01123/89

Parameter !ltlmatea II

Parlmeler EDtlmalal II

TZn (uRII) By Date I

4.1.2 WaterQualifY Indicators

Water Quality Constituent Monitoring

During storm events, more than 80% oftotal metal concentrations in Coyote Creek areassociated with particulates; i.e., concentrationsof total metals vary with concentrations of totalsuspended solids (TSS).

Total copper and zinc concentrationsdecreased during the monitoring period, butthese trends were significant only at the 69% and87% confidence levels, respectively (Figure 415). Particulate copper and zinc concentrationswere more significant' than total metalconcentrations (96% and 90% confidence level,respectively). The relationship between time andparticulate metal concentrations explained 18%and 14% of the variations in concentration forthe two metals, respectively.

Strong positive and negative correlations (r> 0.5, i.e., or 25% of the variability is explainedby the parameter) between total and dissolvedcopper, lead, nickel, zinc, total suspended solidsand hydrology and rainfall were observed forseveral parameters (Tables 4-4 and 4-5). Ingeneral, positive correlations were found fortotal concentrations and negative correlations fordissolved and particulate concentrations. Thisdichotomy could be explained by the sorption ofdissolved constituents by TSS. Total suspendedsolids were positively correlated with indicators

'A,Uo1.01111

ProP'0.3131

'A,lto2.4312

,,0000r0.1321

~.: \ :

....... -:......... .'L........ _;_ .. _ - ......~.........-_ -: - :........ '

-. ........

DO

250"

10

~ co

~ 30·

20

AnalYIII 01 Variance Ihurol Dr lum 01 Iq\l.,.. "un tc;u."Mild" I 8801.928 5801.93Enor 28 84153.112 238M7CTotlll 24 60558.0c0

o+--'-,,--"'-or---'-...----j)123/89 2121191 9122192 4124/04 11123195

Oat.

Linear Fit Ileu (UIrIl) • 191.181 ~ lS,ca,·e oato

SummarY 01 Fit II

AnalYll1 01 Variance IIClWrcI Dr"'m cd Squa," M.,n IqUI'.Moct" 1 211.8143 211.874ErrOt 24 4189.3661 1996D1CTotlll 25 8001.2404

0+--"--'--"'---"'-""--/)123/81 2121/91 9122192 4124194 11121195

D,te

Linear Fit ITZn_turm-. 1008.06 .. 3.211.7 Oalo

SUmmary 01 Fit II

IE Rttlng IE '* Un.., Fa I

IEFitting IE -=:-: u- At I

72

Figure 4-15. Time Trend OfTotal Suspended Solids,

Total Copper, and Total Zinc in Coyote Creek.


Table 4-4. Positive Pearson Correlationsfour week flow, total rain to date) and with totalevent flow, suggesting that large or wet eventscause conditions that increased sediment transportor erosion. Total copper was positively correlatedwith average and peak rainfall intensity suggestinglarger more energetic stonns cause more copperto be flushed from urban surfaces. On the otherhand, dissolved copper and dissolved zinc showedstrong negative correlations with peak, averageand total event flow, suggesting either dilution orincreased sorption of dissolved copper due to thehigher concentration of suspended solids duringlarge flow events.

Particulate copper and particulate zinc werenegatively correlated with indicators of theamount of flushing (total rain and flow to date,previous two and four week flow) indicatingcopper is seasonally flushed from urban surfacesand diluted by background concentrations foundin soils and sediments.

Including flow volume in our analyses ofwater quality constituents was important forinterpreting monitoring results. The 1'$S-flowregression showed that 95% of variation in 1'$Swas explained by changes in flow volume in theantecedent 2 - 4 weeks, by peak and average flowrates, and by the number of dry period days priorto the event (Figure 4-16). Because TSS washighly correlated with flow parameters it was usedas a substitute for flow indicators and included asan additional factor in the rainfall regression. Thebest stepwise regression model for total metalsexplained 57%, 45%, 95%, and 58% of the'variations in copper, lead, nicke~ and zinc,respectively. Total nickel variations are explainedby the same variables used to explain thevariations in TSS, providing strong evidence thattotal nickel concentrations are driven by erosion.For particulate metals the best model explained66%, 23%, 59% of the variations in copper, lead,and zinc, respectively. Model relationships fordissolved metals explained 38%, 15%, and 49% ofthe variations in copper, lead, and zinc,respectively.

Results from ANOVAs demonstrated thatmean total copper concentrations in dry winterswere not significandy different than total copperconcentrations in wet winters. Mean total copperconcentrations differed by less than ten percent.The lack of apparent differences may be explained

dH dr 1QaliW

Table 4-5. Negative Pearson Correlations

drlWater Quality and Hvc o oIlYWater Quality Hydrology Correlation

Parameter Parameter CoefficientDissolved Peak Event -0.648

Coooer FlowDissolved Average Event -0.586Copper Flow

Dissolved Total Event -0.5568Copper Flow

Particulate Total Rain to -0.8645Coooer Date

Particulate Previous Two -0.5645Copoer Week Flow

Particulate Previous Four -0.5682Copper Week Flow

Particulate Total Seasonal -0.6798Copoer Flow to Date

Particulate Lead Peak Event -0.5025Flow

Particulate Lead Total Event -0.6428Flow

Dissolved Zinc Previous Two -0.647Week Flow

Particulate Zinc Total Rain to -0.8262Date

Particulate Zinc Total Seasonal -0.6361Flow to Date

ater u ltv an lyt OO2V

Water Quality Hydrology CorrelationParameter Parameter Coefficient

TSS Total Rain to 0.552Date

TSS Total Event 0.581Flow

TSS Previous Two 0.647Week Flow

TSS Previous Four 0.5009Week Flow

Total Copper Average 0.740Intensity(In/hour)

Total Copper Peak Intensity 0.549(in/hour)

Total Lead Previous Two 0.660Week Flow

Total Lead Previous Four 0.621Week Flow

Total Lead Total Seasonal 0.549Flow to Date

Total Nickel Total Event 0.6044Flow

73


Figure 4J.16. Predicted verses Measured TSS

Exceedance Frequencies ofWater QualityStandards

Coyote Creek monitoring data are comparedto total and dissolved water quality criteria inTable 4-6. The table shows the number ofexceedances versus the total number of sampledevents. Few parameters exceeded chronic waterquality criteria and even fewer exceeded acutecriteria. The highlighted cells in the table arethose showing at least one exceedance of thecriterion.

Results from the SYNOP analysis indicate

much of a difference between monitoring periodscould be reliably detected for a given number ofmonitored events. Adding a hydrology factor tothe water quality model increased dle statisticalpower of the test and resulted in requiring fewersampling events (30 storm events, 15 permonitoring period versus 50 monitoring events,25 per period without the hydrology factor) todetect a 40% difference in metal concentrations(demonstrated using copper) (Figures 4-17 and 418).

•........................

........

•........................•

200 400 800 800 1000

Meaaured TSS Cancenlrallon (mgll)

1200

1000

i 800

I 800

~

I 400

200

aa

by. the lack of differences in hydrologic variablesdetermined to be important through the step-wiseregression model between the monitored events .in the wet and dry winters. .

Results from the comparison between wetand dry winters were used to determine how

1

0.9

0.8

0.7

.. 0.6~o 0.5~

0.4

0.3

0.2

0.1

oo....

~10%Change

--20% Change

""""""30% Change

~40%Change

---- 50% Change

8....Number of Samples

Figure 4-17. Power Analysis Results For Total Copper Changes Using ANOVA Analysis

74


1

0.9

0.8

0.7

0.6

0.5

0.4

03

0.2

0.1

oo..... o

N

Number ofSamples

o0\

oo.....

--+- 10% Change

--- 20% Change

......- 30% Change

~40%Change

.;E- 50% Change

Figure 4-18. Power Analysis Results For Total Copper ChangesUsing ANCOVA Analysis with Hydrology Factors

median storm duration of about 19 hours with75% of the storms less than about 35 hours induration. This implies that acute criteria would bemost applicable. However, chronic criteria mightalso be relevant for the larger storms.

An overall decrease U1 the number ofexceedances year-to-year suggests animprovement in water quality, especially forparticulate metals concentrations in suspendedsolids. Apparent changes in water quality could be

Table 4-6. Summary of Exceedance of Water Quality Standards in Coyote Creek Runoff

0/27 0/27

Notes:Total Water Quility Objectives are in Cilifomia's Water Quility Control Plan for the San Francisco Bay Basin, and reference EPA Federal Register 40CFR Part 131 (d)(10)(it), December, 1992 Dissolved Water Quality Criteria are from EPA Federal Register 40 CFR Part 131 (d)(10)(it), May 4,1995.0/5: Number of exceedances/Total Number of Sampled Events Shading indicates an exceedance. • The chronic WQO for total mercury is based on preventingfish from accumulating levels of mercury concentrations that could be hazardous to human health. However, it is not certain whether concentrations inwaterways during storm events persist long enough to allow accumulation in fish.

75


June October

12 12

0 0

... 0

8 ... 8 0

... 0

0.. .• ....cl

t~.Cl

~ 4 I:\.t 4

0 0 ~g...

-4 -4~ u:- ~ ... ";\ ";\ u:- N ~ u:- ~ ';' ";\ ";\ u:- N

~ ~ ~ I-< I-< ~ ~ "" "" ~ ~ ~ I-< I-< ~ ~ "" ""I:.l I:.l I:.l I:.l~ ~ Site

ll'l ~Site ...... ......

"? "?ll'l ll'l

Figure 4-19. Sediment Particle Size Distributions at each Site. (Horizontal Bar=Median, Upper Box Hinge=ThirdQuartile, Lower Box Hinge=First Quartile, Upper Whisker=Upper Hinge-(1.S*(Median-Upper Hinge), Lower

Whisker=Lower Hinge-(1.S*(Median-Lower Hinge), *=Outside Value, o=Very Outside Value)

due to retumto average wet annual rainfallpatterns that could have decreased metalsconcentrations in runoff and local streams.

Sediment Characteristics and Contamination

Two spatial differences in sedimentcharacteristics and pollutant concentrations wereapparent. First, particle size differed along theurbanization gradient, with coarser sedimentfound in the upstream reaches, and finer sediment

. found in the downstream reaches (with theexception of R5 Cochran, which had the highestvariance between surveys, and R3 Miramonte,which had the finest sediments of all sites). SeeFigure 4-19. Second, distribution of metalsconcentrations for cadmium and lead indicatedthat concentrations increased with urbanization.See Figure 4-20 Lowest concentrations atreference sites were partly attributed to coarserparticle size found at these sites.

However, there were no seasonal differencesobserved between metals concentrations, and fewtemporal differences; most concentrations did notchange over the 20-year period betWeen sampling.MercurY was an exception; sedimentconcentrations apparently decreased substantiallyover this period. Figure 4-21 compares the medianand range of concentrations of the contemporaryand historical data sets for each of the six tracemetals.

76

Arsenic Copper

18 OJun.8ll 180

18 .Ocl~ 140

14 120

12~ ~ 100

1 '0 1 10

t 8 t 10

40420

0

11-4 IJ.4 IH T-t T-J R-J R-5 R_1 REF... U-4 IJ.4 U-7 T·1 T-3 R-3 R-5 R.-EF1 REF~

SampllngS_ SampllngS_

Cadmium Lead

1.4 OJun.8ll 1800_

l!IOcl~ 180 .Ocl~1.2

140

120~ ~1 0.8 "i' 100

co

t 0.8 t so80

0.440

0.2 20

0

U-4 U05 U-7 T·1 T-3 R-3 R05 R_EF1 REF~ U-4 U-5 U-7 T·1 T-3 R-3 R-5 R.-EF1 REF..2

SampllngS_ SampllngS_

Chromium Mercury

R05 R_F1 REF..2T·1 T-3 R-3SampllngS_

U-7U05U-4

0_

1~0cl~

--

i- I--

- I-- -r Hl-r •c- I- r-. ...... ....

0.45

0.4

0.35

0.3~'l 0.25co~ 0.2

l 0.15

0.1

0.05

oR05 R.-EF1 REF~T·1 T-3 R-3

BampllngBnes

U-7U-4

500 ,------------------------i0_ .Ocl~

400_1_---------------_;:I5O+-----------------i

~300-I-----------------;

i : +--------,=-----i150 .Lr-""_--'

100

50

o+-L---........-'--.-....

Figure 4·20. Seasonal and Spatial Comparison of Trace Metal Concentrations in Coyote Creek along a Gradient from Downstream Urban Sites toUpstream Reference Sites.

Arsenic Chromium

50~ 40

" 30CDa 20E 10

o

14

3 4

4

6 4 T 4

I ... I - I - -OPB

.WERF

800 .,.-----.."..=r------------,~ 600 +------1

"~ 400 +---=------1CDE 200 +--+~....___---1

0+-11.-..

DPB

.WERF

Urban Transition Rural Ref Urban Transition Rural Ref

OPB

.WERF

Lead

Ref

Mercury

Cadmium3 OPB 14

1.5 6 200

~.WERF

~150

" "CD CD 100a 0.5 ~4E E 50

3 4 4

0,

Urban Transition Rural Ref Urban Transition Rural

Copper

.3

HI 1 r -,I 6 ~4 I I

150 4 15~ OPB ~ 12

" 100 " 9CD 4 .WERF CD~ .¥

6CD 50 -=E E 30, 0

Urban Transition Rural Ref Urban Transition Rural Ref

OPB

.WERF

Figure 4-21. Comparison of Sediment Trace Metal Concentration Measured in the Present Study (WERF) with BaselineConcentrations Reported by Pitt and Bozeman (PB). Bars represent the median concentrations, error bars indicate the range ofconcentrations reported at each site and the numbers indicate the numbers of measurements in each study. .


Figure 4-22. Percent of Native Fish Species in Coyote Creek 1980 and 1999

120 ...----------------------------,

100 • • •

O+---+--!--+--+-+---+--!--+--+-+---I--!--+--+-+---+--!

Sampling Station

Biological Indicators

Fish Assemblages

We identified a total of 21 fish species, ten ofwhich were native. Comparison of fisheriesmetrics indicated that urban influence extendedfarther upstream from where it had been whenthe baseline study (pitt and Bozeman 1982) wasconducted. Statistical comparisons were moresignificant when the two downstream transitionstations were grouped with the urban stations, andthe upstream transition station was grouped withthe rural stations (Figure 4-22). This resultcorrelates with the increase in the number ofstormdrain outfalls in the transition zone since thelate 1970s. Metrics for number of native species,number of native individuals, percent of cyprinidand centrarchid species, percent of cyprinid andcentrarchid individuals, and total biomass werefound to have statistically significant differences5

5 The statistical significance of a result is the probabilitythat the observed relationship (e.g., between variables)or a difference (e.g., between means) in a sampleoccurred by pure chance. Specifically, the p-valuerepresents the probability of error that is involved inaccepting our observed result as valid, that is, is

(a. = 0.05) among the urban, rural, and referencestation groupings (fable 4-7).

Fish assemblages did not differ amongstation groups by sampling period in 1999 (Figure4-23). The total number of rainbow troutsampled, however, declined with subsequentsampling periods, suggesting that individuals maymove to other creek reaches or be harder to catch.Some individuals had par marks, indicating thatthey may be steelhead (i.e. anadromous). Thus,the decline in the number of fish capturedbetween spring and late-summer may also reflectseasonal migration to San Francisco Bay.

Rainbow trout were found at 50% of SEIDPsampling stations (fable 4-8), including urban,rural, and reference stations. Pitt and Bozeman(1982) found them only at the most upstreamreference station. These results could indicate animprovement in water and/or habitat quality, andcould also reflect the influence of interannual

"representative of the population." For example, a pvalue of .05 (i.e.,1/20) indicates that there is a 5%probability that the relation between the variablesfound in our sample is due to chance alone (StatSoft,Inc. 1999).

79


Table 4-7. Comparison of Metrics in Grouped Stations

uG=;'u ::so

'£1 lcrru:::s Title Results

Q.,!:!,

la Number of reference> urban 0.0032

INative Species reference>rural 0.0151

Ib lNumberof reference>rural 0.0127

lNative Individuals reference>urban 0.0001

rural > urban 0.0001

~a Percent of Cyprinid urban > reference 0.0025

~nd Centrarchid Soecies urban> rural 0.0001

~b Percent of Cyprinid urban> rural 0.0001

~d Centrarchid Individuals urban > reference 0.0001

~b Number of Insectivore rural > reference 0.0001

and Invertivore Individuals urban > reference 0.0001

13 Biomass. 1999 < introduced fish in rural 0.0039

1999 > native biomass in urban 0.0137

precipitation and flow on community assemblagestructure and composition (see below).

As shown in Figure 3-5, Pitt and Bozeman(1982) placed the "Urban Boundary" between thestations we have designated as U-7 and T-1.Figure 4-23 suggests that the upstream movementof this urban boundary between 1980 and 1999may be reflected in the proportion of native

species.Analysis of the entire record of

historical fisheries assemblages within CoyoteCreek (Buchan et aI. 1999) indicated that theassemblages identified in 1999 were similarto those observed since the construction ofAnderson Dam in 1950 (Figure 4-24).Species found at SEIDP urban and transitionsites included similar native species but hadthree fewer nonnative species than moststudies found after 1950. Species found atSEIDP rural sites below Anderson Damincluded three fewer of the commonlyfound, native species (Hitch, Laviniarymmetn'cus; Sacramento pikeminnow,P!ychocheilus grandis; Sacramento blackfish,Orthodon microlepidotus), one additional nativespecies that had not been observed in thesereaches since 1925 (rule perch, Hysterocapustrask/i), five fewer nonnative species(Common carp, Cyprinis capio; Goldfish,

Carassius auratus, Mosquitofish, Gambusia affinis;Bluegill sunfish, 140mis macrochirns; Greensunfish, L ryanellus) , and one nonnative speciesthat had not been observed by any previous study(Fathead minnow, Pimephalespromelas).

It is likely that natural hydrologic fluctuationhas had a considerable influence on thedifferences observed in fisheries assemblages

,

Figure 4-23. 1999 Coyote Creek Native and Non-native Fish Species

14

12

11~CIl

'0 6] 4

~ 2o

.99 Non-native

.99 Native

Sample Station

d' 1999 S Ii ECT bl 4-8 Rai b Ta e now rout apture an amp m ventsMonth V-l U-2 U-3 U-6 T-2 R-4 R-6 Ref-1 Ref-2 TotalMav 1 2 1 1 1 2 3 2 11 23June 2 1 2 4 9 19Sept. 2 2 4

80


Figure 4-24. Native and Non-native Fish Species from 10 Studies

30 -,----------------------------------,

25

OIl

'0 20

~'0 15

j§ 10Z

5

o

• Non-native

III Native

78 82 84 '89 90 94 96 97/98 98 '99

Study Year

between the baseline and the 1999 study.Comparison of SEIDP and the baseline fisheriesassemblages with the three antecedent years ofstreamflow data indicated that peak flows prior tothe 1998-1999 sampling were 10 - 50 timesgreater than peak flows prior to the 1978-1979sampling (Figure 4-25). High magnitude flowsinfluence biological community composition andmay be responsible for reducing the relativeproportion of nonnative species which, unlikenative species, are not adapted to such dramatic

hydrologic fluctuation (R. Leidy, pers. Comm.).Thus, it is likely that natural hydrologic fluctuationhad a considerable influence on the differencesobserved in fisheries assemblages between the twosampling periods.

Several fisheries metrics were significandyassociated with cumulative percentimperviousness both at the subwatershed andriparian corridor scale (fable 4-9). Metrics basedon species, rather than individual counts, weremost strongly associated with cumulative percent

,f

Table 4-9. Significant (alpha =0.05) associations between percent imperviousness (cumulative (or subwatershed scale,and non-cumulative (or riparian corridor scale) and metrics calculated (or fisheries data sampled in May, June, andSeptember, 2000 in Coyote Creek (rom stations Ut- Refl. Associations were tested using simple linear regression; anasterisk indicates associations tested using logistic regression. Results are sorted in descending order based on thecoefficient o( determination, r, at the subwatershed scale. Blank ceUs indicate non-significant results.

Metrics r2 P-value r2 P-valueSubwatershed Riparian Corridor

# Tolerant Species .65 <0.0001 .38 0.0069% Tolerant Species* .59 0.0002# Nonnative Species .50 0.0009 .33 0.0124% Native Species* .43 0.0029# Diseased Individuals .33 0.0127Total # Species .31 0.0166 .33 0.0121# Native Individuals .26 0.0327

81


imperviousness. The response to percentimperviousness measured by the four species-levelmetries that were strongly associated6 (0.43 ~ r2 S0.65) with cumulative percent imperviousness wascontinuous; however, the response rate washighest between 8 and 11% (Figure 4-26). Asimilat, but less clear pattern existed for the otherthree fisheries metrics that were significantlyassociated with cumulative percentimperviousness (Figure 4-26). In general, as thecumulative percent subwatershed imperviousnessincreased, the proportions of nonnative species,total number of species, and diseased individualsincreased, and the proportion of tolerant speciesdecreased.

Dissolved oxygen (DO) concentrations at allfive continuous monitoring station locations weregreater than five milligrams per liter except at thewater quality monitoring location TS-l (Figure 35) just upstream of station U-5. At this site, DOrepeatedly dropped below 5 mg/L (Figure 4-27).Between September 9 and 16, 1999 the DOremained below 5 mg/L and nearly dropped tozero. This may have caused a fish kill, evidencedby observations during the September samplingevent at station U-1 of 26 dead carp and one deadcatfish.

Macroinvertebrate Assemblages

We identified a total of 44 taxa, identified tothe tribe, subfamily, or genus, of which nine wereclassified as tolerant and 35 as intolerant (Ode1999 Seasonal comparisons for taxa sampledwithin 1999 did not identify significant differencesamong station groups. However, spatialcomparisons among station groupings revealedsignificant differences .for several metrics. Thetotal number of taxa (Figure 4-28) and the totalnumber of EPT taxa were greater at the referencestations than at the rural and urban stations. Thepercent of individual EPT insects was similar atthe reference and rural stations, but higher than atthe urban stations (Figure 4-29). Therefore, the

6 The R-square value is an indicator of how well themodel fits the data, i.e., the "strength" or "magnitude"of the rdationship. An R-square value close to 1.0indicates that we have accounted for almost all of thevariability with the variables specified in the model.

82

total· number of taxa was useful fo.r describingmaeroinvertebrate diversity, but not populationcomposition.

The reference stations had the greatestnumber and percent of low tolerance taxa (Figure4-30), as well as the greatest percent of lowtolerance individuals. The rural stations had morelow tolerance taxa and individuals than the urban

.stations. Evaluating low tolerance taxa appearedto be a more discriminating measure thanevaluating high tolerance taxa because lowtolerance taxa only exist where the water quality issuitable but high-tolerance taxa are ubiquitous(albeit in varying densities). Since the tolerancemetric directly relates to water quality it should beincluded in future macroinvertebrate assemblageanalyses.

Comparison to the baseline study revealedthat the mean total number of taxa per stationmore than doubled (29 per station grouping vs.12) since 1978-1979. Several differences betweenthese studies may account for some of this: the1999 data were identified to a higher taxonomiclevel, taxonomy has changed since the baselinestudy, and differences also could have resultedfrom different interpretations by differenttaxonomists. In addition, sampling methods weredifferent between the two studies, anddocumentation from the baseline study wasincomplete; only pooled data from multiplesampling events were reported.

We found that the percentage of EPT taxawas a more robust metric than total number oftaxa because it is less susceptible to differences intaxonomic interpretation. This metric indicatedthat the mean percentage of EPT taxa in the ruralstations had not changed since the baseline study.In contrast, there was a significant decrease in thepercentage of EFT taxa at the transition stations(from 20.5% to 13.5%, p=0.0131) while the meanpercentage of EPT taxa at the urban stationsnearly doubled to 6.8% from 3.5% (p=0.0554). .

These results may indicate that since thebaseline study, water quality and/or habitat haveimproved in the urban reaches of Coyote Creek,and decreased in the transition reaches whereurbanization has since encroached. We did notfind evidence that water quality parameters, suchas dissolved oxygen, were limiting the 1999

Figure 4-25. Monthly streamflow three years prior to Pitt and Bozeman (1982) and SEIDP (1999) sampling.

Monthly Streamflow Three Years prior to Pitt and Bozeman sampling(Oct 1974-Sep 1979)

Coyote Creek, Edenvale Gage.

Monthly Streamflow Three Years prior to SEIDP sampling(Oct 1995 - Sep 1999).

Coyote Creek, Edenvale Gage.

4000 +-----------------------------'

1000 +-----------------------------1

2000 +---------------------------1

!

I

E:l~

A ~~A

J ~Apt-96 Oet-96 Apr-97 0et-97 Apt-98 Oct-98 Apt-99

Date (Month, Year)

5000

4000

-CIl~ 3000U....,~

Ii::2000

1000

0

Oet-95

.... - .~.__..-_._--- -._._'.,

;-_._~.__ - -

I'!II_....-.~II!!!~__..I!!'-~~-__--_--~~~_...:llill!!!~~O~

Od-14 .,.-15 0"-15 "'-16 Ott-TO AfIt-l1 _11 Apr-18 Ott-18 AfIt-19

Date (Month, Year)

5000+------------------------,----,---1

E::l_~~

,-..rIl~ 3000 +---------------------------1~~oii:

12 0.8

·A B U410

Ul 0.8

1 8 us I usU4 Ul0.4 I.B

6 12

1 ) 12

4 u,0.2 l'l lJl

T, Ul lQ

~ <J!lIlI 2 AS U7

AS U7 0.0 - A4 RS Ri A2 ,..

0 - il' R! Rl R2 TS

0 6 10 16 20 26 0 6 10 16 20 28

... Subwatllr8hed ImpefVlousnen % Subwalershed ImpervioUBnesIl

14 1.2

C 12 012 ... UI

1.0 ,. il' R5

10 AI TS

f ... U4 Im

0.8 R28 11 lQ

III

jU7

•• I0.6

U7 U2 us4 0.4

us;f. y.l Ul

....2 .....

Rl TS 0.2 U4

0 .. AI AI

0.00 10 16 20 25 0 6 10 15 20 25

'16 Subwatll1'8hed ImpenAouanllllll % SubWatershed Imperviousness

60 28

E l.Il Fus

l.Il 2440

!I22 La

30 TS 20 u,lQ I.B

" 18 U2U4 U1

j)20 R,

16 llII Ul!

12 U7RoD

10 U7 14AI 1 - III U4

'II: il' R3 12 RtIl T1R5 ~

R5 R1 R3l>0 R2n10 R2

80 5 10 16 20 28 0 6 10 15 20 25

% SUbwatershed Imperviousness 'l6 SubwalBrshed Impervlousnen2000

GRetJ

I AI

1000 Ron R2

j Ul1M II... I.B

RI R, U7 Ul l.Il U40 n u,

0 5 10 15 20

'l6 SubwalBrshed Impervlousn_

Figure 4-26. PeRlent cumulative imperviousness in Coyote Creek subwatersheds versus fisheries metrics thatwere significandy associated. Data points are labeled by sampHng station (R6, RefJ., and Rea often overlapped(panels A - E).

16.,----------------------------------------..,

14 +-------------------~~---------------------f

12 +-----'-"-'--'--~---~--____,,,e_--__+--~------------------------_t

4+--------------'-----+-----~__f-,....-,----_+...,.+_----+_--'--~--__\_t_i

Date

Figure 4-27. Mean Dissolved Oxygen Concentration at the Five Water Quality Monitoring Stations.


Figure 4-28. Total Number of Macroinvertebrate Taxa, Coyote Creek, May & June 1999,Plotted with Percent Clay/Silt

50

8 45 I .MllY

~40 • • .June

'if!. 35 j, • A%Silt/Qlly

1 30 ,J: • • •• •~ t5 •i to

.t.Z 5lit

0U-S U-6 u-? T-t T-3 R-3 R-5 Ref-t Ref-2

macroinvertebrate community composition butdid not have similar data from the baseline studyto compare to. We did find the influence ofsubstrate composition reflected in the 1999distribution of macroinvertebrate assemblages.Mean substrate size was greater at stationsupstream from U6, favoring EPT taxa as seen,and indicating greater habitat complexity andstability, both factors that contribute to increasedmacroinvertebrate diversity and abundance

(Merrit and Cummins 1984): sand, the poorestsubstrate for macroinvertebrates, comprised 26%of samples collected from stations V1 - U6jsilt/clay content was at least three times greaterthan at upstream stations. Lacking similar datafrom the baseline study, we were unable to drawconclusions regarding the relative influence ofsubstrate over this period of time. Hydrologicfluctuations can dramatically impactmacroinvertebrate assemblages. Because Coyote

Figure 4-29. Percent ofEPT, Chironomid, Oligochaete and Other Taxa, CoyoteCreek, 1999

IID%EPT

DlI% Other

• % Chironomid

ll!I % Oligochaetes

100% .,....,~,...--.r~-~~-~~-~~_"'""'"r--",,~-~~-~~ ,....- ---,90%

80%

70%

.. 60%

i 50%D. 40%

30%

20%

10%

0%U·5 U·6 U-7 T-1 T-3 R-3 R-5 Ref-1 Ref-2

86


Figure 4-30. Average Number of Low Tolerance MacroinvertebrateTaxa

18

16

('$ 14~

Eo-12....

0..~ 10,t:l

a= 8Z

~ 6~

~ 4

2

0US US U6 U6 U7 U7 T1 T1 T3 T3 R3 R3 RS RS Ref1 Ref1 Ref2 Ref2

Station

Creek flows have been highly regulated duringboth studies (annually from April 15 - October15), only large fluctuations causing droughts orfloods are likely to have contributed to observedchanges in assemblages, through impacts onerosion, bedload movement, and distribution ofinstream habitat units.

While numerous factors can modify habitatand potentially confound interpretation of results,including effects of factors related to hydrology,sediment transport, and taxonomy and sampling,it appears likely that factors related tourbanization have influenced the distribution andabundance of macroinvertebrate assemblages in

this system. Identifying specific causes and theirrelative contributions is difficult, particularly incomplex urbanized systems such as CoyoteCreek.Programmatic Indicators


Estimating percent imperviousness usingdifferent data sources (1995 ABAG data versus acompilation based primarily on 1999 CountyAssessor data) resulted in large differences forsampling station subwatersheds (Table 4-10).Urban sites were characterized by the 1995 ABAGdata as having higher percent imperviousness (5 -

o

apter

Percent Imperviousness for Sampling Station Subwatersheds

Land UseBasemao 1 2 3 4 5 6 7 1 2 3 1 2 3 4 5 6 eft ef2

Assessor'sLand UseDatll 6.5 9.6 3.9 7.4 1.2 0.2 7.6 6.7 .7 0.7 .9 .2 0.6 .5 .8 .9 .6 .7

ABA-G'sLand UseDatll 1.4 29 7.5 6.9 27 22 7.3 20 .7 .4 .0 .1 .1 .0 .9 .0 .0 .0

Table 4-10. Subwatershed percent imperviousness estimated using different data sources with different spatial resolutionand currentness. Primary data sources were 1999 Santa Clara County Assessor land use data (ownership parcel as spatialresolution), and 1995 Association for Bay Area Government's land use data (I-hectare spatial resolution) (ABAG 1996).Additional data sources used to develop the estimates of imperviousness for the Assessors land use data are described inCh 3

87


24%). Non-urban sites (e.g., transition, rural, andreference) exhibited a lesser difference in percentimperviousness (1 - 5%, 1 - 4%, and < 2%,respectively) based on the disparate data sources.Differences in percent subwatershedimperviousness for all sites resulted from similarland use categories. Industrial, residential, andcommercial land uses accounted for moreimpervious acreage in the ABAG-based estimatesthan in those based on

Assessor's data; transportation andvacant/undeveloped land uses accounted formore impervious acreage using the Assessor'sdata, as compared to the ABAG-based estimates.We attribute these differences to the finer spatialresolution of the Assessor's data and our use ofmultiple data sources and groundttuthing toimprove land use classifications. We foundexceptions to these patterns in subwatershedswith recent development (R2 - R4). Here,industrial, residential, and/or commercial land·uses accounted for slighdy higher (0. 5 - 1.5%)estimates of petcent imperviousness in the morerecent Assessor's 1999 data than in the 1995ABAG data.

Cumulative percent subwatershedimperviousness increased slighdy (.05%) betweensites above Anderson Dam (Ref2 to R6) and more .substantially (16.21%) between sites belowAnderson Dam (R-5 to V-1) (Table 4-11, Figure4-31).

Selected Component.r ofSubwatershed ImperviousnessThe relative contributions of different land

uses to percent imperviousness varied bysubwatershed (Table 4-12).

Porcent imperviousness within the riparian corridorThe percent of riparian corridor

imperviousness was highest in lower urbansubwatersheds (V1-U5). Percent imperviousnesswithin riparian corridors differed from therespective subwatersheds (Table 4-13). In all butthree subwatersheds, percent imperviousness waslower in the riparian corridor than in the entiresu~watershed (Table 4-13). Road-right-of-waycomprised the greatest proportion ofimperviousness within the riparian corridor of themainstem Coyote Creek (1.88%) (Table 4-14).

88

Projected imperviousnessPercent imperviousness for the Coyote Creek

watershed was projected to increase by 1.7% from1995 - 2020. About two-thirds (72%) of thisincrease was due to increased residentialdevelopment; about one-third (28%) was due toincreased industrial/commercial development.


The SWRCB database identified 378 NOIfilers in the Coyote Creek watershed (Table 4-15,Figure 4-32).

Industrial activities covered under thestatewide stormwater NPDES permit forindustrial activities included the 17 industrial

Table 4-11. Cumulative percent imperviousness influencingsampling stations was estimated using the County Assessorland use data for Coyote Creek drainage area. The numbers inbold reflect where the cumulative imperviousness estimates ateach sample station in the Coyote Mainstem also includesdrainage area of major tributary and its correspondingimperviousness.

Sampling Cumulative Area CumulativeStation Impervious Watershed Percent

Area Area (Miles2)Imperviousness

(Miles2)

Ref2 1.27 80.58 1.57

Refl 1.29 81.51 1.58

R6 1.70 105.17 1.62

Anderson Dam

R5 0.05 0.79 5.8

R4 0.08 1.66 4.61

R3 0.44 5.10 8.58

R2 0.64 7.85 8.1

Rl 0.86 12.37 6.95

T3 2.53 27.35 9.24

T2 2.87 31.79 9.03

Tl 3.01 33.04 9.11

U7 4.09 36.96 11.07

U6 6.40 45.91 13.94

U5 9.39 50.79 18.48

U4 10.14 52.11 19.47

U3 22.77 96.17 23.67

U2 25.12 120.78 20.08

Ul 27.30 124.06 22.01

Legend

10

~Bs

santa Clara Valley Revised 1/14100Urban Runoff Pollution Prevention Program

Figure 4-31. Estimated percent imperviousness in Coyote Creek subwatersheds. Estimates for subwatersheds along the CoyoteCreek main channel reflect cumulative percent imperviousness (UI-RS include upstream drainage area below Anderson Reservoir;R6 - ~ef2 include all upstream drainage area).

Figure 4-32. Industrial and construction sites 6led under the California National PoDutaut Discharge EliminationSystem pennit in the Coyote Creek watershed by standard industrial classification sector.


Table 4-12. Relative contribution of selected land uses to percent sampling station subwatershed imperviousness.

Percent Imperviousness for Selected Land Uses

Sampling Station Commercial Industrial Residential Road TotalRight-of-Way

U1 9.3 25.29 12.04 16.46 63.09U2 7.38 43.33 0.38 15.3 66.39U3 5.45 1.13 23.72 18.87 49.17U4 3.58 12.34 16.07 18.79 50.78US 7.96 10.01 19.24 16.63 53.84U6 4.17 5.96 20.76 14.07 44.96

U7 0.46 1.46 10.99 12.29 25.2

T1 0 243 0.39 6.08 8.9

T2 0.36 0.46 1.15 3.51 5.48

T3 0 0 0 4.68 4.68

Rl 0.05 0.05 0.05 2.81 2.96

R2 0.01 1.53 0.05 3.38 4.97

R3 1.34 0.5 2.19 4.41 8.44R4 0.05 0 0.6 0.51 1.16R5 1.49 0 0.36 1.92 3.77

R6 0 0 0 0.82 0.82

REF1 0 0 0 0.98 0.98

REF2 0 0 0 0.12 0.12

categories listed in Table 4-16.We georeferenced industrial and

construction filer site locations (Figure 4-33) andcross-referenced potential pollutants that mayexist in runoff from these sites (Figure 4-34) byassociating site SIC codes with pollutantsmonitored as a permit requirement (SWRCB1995). We did not estimate the proportion ofnon-filers in this watershed due to the difficulty inidentifying reliable data sources for such a largearea.

In the Walsh Avenue Catchment we were

able to assess the status of non-filers because wedeveloped a reliable data source by conductingour site surveys and inspections. We did notidentify any non-f1lers within the catchment;however, because the catchment was the subjectof a pilot inspection program in 1992, facilityoperators may be more aware of filingrequirements than is typical. From the SWRCBNOI database we identified two active NOI filersand three businesses that were terminated fromthe Permit during its initial year (fable 2, Figure4-35). Of these three, two closed their operations;

Table 4-13. Percent imperviousness of the riparian corridor within each sampling station subwatershed and of each entiresampling station subwatershed. Estimates based on Santa Clara County Assessor 1999 land use data.

Percent Imperviousness for each Sampling Station Subwatershed

Land Use Area Vl V2 V3 U4 US V6 V7 T1 T2 T3 Rl R2 R3 R4 R5 R6 Refl Ref2Riparian Corridor

34.1 54.3 39.3 14.2 16.6 6.7 6.0 5.8 7.7 5.7 5.1 6.1 7.8 6.1 3.8 1.0 1.0 1.9

Entire Watershed66.5 69.6 53.9 57.4 61.2 50.2 27.6 16.7 9.7 10.7 4.9 7.2 10.6 3.5 5.8 1.9 26 1.7

91


Table 4-14. Percent imperviousness within the ripariancorridor of the Coyote Creek mainstem from its mouth(downstream of V1) through the most upstream samplingstation subwatershed (ReQ). Estimate of imperviousnessbased on Santa Clara County Assessor 1999land use data, and sorted in descending order.

Land Uses Impervious PercentArea Impervious(Acres) Riparian

Corridor

One of the two active facilities developed andimplemented a SWPPP. This facility's SWPPP hadnot been updated since it was prepared in 1996.For the two active NOI facilities in theCatchment, we found that general housekeepingand employee training BMPs were beingimplemented.

Illicit Connections Identifled/Corrected

the third altered its drainage so that runoff fromthe work area discharges to the sanitary sewer.Of ten annual reports required from the twoactive NOI-ftling businesses over the 5-yearhistory of the statewide permit, four reports wereactually submitted (40% compliance). Of tenrequired monitoring t:eports, three were submitted(30% compliance); There was a trend towardmore consistent submittal of reports over the pastthree years than in the earlier years of the permit.This could be attributed to the increased effortassociated with the local stormwater programsand regional public infor mation effort. However,by this measure, compliance in the Walsh Avenuecatchment was poor when compared to averagecompliance found for industries in this countyand the Bay Area region (Table 4-17).

Road Right-of-WayResidentialUrban Recreation

IndustrialAgricultureCommercialPublic, Quasi-PublicNon Urban RecreationVacant, UndevelopedTmnspto/Commtn/UtilitiesSanitary Landfill

301.35

236.24

127.00

101.45

92.58

79.80

58.15

43.17

19.09

16.48

0.05

1.88

1.47

0.79

0.63

0.58

0.50

0.36

0.27

0.12

0.10

< om

Illegal Dumping IncidentsThe locations of illegal dumping incidents

reported in the Coyote Creek watershed (1995 1999) are shown in Figures 4-36 and 4-37. Themost commonly reported types of dumpingincidents in San Jose and Milpitas, respectively,were residential auto (30.3%; 37.0%), commercialauto (9.8%; 8.8%), construction (7.0%; 9.2%), andillegal dumping (15.8%; 11.3%). Severalresidential incident types (gray water, irrigation,sediment) each contributed an additional 5;2% ofall illegal dumping incidents reported in the·Coyote Creeit·watershed. Equipment cleaning,restaurants, and sewage spills each accounted forapproximately 4% of total incidents reported inthe two-city area but a smaller proportion of thetotal incidents in the Coyote Creek watershed.

Incidents reported in the following categoriesdeclined consistently in the two-city area (19931998): automobile related activities (commercialcar washing and wastewater discharge, residentialradiator fluid dumping, oil dripping, and dumpingassociated with multiple family residences),residential cement washing, industrial coolingwater discharge and equipment cleaning, paintspills and dumping, and sewage spills. The 19951998 trends in Coyote Creek watershed were

Table 4-15. Industrial and Construction site operators in the Coyote Creek Watershed and the Walsh Avenue Catchmentthat filed NOIs under the 1995 California National Pollutant Discharge Elimination System (NPDES) permit

study Area Industrial NOIs Coristruction NOlaActive Active

Total (% ofTotaJ) Total (% ofTotal)Coyote Creek Watershed 227 162 (71 %) 151 80 (53%)Walsh Avenue Catcbment 5 2 (40%) 0 0

Total 232 164 (70%) 151 80 (53%)

92

Figure 4-33. Industrial and construction sites filed (1998) under the California National PoUutant DischargeElimination System permit in the Coyote Creek watershed and associated land uses.

Figure 4-34. Pollutants monitored by NOI industrial facilities in the Coyote Creekwatershed, as a requirement of theNational Pollutant Discharge Elimination System permit (SWRCB 1995), and pollutants likely di!charged byconstruction NOI sites.

Industrial NOI Filer Statusin the

Walsh Avenue Catchment

NOI FUer Status_ Active

D Non NOI Filer_ Terminated,1992~ Storm Drain Inlet• Walsh Avenue Catchmento Ownership Parcels

s

o 0.1 Milesi~~~~~~~~!li

Revised 3/31/99 SCVURPPP

Figure 4-35. Industrial facilities within the Walsh Avenue catchment iUustrated by NOI filer status.

Illegal DumpingIncidents Reported

in the Coyote CreekWatershed

• Stormdrain OutfallsIllegal Dumping Incidents

• 95-9696-97

> 97-98• 98-99N Coyote Creek

1\/Surface WaterbodiesMunicipalities

Milpitas>-' Morgan Hilllil'i San Jose

County

Revised 3/31/99SCVURPPP

Coyote CreekWatershed

SouthSan Francisco

Bay

s

N

W*E5o

i

. Figure 4-36. Recent (1995 - :1998) illegal dumping incidents reported in the Coyote Creek watershed.

Illegal DumpingIncidents Reportedin the Coyote Creek

Watershed• Stormdrain Outfalls

Illegal Dumpings & Illicit Connectionso 95-96o 96-97It 97-98It 98-99

A/Coyote Creekt:J SubwatershedsLand Uses;,e,Y,i; High-Denslty Residential

IIModerate-Density ResidentialLOW-Density ResidentialCommercial

_ Public, Quasi-Public_ Heavy Industrial

Light Industrial_ Transportation, Communication_ Utilities~ Sanitary Land RllsII!mlI Mines, Quarries, Gravel Pits"", Urban Recreation

Vacant, Undeveloped,., Agriculture

Rangelandl1li Wetlands_Forest~ Bays and Estuaries_ Freshwater

Revised 3131/99SCVURPPP s

Figure 4-37. Enlarged area of Coyote Creek Watershed iUustrating recent iUegal dumpingIncidents in the City of San Jose and their association with land uses.


Table 4-16. Pollutants monitored by NOI industrial facilitiea in the Coyote Creek watershed, aa a requirement of theNational Pollutant Discharge Elimination System permit (SWRCB 1995). Monitoring parametera are listed by standardinduamal claaaification (SIC) sector (digit places designated as "X" indicate that subsectors exist, in which cases only a subsetof monitoring parameters may apply).

Seeto Industrial Activity SIC Code Monitoringr Parameters

B Paper, Allied Products Manufacturing 26XX COD

C Chemical.. Allied Products Manufacturing 28XX Al;Fe;N+N;Zn;Pb;P

D Asphalt Paving/Roofing/Lubricant Manufacturers 295X TSS

E Glass!Clay!Cement!Gypsum Manufacturer 32XX Al;TSS;Fe;Zn

F Primary Metals 33XX A\;Zn;TSSjCujFe

J Mineral Mining & Dressing 14XX TSS;TDS;N+N

L Landfills and Land Application Sites 4953 TSS;Fe

M Automobile Salvage Yards 50XX TSS;Fe;Pb;A1

P Land Transportation Facilities 41XX;42XX none listed

R Ship and Boat Building or Repairing Yards 373X none listed

S Air Transportation Facilities 45XX BOD;COD;NH;pH

U Fooe! and Kindred Products 20XX BOD;CODjTSS;N+N

W Furniture and Fixtures 25XX;2434 none listed

X Printing and Publishing 27XX none listed

Y Rubber/Plastic Manufacturer 30XX Zn

AA Fabricated Metal Products 34XX Zn;N+NjFe;A1

AC E1ectronic/E1ectrical/Photographic/Optical Goods 36XX none listed

LJgmd:

Al Aluminum NH AmmoniaBOD Biololrical Oxygen Demand P PhosphorusCOD Chemical Oxygen Demand Pb LeadCu Copper TSS Total Suspended SolidsFe Iron Zn ZincN+N Nitrate + Nitrite

Table 4·17. NOI filer compliance with the California NPDES Permit requirement (or annual report submittal)within Bay Area counties during fiscal year 1997·1998 (Source: Region 2 Water Quality Control Board).

County Number of Annual Report Percent ComplianceActive Sites Submitted

Solano 62 39 63%

Alameda 532 325 61%

San Mateo 138 83 60%

Contra Costa 186 107 58%

Santa Clara 517 300 58%

Marin 37 20 54%

Sonoma 59 28 47%

Napa 109 45 41%

San Francisco 27 7 26%

Total 1667 954 57%

98

Table 4-18. New building development completedor under construction {rom 1995 - 1998 inplanning areas (Edenvale, Evergreen, andBenyessa) within Coyote Creek Watershed

Source; City ofSan Jose Pbnning Department,bllll' Ilwww<isaPio" ca.ys/p1anning/sjplan/dcmp03 btm

"Major" defined as follows: residential = projects ~ 100+dwelling units (DU); commercial = projects ~ 25,000 ft2(.57 ac); industrial =projects ~ 75,000 ft2 (1.72 ac).

involving residential radiator fluid remainedrelatively constant and those involving residentialcement washing fluctuated.

Incidents reported 1993 - 1998 increasedconsistently for the following categories:residential oil dripping, construction activities, andresidential gray water and sediment. Pollutantsassociated with auto-related activities (e.g., oil,antifreeze, solvents, and fuel) accounted for40.8% of incidents reported as "hazardous illegaldumping," 5% of those reported as "nonhazardous illegal dumping", 25.7% of thosereported as "parking lot" incidents, and 50.9% ofthose reported as "spills."

Illicit ConnectionsThe number of illicit connections identified andresolved decreased 70-fold between 1993 and1998. Most (79%) of the illicit connectionsreported in FY 93-94 resulted from a singlecondominium development that had plumbedwashing machines and/or water softener drainlines into surface storm drain inlets.

Changes in Population and Program PracticesEvaluating stonnwater program effectiveness

requires considering co-occurring factors that canaffect the rate and spatial pattern of reportedICID incidents. In the last decade (the period inwhich the Program was initiated) the SiliconValley, and particularly its eastern watersheds,including Coyote Creek, have experienced rapidgrowth in population, industrial, commercial, andresidential development, and transportationvolume.

Develonment Catel!'orvMajor ResidentialMajor CommercialMaior Industrial

Developed Area5,781 DU

28.6 ac66.6 ac


From 1990 to 1998, Milpitas and San Joseincreased in population by 14% and 23%respectively (Santa Clara County Planning Office1998). The number of jobs in these cities grew13.5% from 1985 to 1995. Employment isprojected to increase an additional 20.8% from1995 to 2000. Increased employment and thehigh cost of housing have increased commutingdistances and volumes. ABAG (1998) projectedan increase in Santa Clara County from 40,000daily commuters in 1980 to 116,000 dailycommuters in 2000. Development continues inboth San Jose and Milpitas, includingdevelopment of the City of San Jose portion ofthe Coyote Creek watershed (fable 4-18).Analysis of land use projections showed that anadditional 1% of the Coyote Creek watershed willbe developed between 1995 and 2000 forresidential, commercial and industrial uses(Buchan 1999).Co-permittees have increased and allocatedinspection staff to meet Program requirementsand to focus inspection effort on incident typesand/or industries they considered to be highpriorities. Since 1990 the City of San Jose'sinspection staff increased from 5 to 9 full-timeequivalent (FfE) positions. Staff allocations toICID inspection efforts increased from 1 FfE(1990) to 3.4 FTE positions in 1999. Inspectionstaff also was effectively increased by conductingcross-training sessions for city personnel in theCity's public works, streets and parks, hazardousincidents, and fire departments. The smaller Cityof Milpitas increased their staff from 2 nEs in1990 to 3FTEs by 1996.

Decreases in the number of incidentsreported in some incident categories may haveresulted from the Co-permittees' outreach efforts,which included advertising to encourage used oilrecycling, workshops, public relations events, androutine inspections of industrial and commercialfacilities. The City of San Jose prioritizedinspections for construction sites, auto repairshops, auto dismantlers, and restaurants byanalyzing their illegal dumping database,evaluating response from industrial/commercialowner/ operators during inspections, and byevaluating growth trends. Inspectors reporteddifficulty in responding quickly to incidents due tolimited ability to communicate from the field and

99


due to limited site access. To improve responsetime the City of San Jose obtained two four-wheeldrive vehicles and cell phones. City inspectors alsoreported the lack of outreach materials in multiplelanguages as an obstacle for field inspectors toaddress residential ICID incidents. Outreachmaterials. have now been produced in theselanguages.

Increases in the number of ICID incidentsreported in other incident categories do notnecessarily indicate lack of stormwater programeffectiveness. Indeed, increases should beexpected given continued urbanization, and theanticipated lag time before public education andregulations will affect behavior. Increases in thenumber of illegal dumping incidents reportedwere observed for construction and auto-relatedmaintenance activities (particularly residential).Such increases corroborate with the increasedurbanization in the region, and with attendantincreased inspection efforts focused on thesetypes of activities and specific areas where theycommonly occur.Apparent fluctuations in the n~ber of ICIDincidents reported in some categories during 1993- 1998 may be an artifact of how categories aredefined. For example, categories such ashazardous dumping, non-hazardous dumping, andspills, are associated with a variety of pollutants,and do not describe a specific activity that couldbe targeted by inspectors. More descriptivedefinitions of these categories would improve areporter's ability to associate pollutant types withthe incidents. Fluctuations in the number ofreported illegal dumping incidents could alsoresult from variable reporting and inspectioneffort, or from increases in the type of dischargecorresponding to localized urbanization.Discharge of pollutants . associated withautomobile washing and repair (e.g. ethyleneglyco~ polyaromatic hydrocarbons (pAHs),surfactants, phosphates, and organic solvents)decreased for some residential categories,particularly for multiple dwelling residences.Discharge of sediment, organic solvents, selectedmetals, and pathogens may also have diminisheddue to decreases in cement washing, commercialequipment cleaning, and paint and sewage spills,respectively. Such improvements, however, mayhave been countered by discharges of similar

100

pollutants associated· with some categories ofresidential auto repair and construction activities.The significance of the results obtained fromapplying the ICID Indicator to the Program arelimited because:

=> Co-permittee efforts to investigate andeliminate rCID vary (over time and from Copermittee to Co-permittee).

=> ICID reports may not accurately characterizeincidents or pollutants involved.

=> Co-permittees reporting summaries haveinconsistent formats, and some data is notavailable.

4.2 Results in the Walsh Avenue Catchment


Industrial/Commercial Pollution Prevention

Forty-five percent of the site managersinterviewed were unfamiliar with the City of SantaClara's stormwater pollution prevention program.However, most of these site managers understoodthat stormwater can carry pollutants to nearbycreeks and water bodies, were receptive to theneed to prevent stormwater pollution, andreported that they were implementing pollutionprevention measures such as proper materialstorage, dry sweeping, good housekeeping, andsafety. In gene~ they accepted theserequirements as common-sense practices.

Few respondents were familiar with thedifferent Program components or with theenvironmental and legal consequences ofstormwater violations. Few were familiar with theterm "best management practices" - but most ofthe sites were implementing BMPs. Cost, workeffort, and site manager attitudes were notsignificant barriers to implementation of runoffpollution control measures.

Since the 1993 pilot project, businesses hadincreased their implementation of pollutionprevention BMPs, and more businesses were incompliance with pollution-preventionrequirements. However, lack of detail in theCity's inspection record made it difficult todistinguish which BMPs were implemented as a

result of efforts specifically related to stonnwaterpollution prevention outreach.

Businesses' costs for stonnwater pollutionprevention include construction and maintenanceof BMPs, training of staff and labor costs toimplement "operational" BMPs. Managersconsidered costs associated with operational andstructural BMPs to be minimal and did not trackthem. For most facilities in the Walsh Avenuecatchment, staff training is infonnal, and is part ofday-to-day interaction between supervisor andemployee. Where site managers did identify costsrelated to stonnwater pollution prevention, thesetended to be one-time costs (for example, the costto purchase a piece of equipment or to install a"structural" BMP). Although the BMPs areimplemented, most managers are not consciouslymotivated by the need to prevent stonnwaterpollution.

In general, the tendency of respondents tonot associate specific training and operationalactivities with stonnwater pollution preventionseemed related to their general unfamiliarity withthe City's pollution-prevention program.

Number of BMPs Installed, Inspected, andMaintained

Respondents were asked to indicate, from alist, which BMPs were typically associated withtheir site. The most commonly reported BMPsincluded:

=> Perfonn daily cleanup and sweeping ofindoor and outdoor work areas (21responses);

=> Regularly remove trash and debris fromoutdoor areas (parking lots) (19 responses)

=> Properly dispose of process residues(sawdust, metal scraps, fluids) (18 responses)

=> Properly store raw materials, products andby-products (15 responses)

=> Minimize the use ofwash water whencleaning equipment and vehicles (14responses

=> Properly store paints, chemicals and solvents(12 responses).


Discussion of the survey with the site managersresulted in a more detailed list of BMPs appliedand agreement on additional BMPs that should beapplied based on observations made during thesite visit. Most of the sites in the Catchment wereindoor operations, minimizing the potentialimpact to stonn water and the need for BMPimplementation. However, some BMPapplications were applicable. Existing (andrecommended) BMPs were related to generalhousekeeping in the area surrounding the businessand debris bins, staff training and material storage.

Many of the sites in the Catchmentimplemented BMPs but did not associate thisimplementation with the City's outreach andinspections. For example, many managers ofcommercial and light industrial sites citedorganization and efficiency as the reason forstoring materials properly. Similarly, parking lotsand entranceways were swept to maintain apleasing appearance. BMPs at the more industrialsites were implemented to comply with otherenvironmental regulations, safety codes, and localfire codes, and not necessarily to protect stonndrains.

Most BMPs implemented in the Catchmentwere non-structural with the exception of theplating facilities. Most of the commercial and lightindustrial businesses were implementing someappropriate housekeeping and material and wastestorage BMPs. Additional BMPs, however, wererecommended for many of the businesses.

Industrial Site Compliance Monitoring

Of 23 businesses in the Walsh AvenueCatchment that were inspected by the City ofSanta Clara September 1998 - March 1999, 16(70%) were found to be in compliance with allvarious stonnwater pollution-preventionmeasures. The remaining 7 facilities hadviolations relating to general housekeeping,outside storage of equipment or raw materialstorage and the washwater disposal. Additional orfollow-up reinspections were conducted asneeded, in response to conditions observed at thescheduled site visits. In follow-up reinspections,two previously non-complying businessesremained in non-compliance. New violations

101


where dissolvedthroughout the

were identified at two businesses that had been incompliance during the previous inspection.

Of the 32 businesses currently in theCatchment, 18 participated in the 1993 PilotStudy. A comparison of the inspection data forthese sites for the two periods indicated a markedincrease in the proportion of sites in compliancewith the City's stormwater regulations. In twocases, some have never achieved compliance anddemonstrated repeat violations in the same areas.

The two metal plating/finishing facilities inthe Catchment are subject to the State's GeneralIndustrial Permit and must submit a Notice ofIntent (NOI). As a part of the General Permitprocess, proof of the NOI, an Annual Report anda Stormwater Pollution Prevention Plan should beavailable. One of the facilities maintained a 1998NOI, but their Annual Report and SWPPP werefrom 1995-1996. The other facility providedAnnual Reports for the previous two years.

These two facilities also operate on-siteindustrial waste treatment systems and maintainpermits from the San Jose/Santa Clara WaterPollution Control Plant to discharge treatedeffluent into the sanitary system. Staff based atthe WPCP inspect the treatment equipment andrequire monitoring of effluent quality discharges.

4.2.2 Water-Qllaljry Indicators

Toxicity Testing (Indicator #2)

All 15 samples, were toxic, resulting inmortality within 24 hours of exposure to 100%runoff. Lack of apparent trends in toxicity is due,in part, to the use of the screening test designwhere organisms are exposed to 100% runoffrather than a dilution series used in the definitivetest. If the definitive test was used it may havebeen possible to observe changes in thepercentage of dilution of the runoff that wasrequired to allow partial mortality of the testorganisms.

The disadvantage with the definitive test isthat the costs are greatly increased (3-4 fold) overthe screening test. An alternative approach is tomonitor changes in concentration of the causativetoxic agents. In the case of Walsh Ave thecausative agents were previously identifiedthrough a TIE as dissolved metals. The toxicity

102

monitoring results are consistent with the resultsof the zinc monitoringconcentrations stayed highmonitoring period.

Non-Point Source Loading (Indicator #3)

Figures 4-38 and 4-39 show the results of thetime-trend analysis for the total metals and solids.Time trends are presented for both pollutantconcentration and pollutant load. Significanttime-trends were found for both concentrationsand event loads of TSS, lead, and nickel (p< 0.05),which decreased with time. In general. the trendswere more significant for the pollutantconcentrations than for the pollutant event loads.The load time trends explained 33% of the datavariability for solids and lead and 51% of the datavariability for nickel. Time-trends for zincconcentrations and event loads were notsignificant. Zinc concentrations were elevated ascompared to most urban runoff with the highestconcentration measured during the last year ofmonitoring. No significant trends were found fortotal copper concentration. However, weaktrends in copper event load were significant at the92% confidence level (p =0.078). The time-trendin copper event load was highly influenced by twoevents that occurred early in the monitoringprogram. When these two data points are omittedfrom the analysis the trend becomes insignificant.

The results of time trend regressions forparticulate copper, particulate lead, and particulatezinc are shown in Figure 4-40. The results of theanalysis show none of the particulate metals hadsignificant time trends. Particulate lead showedvisually decreasing concentrations with time, whileparticulate copper and zinc concentrations wereessentially unchanged over time.

Table 4-19 shows a statistical summary ofconcentrations of TSS, Copper, Nickel, Lead, andZinc for runoff from the Walsh AvenueCatchment. Data are shown for three metalspecies: total, dissolved, and particulate. Dissolvedconcentration data were available for nickel onlyfor the last season of monitoring. Particulateconcentrations were calculated from the total,dissolved and TSS values using the followingrelationship: Particulate (J.1g/g) ={[fotal (Jlg/I) Diss (Jlg/I)]/ TSS (mg/l)} * 1000 Jlg/mg.


(T8S(rro1)Byllolo-'- ) [T.... Pb(ugIl)Byllolo~ ) (T.... HI (uDAl By OlIo -.- 1:ISO 250 IlO.300 . eo .

200 70250 eo .200

160

,~ , ! 60

1160

~.

t. :Ii 40 . .! !

100

! .. . . . 30 .100 # •

~ 60 20 . .60 ". . -.........: : :: 10 .:- .

0 0 0

l:lJ22A17 21211111 412_ e1241ll7 8124'00 12JZ1Jll7 2f.l1181 412_ 812..-&7 8124'00 12122187 21211111 412_ 812..-&7 8124'00lloIo-,- 0II0~ Oote-.-

= LIMorFI I = LIMorFI I = LIMorFi I[LIMorFI ) [LIMorFI ) (LIMorFI )T8S (rro1)' 87ll.1l15 0 5.1~70lIo-'- T.... PI> (ugIl)' 1028.40 0 Ull..71lolo llorTl>iod T.... HI(ugIl). 4111.244 0 1.•7 OIIlellorTl>iod

(lluImwyelFI ) (S........,OlFI ) [ 8uImwyolFl )RSq.... 0= R8quafa 0.4244e RSq.... 0._711

R8quafaAllj 0.255e911 R8quafaAdJ 0.40e474 RSq.... Allj 0.470ll4ll

R..._Sq....e- 52.4098e Roof ...... 8quafa e- 38.1551 Roof_ Sq....e- 158555

_elR_ 81.44828 M_ofR.....,... OU32S5 _ofR_ 28.217e5

~(orSumWgtI) 28 Ob.....ricln. (or Sum WgtI) 54 OO__ (orSumWgtI) 54

[AnotjIiI of v...... ) (AnaIjoII elV'- ) (An'-ofV.- )- OF Sum of Squara -8quafa FRelio - OF SumotSqUifa -Sq.... FRelio - OF llumofSquara -8quafa FRelio...... 1 28188.52 281M.3 10,8'8' ....... 1 34357.038 54357.0 25._ ...... 1 7838.S08 7'838.31 30.3755

e- 27 74182.85 2748.8 - e- 52 4MaS.D75 1455.8 ProO>F e- 52 8044.700 251.40 -CT.... 21 103331.17 0.0030 CT.... 55 10943.014 <.0001 CT.... 55 15881.009 <.0001

(_E_ ) (-~ ) (-~ )T.... e_ SIde- IRe PnIb>ltl T.... e- 81d e- I AlIi> PnlPiIl T.... e_ SIde- tRatio -Ill-.opt 87ll.1l1548 271.81122 5.158 0.0013 -.... 1028._ 187.8118 5.18 <.0001 -.opt 481.2435 12.242G5 8.85 <.0001

0II0~ "'.11.7 8.588o-ll .".28 0.0030 lloIoa-1od -3._7 8.885H1 -4.88 <.0001 0II0~ -l.Illl5e-T 2.884e-8 -11.81 <.0001

(Soide_LoaI('a)By_llompIod ) '-IE.... Lood (pwDo) B,__ [Nad eood LoaI(pmo)8yDokllomplod )140 45

120 . '" 40160

35100

~r 30 .

I .l 80 ! 100 25

~~ eo ~ ~ 20

! i ~ ! 15

! 40 u 50 . .! I 10

20 ...~~... ~:5

0 0 0

12122187 21211111 412_ e1241ll7 8124'00 12122Al7 21211111 4I24Ill4 8124/87 llI24IOO 12122187 2tJ:1191 412_ 812.....7 8124'00

0IIle-,- 0.1. S8rT'f*d IlolollorTl>iod

= LIMorFI I =L_Fh I =LIMorFI I[Uooorf1l ) [Um.'Fit ) (Uooorr. )Sold e_ Lood (kg)' 0511.221 0 1....7 OlIo -.- l.Hd Evo.. Lolld (gamI)' 883.843 0 2.51..7 Dolo SampIod _e_Lood(g....)·23S.04ll07.7~llolollorTl>iod

(_om ) (-.orru ) (_oCfI )RSqun 0.555757 R8quero 0.355888 RSq.... 0.6Oll888

R8quafa Allj 0.288885 R8quero .... O.3aM01 RSq.... Allj 0....7401

Roof_ Sq....e- 3O.808OIl__aq....e....

34.&15133 Roof_ Sq....e- 8.131501_ ofR_

44.07107 MMno'RMp)nM 55._3_ ofR_ 11._

~(orBumWgtI) 18 a..vatioAl(or&mWgf'l 24 00-. (or 8umWgtI) 2'

(~orv...... ) (......,m.rv...... ) (~orv..... )- OF Bum 01 Squar.a -8quafa FRelio - OF Sumot~ ..... 8qun FRitlo Sou... OF Bum of Squ..s MeanSqu.. FRIItio

....... 1 81150.603 81150.60 8.5830 Modo' 1 134M.7oo 15488.7 11.0178 ...... 1 1512.1527 1512.15 22.8884

e- 17 18138.404 848.20 Pnlb>f e- 22 2B88O.484 1222.3 - e.... 22 1454.8887 88.12 Pnlb>f

CT.... 15 24282.907 0.0083 CTotal 23 40367.184 0.0031 CT.... 25 281S8.821. <.OO()1

(-- ) [P""-- ) (-- )T.... e- SIde.... IRelio -Ill T.,m EatitNle Std e- I Rollo P!ob>I~ T.... E_ B1d E.... IRe PnIb>ltlIn_

558.22127 175.8781 3.18 0.0055 lrUn:opl 88354252 188.8883 3.48 0.0021 -.... 233.04825 48.25413 5.04 <.0001

OIIollorTl>iod -U03e-7 8.14_ -2.85 0.0083 oet, s.rrp.d -.2.311.-7 8.883e-8 -3.32 00031 0II0~ -7.744.-8 1.e1ee-a ~.78 <.0001

Figure 4-38. Time Trend in TSS, Lead, and Nickel In Walsh Ave Catchment

103


TObIl Zn (ugII) Dy Date llamptod ) TObIl Cu (ugII) By OCt. Blmplld 1S500 1110

:IllOO0

01211

2500 •0 100

12000 0

0 0

1 00 * 18

,fl11lOQ ---.:..a--- a~I . .. ( . !o • 0.. 110

1000

800 0 211~..0 0

o OK

12JZ21l11 '21211\11 4/241ll4 ~1 1/24/00 12JZ21l11 21211\11 4/241ll4 lII2411l1 8124100OCto IlImptod Date llamptod

=~Fil I =~Fil I(~Fill (U-Fil )

Tot.! Zn (ugII) .,401.18 0 1.07..7 OCto Bamplod Tot.! Co (uglil' 222,128 0 0.3_ OCt. Blmplod

(BummaryOlFil ) (Bummary of Fil )ROq...... 0.00028 RBquaIo 0.llS3n&ROquoroAdJ ~.03304 RBq...... Adj 0.024184Root_. Oq.1tO Enar 81U274 Root Moon BquoroE_ 25.72478_.ofR..ponlO 1187.1144 ..... ofR.._. 4:1.3521l40bI_. (or Bum WgtI) 32 _.(or Bum WgII) :14

(Aftolyol. 01 y.tta_ ) (An.lyoloolYorianol )- DF Bum 01 BqUltOl MoonBquoro FR_tio - OF Bum 01 BqUltOl MoonBquoro FRatio...- I 11611 11611 0.0084 - I 1202.517 1202.114 1.8112Enar :10 112-. 1741145 Prob"F Enar 32 21178.407 llll1.10 -CToIlIl II 11251400 0.0278 CToIlII 13 22178.1145 0.1871

(P._E_ ) (Porom«or Eotimotol 1Torm Eo1lmoIo Old Enar I Ratio -Ill Torm e_ Old Emn I Ratio PIoO>t1lIIIlolcopl 1401.187 1120.008 0.45 0.06ll4 Inton:Ipl 222.12848 1130435 I.llll 0.1087Dolo llamptod ·1.0740-7 0.000lI01 ~.OG 0.0275 OCto Blmplod 08.:l30-li 4.8080-8 ·1.18 0.1071

Zillc e_ Laod (JI'Ill') Dy IlIIllIlomplal I (CqlpaE_ Lood (JI'Ill') Dy 1lIIll8loqllcd )IIIOQ 80

• 0

1250 • 800

0

1000

~ I40

~I 7110 ao] ~

]

I IIOQ ] 20

M ~ J250 • • • 4: 10~. •.. .0 ,

0 012JZ2IlI7 21211111 4/241ll4 81241117 8124/00 12JZ21l17 21211\11 4/241ll4 81241117 IIIWIlO

Dolo Blmptod Dolo IlImpiod

=UnoorFil I =UIIOIfFil I(U-FI. ) (U-F1. )

Zlno Ewn! LDO<llomnol'2&71.aa 0 7.07..7 OCto Blmplod CoIlpor e_1.ood lomnol" 1611.878 0 4.550-8 OCto Blmplld

( IIulDalay arFil 1 (lllmmlay off" )ROquoro 0.047842 RBquaIo 0.113014

RBquaI. AdJ 0.000024 ROq.... AdJ 0.004t148Root Moon Bquoro Emlf aeU4ae Root Moon Bq.... Em>r 11.28788....n01 n..portH 802.8122 ....nof Allponla 10.10081Db_. (a< Bum WgtI) 22 _.(a<tklmWgII) 24

(AoaIylio ary_ l (AoaIylio ary_ )Boun:o OF Sum of Squa,.. ....nSqUII,. FRatio - OF Sum ofSqu..... MoonBqualo FRIlIoM_ I 1451178.1 14ll1l78 1.0008 - I 800.8147 800.815 3040'8EmIr 20 2018083.8 1451lO8 Prob>F Em>r 22 ae84.4128 17U07 Prob>FCTOIal 21 1081071.7 0.1201 CToIlII 21 "1S.oall 0.0780

(............ 1!I1imoJa ) (_1!I1imoJa )T_ ea.IIn.t11 Old e"", IRltlo _'111 Torm ElUmat. ll1llE"'" I Rotlo PIOb>IIIInton:opt 2871.85'8 2280.800 1.27 0.2204 Inton:opt 1511.875115 78.6Il48' 2.10 0.0478OCt. Blmplod ·7.0850-7 7.0830-7 .1.00 0.3201 Datea.mpt.d ".8&10-8 2.8480-8 ".114 0.07M

Figure 4-39. Time Trend in Zinc and Copper in Walsh Ave Catchment

104


Pat Pb ("&llly!llto SoolpIcd I (Pat a. (..&lily Dwo Sar.,od ) Pat z.. (..&lily Dwo SoolpIcd )800. .

. 1500 100 25000 ..

~700 .. 20000lIOO .

fOGO 1 500~ 1 '5000

~f- a 400 • I~.

l l • l ooסס1:IOCl

~. .2---500

~ • •-2005000 • ~-'"

.~'. .100 •. .

0 0 012122187 2121111 4/24/94 8124/97 1/24100 12122187 11211111 4/24/94 81241f17 1/24100 12122J17 11211111 4/24/94 111241f17 1/24100

Dote 8arr4Jled Dote 8arr4Jled DoteSo~

= LJnearF1t I =UnoorFil I = LJnearFil I(U-Fit ) (UlarFil ) (l.irGr Fit )

Port "" (ugIg). "".05 0 1.31tH! DoteSo~ Port Cu (ug/O) • 481.nl 0 3.aa.a Dote 8arr4Jled PortZn (ug/0)··13457+ 7._ Dote Soll'4'led

(s-.,yafr. ) (Suamay.... Fi< ) (Suamayafr. )RSqu.. 0.10302ll ~e 0.000427 RSquoro 0.001991RSqu-.Adj 0.055lI17 ~eAdj -G.05211 RSqu-.Adj -G.03I2Roo! _ Squoro Em><

381.8127 Root _ Squore Error 115.4034Root _ Squoro Em><

7017.154_of,,-- 194.7143 _of,,-- 350.5238 _of,,-- IOlIl.e52CboermIona (or Sum WgIe) 21 0b0erva1I0ne (or Sum WgIe) 21 ~lorSumWgle) 23

(Aoolym afVIrixloo ) (AnoI)mafV...... ) (AnoI)mafV""" )8ou>ce OF Sum of Squoroe Moonllqu.. FRetio - OF Sum of Squoroe Iol... Squoro FRetio - OF Sum of llquaree MoonSquoro FRetio- I 317110.2 317110 2.1123 - I 270.28 270.3 0.0081 - I 057tt88.tt 057tt80 0.1005

Error 18 2786031.1 145828 - Em>< 10 853113.88 34374.4_F

!!nor 21 10541l911oo eo237871 -CTOIlII 20 3084748.3 0.1580 CTotaI 20 853383.24 0.0201 CTotaI 22 1084582288 0.8880

C-......... ) (-- ) (-- )T""" EIlImete BId Em>< I Rollo -Ill Term EIlImete BId Error 1Retio -111 Term EIlImote BId Error tRoIlo -111IntetwpI "".04a4 _.ne 1.75 0.0883 I-.ept 481.nlO2 1234.301 0.37 0.7125 I-.ept -13457.05 40381.01 -G.27 unoDote Soll'4'led -G.OOOOOI 8.1520-7 -1.48 0.1580 Dote So....ed -3.lno-l 4.3010-7 -G.GO 0.0201 Dote 8arr4Jled oo75סס0.0 OO17סס.0 0.44 0._

Figure 4-40. Time Trends in Particulate Lead, Copper, and Zinc.

105


T bl 4-19 W Qa e ater ualitv Summary Statistics for Walsh Ave

~otalCu Dissolved Cu Particulate Cu

h'ss (ma/l) I (Lla/l) I (j.lg/I) % Diss. Cu ' (Ila/a)

Mean 92 44 16 43% 367Median 78 37 13 36% 314!Standard Dev. 63 26 11 19% 194

CV 0.69 0.59 0.71 0.45 0.45

IN 27 32 25 25 25

rrotal Ph Dissolved Pb Particulate Pb

trss (m!!'/!) I (Ll!!'/l) (Lla/I) % Diss. Cu (Ll!!,/g)

Mean 29 67 4.5 10% 706Median 22 58 3.1 8% 633Standard Dev. 22 50 4.3 11% 387IrV 0.76 0.74 0.96 1.04 0.55

IN :!I2 32 24 25 22

trotal Zn Dissolved Zn Particulate Zn

rrss (rna/I) (Lla/l) (j.lg/l) % Diss. Cu I (Ll!!'/a)

Mean 10 1218 682 54% 8067Median 7 ~75 660 51% 545:!1Standard Dev. 9 602 381 28% 6804

tv 0.87 0.49 0.56 0.52 0.84

N 4 30 25 22 24

Factors Contributing to Variation in Event LoadsIn contrast to results from large waterway

stations, approximately half the copper, nickel,and zinc in the Walsh Ave catchment are found inthe dissolved p.hase.· Lead was mostly associatedwith the particulate fraction.

Results ofANOVA for copper, lead and zincon the small storms are shown in Table 4-20.

For all parameters variations in bothconcentration and flow are important indetermining event loads. The relative importanceof each load component changes for eachparameter. The results of the load componentanalysis indicate parameters with the mostsignificant time trend in event load (solids, lead,and nickel) were also the most strongly influencedby pollutant concentrations.

Variations in lead event loads were the moststrongly influenced by lead concentrations,probably as a result of decreasing lead

concentrations related to the phase out of leadedgasoline. In contrast, concentrations of zinc hadthe least influence of any parameter on eventloads and zinc had the highest unexplainedvariability of all the parameters. The large amountof unexplained variation indicates the importanceof unexplained systematic or random factors thatinfluence event loads.

When the high flow events were notexcluded from the analysis" variations in flowaccounted for much more of the load variability as

Table 4-20. Percentage of Load Variability Explainedby Different Load Components (Based on ANOVA;

storms less than 60,000 cubic feet total flow).

Param- Concentration Flow UnJmowneterSolids 44% 43% 13%Copper 34% 41% 24%Nickel 42% 34% 24%Lead 68% 22% 10%Zinc 22% 38% 30%

106

shown in Table 4-21.Copper concentrations were not particularly

elevated in most of the samples. Urbanbackground concentrations are around 45 :g/lwhich was the overall average of theconcentrations found in the entire dataset.Detecting changes in copper loads would requiredecreasing copper concentration below this urbanbackground level.

Conversely, zinc concentrations remamelevated in runoff. Detecting changes in zincconcentration should be feasible; assuming thesource of zinc could be located and eliminated.

4.3 Results in the Program Area

4.3.1 Socia/Indicators


Direct coordination by Program staff, andfunding of additional projects which involved thepublic in urban runoff education and pollutionprevention, constituted the main "publicinvolvement and monitoring" activities that theSCVURPPP Co-permittees sponsored joindy andimplemented through the Program. In addition,Co-permittees implemented activitiesindependendy under the auspices of the Program,as discussed below.

The Program maintained an "800" numberfor residents to request information regardingstormwater runoff and pollution prevention.buring the 1997-98 fiscal year, Program stafffielded 210 calls. Program staff also:

1. Presented informational displays anddistributed outreach material at approximatelytwo community events per fiscal year (since1995).

2. Made an average of two presentationsconcerning urban runoff pollution preventionper fiscal year to community and businessgroups.

3. Sponsored and assisted Co-permittees inorganizing two countywide creek clean-upevent.

Participation in creek clean-up events waswell documented, but attendance at community


Table 4-21. Percentage of Load VariabilityExplained by Different Load Components (Based on

ANOVA, a11storms).

Percentage of Load Variability Explained by:

Parameter Concentration Flow UnknownSolids 4% 94% 2%Copper 12% 42% 46%Nickel 14% 43% 43%Lead 32% 34% 34%Zinc 7% 64% 29%

events and presentations was seldom documentedin the materials examined. Creek clean-up eventshave been successful as measured by increasedparticipation each year (from 1 event and 600volunteers in 1995-96 to 2 regular events and over1800 volunteers in 1997-98), extensive mediacoverage, and positive feedback from Copermittees and volunteers. Program staff alsoworked on regional level activities through theBay Area Stormwater Management AgenciesAssociation's (BASMAA) PIP Committee.

In addition, the Program involved the publicin educational programs and monitoring activitiesby funding the following three PIP projects:

1. The Watershed Action Fund: designed toencourage and support pollution preventionand watershed awareness projects conductedby schools and community organizations hasmore than doubled the cash awards availablesince implementation in 1996-97.

2. Environmental Education Center: WithProgram support, the Center has been able todevelop three new interactive programsfocused on stormwater pollution, enhance itsschool education program to incorporatestormwater pollution prevention concepts,and increase the number of visitors to theCenter each year.

3. Coyote Creek Riparian Station (CCRS):StreamKeeper volunteers were trained byCCRS staff to recognize and properly reportincidents of runoff pollution and illegaldumping along their local creeks. Programsexisted for nine creeks in the Santa ClaraBasin and involved 73 members (as ofJune,1997). From August 1995 untilJune 1997,StreamKeepers along Coyote, Sari

107


Francisquito, and Los Alamitos creeksreported and responded to 25 calls regardingpollution or potential creek problems.

In addition to the joint contributions toProgram public involvement and monitoringactivities, each Co-permittee was involved in a

,variety of activities related to: school educationprograms, volunteer storm drain stenciling, creekclean-up events, Adopt-a-Creek programs,community events,workshops/classes/presentations, householdhazardous waste events, support of .communitygroups, and a variety of other activities

One of the most successful and bestdocumented were the school education programs.Seven of the twelve co-permittees reportedcoordinating and implementing School EducationPrograms. Five of the seven co-permitteesdocumented the number of students and teachersthat participated in" their programs - combined,there were 20,283 participants. Co-permittees thatdid evaluate their school programs found theprograms successful in involving a wide audiencein thinking about urban runoff pollutionprevention.

Public Attitude, User Perception

Fairbanks, Maslin, and Maullin (1999) detailsthe results of the 1999 survey of 850 Programarea residents. The report also compares the 1999results with a similar survey conducted in 1996. Asummary of the measurements of awareness,perception, and behaviors from these two surveysfollows.

AwareneuOne in four residents remembered hearing

or reading something about storm drain systemsrecently. Of these, a quarter rememberedwarnings against dumping substances in stormdrains and an additional 20 percent retained thatstorm drains flow directly to the Bay. Twenty-twopercent of those who had recalled something

. about storm drains remembered seeing the stormdrain stencil (about six percent of allrespondents). This number represents a slightdecline from 1996, when 29 percent of those whohad recalled something about storm drainsremembered seeing the stencils (about 11 percentof respondents).

108

Many area residents continue to havemisconceptions about how the storm drain systemoperates. Only two out of five residentsunderstand that sanitary sewers and storm drainsare separate systems, and only half .realize thatstorm drain flows are not treated before theyreach the Bay. These numbers did not differsubstantially from the 1996 results.

Sixty-five percent of area residents correctlynamed the Bay or creeks as the place where stormdrain flows end up; one in five (20 percent)incorrectly named the ocean. These proportionswere virtually unchanged since the 1996 survey.Those who had seen storm drain stencils, or whosaid they had recalled something about stormdrains recently, were significantly more likely toname the Bay or creeks as the final destination ofstorm drain flows.

Thirty-one percent of area residents haveseen something dumped down a storm drain; inthree out of four of those cases, it was either trash

.. or oil. This represents an insignificant changefrom 1996, when 27 percent of residents had seensome form of storm drain dumping. In 1996, oiland trash were also by far the most commonlydumped substances.

Area residents generally understand thattoxic chemicals pose a danger when poured intostorm drains. However, many do not understandthe harm that can be done by everyday items likeleaves, grass clippings, swimming pool water, orwater used to irrigate lawns or to wash a car.These results mirror the findings of the 1996survey.

Eight out of ten survey respondents weremost likely to label chemical and waste dischargesfrom industry as the major source of waterpollution, while a similar number pointed tohazardous wastes (75 percent). These percentagesrepresented significant increases from 1996. Incontrast, just 41 percent of respondentsrecogni2ed discharge from storm drain systems asa major source of water pollution, a numbervirtually unchanged since 1996.

Santa Clara Valley residents were found tohave a generally poor understanding ofwatersheds. While roughly one in four said theyhad seen or heard something about watersheds,only 42 percent of this group (or about elevenpercent of all area residents) had any clear

memory of what they had learned. When askedwhat the term ''watershed'' means, only one infour were able to define the term correctly, withan equal number identifying a watershed as abuilding used to store water.

PerceptionsSanta Clara Valley residents perceived traffic

congestion as the area's most serious problem; 72percent of survey respondents rated it as a veryserious problem, up sharply from 59 percent in1996. On the other hand, unemployment (18percent) and crime (29 percent) were seen as veryserious problems by far fewer respondents thanthree years ago. Forty-five percent of arearesidents perceived pollution of the environmentas a very serious problem, a slightly smallernumber than in 1996.

Water pollution remained as serious aconcern for area residents as it was in 1996. Halfof all respondents consider pollution of the Bay avery serious environmental problem, and nearly asmany said the same for pollution of local creeks(43 percent), pollution of drinking water (42percent) and pollution of wetlands (33 percent).

Residents believed that local reservoirs, riversand creeks are best used for drinking water and ashabitats for wildlife; four out of five respondentssaid such uses are very important. "Irrigation ofcrops or other agricultural uses" was the onlyother category labeled by a majority ofrespondents (65 percent) as a very important useof the area's water resources.

Area residents were inclined to fault largeindustrial or manufacturing companies for waterpollution, and attributed far less responsibility toprivate residents. Sixty-eight percent said suchcompanies were very responsible for waterpollution, while just one in five said the same forprivate residents.

Seventy percent of the survey respondentsindicated that the message "storm <!rain pollutiondestroys the environment for our children andfuture generations" would be very effective ingetting people to change their behavior. Themessage that earned the next highest percentage(67 percent) was "storm drain pollution makesfish and seafood unhealthy for humans to eat." Inthird place was "storm drain pollution ruins ourlocal creeks so fish, birds and wildlife can't live


there" with 61 percent of those surveyed saying itwould be very effective. These findings indicatedno significant change from 1996.

When asked whether various natural featuresmake "extremely important" contributions to theValley's quality of life, 79 percent labeled theocean extremely important. Seventy-two percentgave the same evaluation for the Bay, and 69percent said the same for the region's air quality.These were the top three responses in both 1996and 1999. Four out of ten respondents eachlabeled creeks and wetlands as extremelyimportant to the quality of life in the Valley,figures virtually identical to those found in the1996 survey.

BehaviorValley residents continued to express great

willingness to take a variety of actions to preventwater pollution (particularly supporting fundingfor public education, reporting illegal dumping,and using non-polluting brake pads). There weresignificant, though small, increases since 1996 inthe proportion of residents proclaiming awillingness to use household hazardous wastecollection centers, install non-polluting brake padson their cars, and use non-toxic substances inplace of pesticides and herbicides. However, theproportion of residents who actually engaged insuch behaviors did not increased substantiallysince 1996.

Gardening and yard care continued to be themost common do-it-yourself activities in the SantaClara Valley, with seven out of ten respondentsresponding they participate. Forty-five percent ofrespondents said they paint their houses, and 42percent washed their cars at home. These resultswere almost identical to those obtained in 1996.Similarly, 30 percent of respondents said theychange their own motor oil, an insignificantchange from 31 percent in the 1996 survey.

Nine out of ten respondents visited oceanbeaches at least occasionally, and roughly, four infive visited the Bay that often. Majorities made atleast occasional visits to reservoirs, creeks andcreek trails, and nature or interpretive centersalong the Bay. None of these proportions havechanged significantly since 1996. Local wetlands(41 percent) and the Guadalupe River (33 percent)

109


were used by much smaller numbers of Valleyresidents.

VVhen asked an open-ended question aboutwhat sources they would turn to if seekinginformation about ways to prevent pollution, themost frequendy named responses were local

. government offices (33 percent), the phone book(13 percent), the garbage company or publicworks department (14 percent), the watercompany or water district (ten percent) and theInternet (nine percent). These percentages wereroughly comparable to those obtained in the 1996survey, though the proportion identifying theInternet increased substantially (from one percentin 1996 to nine percent in 1999).

VVhen asked to evaluate different modes ofcommunication, half of all respondents said theywould definitely pay attention to classroomprograms in schools (50 percent)· and televisioncommercials (50 percent). These were also the toptwo responses in 1996. In 1996, 44 percent ofrespondents said· they would definitely payattention to newspaper articles and thatpercentage remained roughly the same in the 1999survey (40 percent). Generally, though, thereappeared be to an across-the-board decline since1996 in the proportion of respondents respondingthey would definitely pay attention to each of themodes of communication tested

110

CHAPTER 6 - UTILITY OF INDIVIDUAL INDICATORS AND RECOMMENDED REFINEMENTS

5. Application of Results to Watershed Managementand Program Implementation in the Santa Clara Basin

This project included the application of 12indicators in the Coyote Creek watershed, sixindicators in the Walsh Avenue catchment, andthree indicators in the SCVURPPP Program areaas a whole.7 The project objectives were toevaluate and refine the indicators, as well as tojudge the usefulness of the 2-level indicatormethodology.

The demonstration project tested whethersystematic application of a group of indicatorswould assist the Santa Clara Valley Urban RunoffPollution Prevention Program to characterizebaseline conditions in the watershed (Level I inthe Claytor and Brown methodology) and toassess and re-evaluate their management program(Level II).

As a first step to assess the generalusefulness of the indicators (and the indicatormethodology), we first review (in this chapter)how the indicator results are being used toimprove watershed management and programimplementation in the Santa Clara Basin.

In Chapter 6, we consider indicator benefitsindividually, i.e. compare our experience in theSanta Clara Basin with the specific usefulcharacteristics Claytor and Brown ascribe to theindicators in their "Environmental IndicatorProfile Sheets."

In Chapter 7, we review the usefulness of thecollective results in the context of Claytor andBrown's proposed framework and methodologyfor using the indicators.

5.1 Application to Watershed Management

The results of applying physical,hydrological, water-quality, and biologicalindicators in Coyote Creek illustrate the complexnatural history of this watershed. Quantitativeresults of these indicators are influenced by

7 The Pennitting and Compliance Indicator (#23) wasimplemented in both the Coyote Creek watershed andthe Walsh Avenue catchment. There were 20 indicatorsimplemented in all.

seasonal and year-to-year variation in rainfall andrunoff and by natural variation in flow, gradient,geology, and vegetation along the stream corridor.These dynamic relationships also contribute torandom variation in the parameters we measured.

However, spatial trends were evident alongthe urbanization gradient for most indicators thatwere field-sampled. Temporal trends were lessevident, perhaps due to inadequate quality anddocumentation of historical data.

Our reference sites were located inundeveloped areas at higher elevation and withsteeper slopes than the urban areas on the Valleyfloor. Over the past few decades the urban areahas expanded beyond the region of consolidatedsoils and into an area of unconsolidated soils (andgroundwater recharge) further up the alluvial plain(Fig. 3-5).

Downstream from Anderson Dam, CoyoteCreek flows are controlled by releases and waterdiversions; the entire system is characterized bymyriad influences of the region's history ofdevelopment, including mining and agriculture.Figures 3-5, 4-1, and 4-2 partially illustrate theextent of damming, diversion, mining, andchannel alteration within the watershed.

In many areas, the stream channel is stilladjusting to the effects of past gravel mining, andriparian vegetation has not returned. In theurbanizing and urbanized reaches, the influence ofwatershed imperviousness, piped drainage andoutfalls, and channelization are apparent, while anurban park chain has preserved riparianvegetation. Throughout lower Coyote Creek,habitat change, together with the introduction ofexotic species of fish and other aquatic life, havecreated an ecosystem that - although it retainsviable populations of native species - isfundamentally altered.

Riparian cover was consistently around 80%at the sites sampled in the urbanized area (Fig. 46), as a result of the City of San Jose's park chainalong Coyote Creek, and was lower in theupstream reaches, where the creek is dry for muchof the summer.

111


Concentrations of arsenic, chromium andcopper in Coyote Creek embedded sedimentswere similar to those found 20 years ago and didnot appear to show any relationship to urbandevelopment. However, the spatial relationship oflead concentration to stonn drain outfalls is

, readily apparent (Fig. 4-20). The elevated levels oflead in sediments from the urbani2ed portion ofCoyote Creek most likely result from erosion oflegacy deposits near major roadways. This mayprovide perspective on the behavior of other"legacy" pollutants, such as PCBs or chlorinatedpesticides. A more complicated relationship isapparent for mercury (Figures 4-20), a pollutant ofconcern in San Francisco Bay. Here the patternsuggests other sources in the watershed, possiblyincluding abandoned mercury mines in thewatersheds of tributaries that enter Coyote Creekalong its lower reaches. The significant apparentdecline in sediment mercury concentrations over20 years (Figure 4-21) may represent changes in awatershed source (e.g. "washing out" of legacydeposits from mining).

Coyote Cre.ek continues to supportsignificant native fish populations, includingrainbow trout. Comparison with the projectbaseline study (pitt and Bozeman, 1982) and otherdata suggests that fish assemblages are similar towhat has been observed since 1950. However,there was significandy less biomass of introducedfish in rural reaches and significantly more nativefish in urban reaches than in 1997-1980 (Table 47, Figure 4-23), suggesting that the population ofnative fish in the creek may have increased. Thismay be the result of increased summer streamflowfrom dam releases. Peak stream flow inantecedent years may also influence the relativeincidence of native and non-native fish species.

Improvement in the urban reaches is alsosuggested by increases in the mean percentage ofEPT taxa since 1977-1980' (Figure 4-29).However, there are. still significandy more lowtolerance macroinvertebrate taxa in the rural andreference reaches as compared to the urbanreaches (Figure 4-30), indicating that water qualityis still a concern in the lower reaches of thewatershed. This may be confirmed by thedepressed dissolved oxygen levels (Figure 4-27) atStation TS-1.

112

Despite the improvements in the urbanizedreaches, the past 20 years' development at theurban fringe has apparently resulted in declininghabitat or water quality, or both, in the affectedreaches of Coyote Creek. This was shown by asignificant decrease in the percentage of EPT taxaat these stations. Qualitative comparison of thenumber of native fish species and percentage ofnative fish species at stations sampled in 1999 and1977-80 (Figure 4-23) suggests that the boundarybetween urban and rural reaches (as characterizedby this indicator) has shifted upstream as a resultof ongoing development.

Consistent with this latter finding, cumulativepercent subwatershed imperviousness wasstrongly associated with several fisheries metrics ata species level (number and percent of tolerantspecies; number of nonnative species, and allspecies) and an individual level (number of nativeindividuals, number of diseased individuals)(Table 4-8, Figure 4-26). The steepest responsewas noted between 8% and 11% imperviousness.

These 'results suggest that the Santa Clara"Basin Watershed Management Initiative shouldfocus on limiting the effects of futuredevelopment on stream hydrology and riparianareas (particularly in the "transition" areas at theurban fringe). There is also apparent potential toobtain significant improvements in the habitatvalue of urban reaches, possibly by improvingsummertime flows and water quality. In addition,there may be opportunities to improve habitatthrough restoration of some in-streamcharacteristics.

5.2 Application to the Santa Clara ValleyUrban RunotIPollution Prevention Program

The biological indicators suggest that effectson beneficial uses are associated with the presenceof stormwater outfalls. Stonnwater outfallscorrelate with increased sediment concentrationsof cadmium and lead, but the concentrationsfound are not associated with measurable impactson aquatic life or other beneficial uses. Results ofthe water-quality indicators were inconclusiveregarding any specific relationship to long-termloadings of the measured pollutants.

Although dissolved oxygen monitoring is notone of Claytor and Brown's water-quality

CHAPTER 5. APPLICATION TO WATERSHED MANAGEMENT AND PROGRAM IMPLEMENTATION

indicators, our use of continuous monitoringprobes (performed to support interpretation ofthe fish assemblage· and macroinvertebrateassemblage data) revealed that dissolved oxygenwas repeatedly depressed at one urban station, andthat a extreme and prolonged drop may have beenassociated with a fish kill at that location. Thissuggests that the most significant water-qualityproblem related to storm drain discharges may beacute and short term, rather than chronic, andrelated to conventional pollutants (biochemicaloxygen demand) rather than toxic pollutants. Thispossibility deserves further investigation andcould lead to significant changes in stormwaterpollution prevention priorities.

From 1993 to 1998, the urban runoffpollution prevention program achieved 70-folddecrease in illicit connections reported; aconcurrent trend toward fewer illegal dumpingreports (for most categories) suggest that the Copermittees' outreach, industrial/ commercialinspections, response to dumping incidents, andenforcement have had an effect. This is consistentwith surveys in the Walsh Avenue catchment,which showed a heightened awareness, amongfacility managers, of the need to implement "goodhousekeeping" measures to avoid the entry ofpollutants into storm drains. During this sameperiod, many industrial facilities became subject toa statewide NPDES stormwater discharge permit;however, compliance (as measured by filing ofapplications and reports) is relativdy low.

Zinc is the primary pollutant of concern inrunoff from the Walsh Avenue Catchment. Totalzinc concentrations remained at high levelsthroughout the monitoring period. This could bedue to accumulated zinc in the storm drain systemand in unpaved areas in the catchment or due tocontinued discharge from the facilities in thecatchment.

Significant decreases in solids, lead, andnickel in runoff from may be the result ofimprovements in housekeeping at businesseswithin the catchment. Decreases in lead areprobably due to elimination of tetraethyl leadfrom gasoline and decreases in solids loads ingeneral (lead was highly associated with solids).Decreases in nickel concentrations may be due to

improved management BMPs or changes inindustry types located in the catchment.

Results of some programmatic indicatorswere immediately applicable (and many wereimmediatdy applied) to improving the stormwaterpollution prevention program.

The primary value of tracking illegaldischarges, illicit connections, industrialinspections, and BMPs is the use of the resultingdata to target further inspection and enforcementefforts. Our results showed that this benefitdepends on the completeness and accuracy of thedata collected and the consistency with which it isrecorded. In addition, the use of relationaldatabases and georeferencing the data greadyenhance the targeting process.

We found that the methods used to collectand record data varied considerably betweendepartments and between municipalities withinthe Program, and all departments could benefitfrom additional technical support. The recentdevelopment of internet-based tools for enteringand storing data makes it feasible for staff fromthe various program municipalities to enter andupdate records in a centrally maintained database.Discussions with municipal staff led to a decisionto investigate the feasibility of a consistentreporting system and centralized, intemetaccessed database for recording illegal dumpingincidents.

Two of the social indicators - the PublicInvolvement and Monitoring indicator and theIndustrial/Commercial Pollution Preventionindicator reVealed the advantages ofincorporating interviews and other feedbackmechanisms into the Program's outreach efforts.

The surveys performed to implement thePublic Attitude Surveys indicator and the UserPerception indicator hdped the Program gaugepublic awareness of watershed issues and allowedthe Program to gauge how well a future campaignwill be received. The survey results will serve as abasis for the development of the Program'supcoming five-year Watershed Education andOutreach Campaign.

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6. Utility Of Individual Indicators And Recommended Refinements

Claytor and Brown (1996) describe a "utilityof indicator to assess stormwater impacts" foreach of their 26 proposed stormwaterenvironmental indicators.

Based on these descriptions, for each of the20 indicators we implemented and tested (Table 31), we developed specific questions applicable toour study (Table 6-1). We evaluated the utility ofeach indicator based on our ability to answer thesequestions using the data obtained from this study.

Consequently, our assessment of indicatorutility is biased by the specific circumstancesunder which the indicator was applied. Factorsthat affected the utility of more than one of theindicators included:

~ Region and climate. Interpreting the resultsof many of the physical/hydrological, waterquality, and biological indicators requiresconsideration of intra-annual and interannual rainfall cycles characteristic of thesemi-arid coastal climate.

~ Watershed scale. We implemented differentindicators at the scale of a 310-square-milewatershed, a 28-acre catchment, and the 13municipality program area.

We have attempted to identify where thesefactors affect the utility of specific indicators.

Each indicator we tested, and its usefulness,is discussed below. The numbering follows that ofClaytor and Brown (1996).

Recommended refinements to each indicatorare presented in Table 6-2.

6.1 Water Quality Pollutant ConstituentMonitoring

Even with relatively large datasets, it may beimpossible to detect changes in water qualityconstituents due to implementation of BMPs orchanges in land use. The model we developedwas able to account for a large percentage of thevariability of many metal constituents (whenmeasured as total recoverable metals). Afterapplying the mode~ changes of 40% could bedetected with reasonable sample sizes.

However, it is unlikely that such an extremetrend would actually occur in Coyote Creek. Totalmetals are often associated with suspended solids,and concentrations tend to reflect changes in theamount of erosion and sediment/bedloadtransport occurring in the stream channel. InCoyote Creek particulate copper concentrations in"wet" winters decreased by approximately 50%from particulate concentrations in "dry" winters(Dry Winter =133 ug/g; Wet =65 ug/g).

Monitoring during dry winters or selectinglower flow events may help overcome theinfluence of constituents in eroded soils.

It might be more feasible to detect trends inpollutant concentrations in storm draindischarges, rather than creek flows. \.We did detecttrends in pollutant concentrations frommonitoring the storm drain in the Walsh Avenuecatchment. See the discussion of Indicator #3,Nonpoint Source Loading, below.) However,measuring concentrations of pollutants in runoffstill in storm drains is somewhat less relevant tothe objective of protecting beneficial uses, sincepollutants in storm drains may be diluted, settledout, or transformed upon reaching downstreamwater bodies.

The most significant effects of water-qualityconstituents on aquatic life in Coyote Creek maywell be due to conventional pollutants (i.e.,biochemical oxygen demand) rather than toxicpollutants, and may be short-term, rather thancumulative over time. Continuous monitoring ofstream pools for dissolved oxygen (andtemperature, pH, and conductivity) insummertime, coupled with targeted surveillance ofdry-weather flows, may be more useful thanmonitoring storm flows for toxic pollutants.

6.2 Toxicity Testing

Toxicity testing was not useful because thetest was too sensitive and no changes (positive ornegative) could be observed. The usefulness oftoxicity testing to determine trends could beimproved if dilution series were incorporated into

115

Table 6-1: Claytor and Brown (1996) indicator utility and SEIDP criteria used to evaluate each indicator.

Indicator

COYOTE CREEK

Physical and:flydrologicallndicaro[S

Utility of Indicator to Assess Stormwater Impacts

(as interpreted from Claytor and Brown 1996)Criteria Used to Evaluate Indicator

(SEIDP 1999)

=> Can.be used to identify streamsegments susceptible to channel => Can indicatOrbe used to identifyebaugesinchannelmorphologyStream WuIening and DownCutting eroSlOn. in Coyote Creek as the watershed becameurbanized (early 1980'5

=> Can document the rate of change to channel geometry as a to present)?

£unaion of increased urbanization. => Is it possible to evaluate whether any influence on channel

=> Useful in estimating the effectiveness of stormwater Best morphology could be attributed to BMPs implemented in

Management Praaices and document the locations where conneroon with the stormwater Program (Initiated in the early

additional controls are needed. 1990s)?

=> Useful in estimatinghabitatqualitybydeterminingifexcessive => Determine whether to recommend channel widening and

flow rather than water quality. are limiting factOrs for aquatic downeutting as an indicator for impacts related tourban run-offhealth. and as a measure of stormwater Program effeaiveness.

=> Can assist municipalities to develop better stormwatermanagement criteria to reduce streambank erosion.

Physical Habitat Monitoring => CaD help isolate and assess whether water quality orhabitat is => Can this indicator be used to determine whether habitat and!orlimiting aquatic biological health. water quality are factors limiting aquatic biodiversity?

=> -Can help identify specific causes of degraded habitat, e.g., => Can this indicator be used to identify creek reaches withuncontrolled stormwater runoff. restoration potential?

=> Can evaluate restoration potential. => Is this indicator useful for identifying causes of habitat

=> Can be used to enhance physical structure of astre:un system; degradation?

to increase or maintain habitat.

Increased Flood Frequency => Can be used to assess the frequency, duration,"and quantity of => Assess the location, frequency, duration, and quantity offloodingflooding with increasing urbanization. associated with increasing urbanization.

=> Can be used to evaluate the effeaiveness of struetUI'a1BMPs in => Evaluate flooding potential associated with land usereducing the potential of flooding and streambank erosion. development patterns.

=> Can be used to evaluate flooding potential associated with => Identify flood prone areas.different land use development patternS.

=> Can indirectly predict potential for streambank erosion andhabitat degradation.

Stream Temperature Monitoring => Can be used to assess the effects of urbanization on stream => Do spatio-temporal comparisons among monitoring stationstemperature base flows and storm flows. indicate:

=> Can be used to assess the effects of BMPs on stream => Where land uses have changed since the 1977-80 baseline study?temperatures and help in promoting praaices, which have => Reaches that could benefit from riparian buffer enhancement?fewer impacts.

Table 6-1: 1 of 6


IndicatorUtility of Indicator to Assess Stormwater Impacts

(as interpreted from Claytor and Brown 1996)

=> Can help identify stream reach lengths that may benefit from. riparian buffer enhancement.

=> Can be used as a watershed land use planning tool in protectingcool water stream systems.

Criteria Used to Evaluate Indicator

(SEIDP 1999)

=> Is this indicator a useful measure of stormwater programeffectiveness?

: Wa'ter QualiTy Indicators' ;: : : _ . '. .,: .~ :'.'.~ ;._~ .: .. '. .~ .. - _. ..' . - . . . . -. : --. : ~

Water Quality Pollutant Constituent

Monitoring

Exceedance Frequencies of Water

Quality Standards

Sediment Characteristics and

Contamination

=> Monitoring results from long-term efforts can be used toidentify trends in water quality over time.

=> Monitoring results may be compared to reference rural orglean impacted" watershed to assess the degree of impairment.

=> Trends may result from land-use changes in the watershed orwatershed restoration efforts.

=> Can characterize water quality impacts due to urban runoffwith respect to various storm categories (frequent storms, floodevents).

=> Can identify long-term and seasonal trends in regional waterquality.

=> Can document periods of poor water quality (e.g., followinglarge storm events; during low-flow summer months).

=> Can be used to evaluate the performance ofstormwater BMPswith respect to various storm frequencies.

=> Can indicate contamination levels in urban embayments, andby proximity, the probable source of contamination in thedrainage area.

=> Samples taken within and/or immediately upstream anddownstream of stormwater management facilities can be usedto evaluate BMP performance.

=> Analyzing trends in sediment pollutant levels may indicatelong-term changes in pollutant loadings.

=> Over a long period of time, analysis of sediment pollutantlevels may be used to evaluate stormwater management effortsto control particular pollutant sources.

=> Has water quality changed during the monitoring period?

=> How much of the variability is due to hydrologic factors?

=> Can the influence of hydrologic factors be accounted for andhow much monitoring would be required to determine a givenchange in water quality with and without accounting for changesin hydrology?

=> Are water quality standards exceeded?

=> Are time-trends in the number of exceedances of water qualitystandards present?

=> Is it possible to show further improvements in the number ofexceeclances of water quality standards?

=> Have the concentrations of surficial sediment contaminantsincreased since the 1978-79 baseline study?

=> Do the concentrations of sediment contaminants in theurbanized area differ from those at the reference sites?

=> Are the concentrations of sediments a function of increasedurbanization?

=> Does this indicator provide a useful measure of stormwaterprogram effectiveness?

=> Can the protocols be rermed to make this indicator moreeffective?

Fish Assemblages

=> Can characterize the existence and severity of aquaticecosystem degradation and help identify causes and sources ofdegradation.

=> Can be used to evaluate the effectiveness of restorationprograms and help prioritize sites for future evaluation.

=> Do spatio-temporal comparisons among the station groupsindicate where land uses have changed since the 1977-80 study?

=> Can the revised Rapid Bioassessment Protocols (Barbour et aI.1997) be applied to the Coyote Creek Watershed?

=> Is this indicator a useful measure of stormwater programeffectiveness?

Table 6-1: 2 of 6


Indicator

Macroinvenebrate Assemblages

Utility of Indicator to Assess Stormwater Impacts(as in from Claytor and Brown 1996)

~ Can be used to help evaluate the effectiveness of BMPcontrols.

~ Can be used on both a regional and local leveL~ Can help identify barriers to fish migration.

~ Can be used to mobilize public suppon when popular speciesare impacted.

~ Can be used to characterize the existence and severity ofaquatic ecosystem degradation.

~ Can help screen potential sources and causes of suchdegradation.

~ Can help assess the performance of watershed restoratibnmeasures (particularly in-stream habitat restoration projeas).

~ Can help evaluate the performance of stormwater BMPs.

~ Can be used to identify shon-term impacts (e.g., from newconstruaion) to aquatic ecosystems.


(SEIDP 1999)ettecuveDeSSl

~ Can the protocols be refined to improve effectiveness; if so,how?

~ Do spatio-temporal comparisons among the station groupsindicate where bnd uses have changed since the 1977-80 study?

~ Can the revised Rapid Bioassessment Protocols (Barbour et at1997) be applied to the Coyote Creek Watershed?

~ Is this indicator a useful measure of stormwater programeffectiveness?

~ Can the protOCOls be refined to improve effectiveness; if so,how?

Pro~rJmmatjc lndicawrs '

Number of Illicit Connections

Identified!Corrected



~ .Quantifying the number ofillicit conneaions identified andcorrected can characterize pollutants that have a direct andimmediate effect on water quality.

~ Estimate the frequency and severityof illegal discharges to thestorm drainage system

~ Can be used as a measure to assess the effeaiveness of amunicipality's overall stormwater program.

~ Can be used to identify potentially significant contributors ofpollutants.

~ Can be used to assess the levd of industrial support forstormwater management efforts.

~ Can be used by NPDES program managers to assesscompliance with regulations and designate areas forimprovement.

~ Allows identification of uncontrolled sources ofpollution tostormwater.

~ Can estimate existing and potential cumulative impacts ofurbanization on aquatic ecosystem funaions.

~ Used as a planning tool inmaking zoning and masterplanningdecisions.

~ Do Co-permittee ICID programs identify the type, location, andfrequency of pollutants introduced illegally to the stonndrainsystem?

~ Is information reponedby ICID inspectors useful for improvingefforts to reduce occurrences of100 incidents?

~ Is information reponedbyICID inspectors useful for evaluatingpotential pollutant effects on water quality and on biologicalcommunities?

~ Is information available in existing databases useful to identifypotential impacts of pollutants on aquatic communities?

~ Is data quality sufficient to support evaluation of stormwaterprogram effectiveness?

~ Can estimates of imperviousness be correlated with indicatorsfor aquatic health?

~ Can imperviousness estimates be used to assist planners withfuture development strategies?


IndicatorUtility of Indicator to Assess Stormwater Impacts

(as interpreted from Claytor and Brown 1996)

=> Used to evaluate effectiveness of Best Management Practices(BMPs) in extending development thresholds.

=> Cost-effective (compared, for example, to hydrologicmodeling).

=> An easily quantified indicator of urbanization.

=> Well-understood within a variety of professional disciplines.


(SEIDP 1999)

=> Can imperviousness be used to monitor BMP effectiveness?

~ Social IDdic~tors '_"', -', . - C', - .' • , " ',. • ' " • -. •


Public Attitude Surveys

User Perception

=> Can be used to help modify citizen behaviors related to sourcecontrols.

=> Can help reduce monitoring expenses and expand ajurisdiction's monitoring database.

=> Can help identify pollutant sources through citizen watchdogactions.

=> Can educate students about water pollution issues.

=> Can generate political support for additional stormwater andwatershed funding.

=> Can foster acceptance of projects through close relationships.with communities.

=> Can assess the publics perception ofexisting or proposed waterquality problems.

=> Can identify the relative value the public places on a particularwater quality issue and thus a foundation for political action.

=> Solicit public or private funding for a particular water resourceISSUe.

=> Used to develop a public education campaign whichincorporates results of surveys into future programs.

=> Used to develop more effective pollution prevention programsbased on reported behaviors and target resources to watershedrelated activities.

=> Can assess the publics preception of conditions in thewatershed.

:=) Can educate the public about the hidden impact of waterquality pollution.

=> Used to generate steWardship programs and public support forwater restoration efforts.

=> Used to develop a public educational program whichincorporates the results of surveys into future programs.

=> Comparison to PIP goals.

=> Comparison to previous year's results.

=> Feedback from target audience, Co-permittees, educators, co-sponsoring organizations, or other staff involved.

=> Estimates of attendance or participation.

=> Amount of trash removed, miles of creek cleaned, etc.

=> Trends observed in pollution problems.

=> Observed changes in behavior.

:=) Do surveys indicate public's attitudes and awareness of urbanrunoff pollution issues?

=> Has the public's awareness or behavior changed as a result of theProgram's public education efforts?

=> Do surveys provide information to guide the Program in thedevelopment of future public education efforts?

=> Do surveys indicate public's attitudes and awareness of urbanrunoff pollution issues?

:=) Has the public's awareness or behavior changed as a result of theProgram's public education efforts?

=> Do surveys provide information to guide the Program in thedevelopment of future public education efforts?

Table 6-1: 4 of 6


Indicator

WALSH AVENUE CATCHMENT

W:ner Quality lndicawrs

Toxicity Testing

Non-point Source Loadings

Utility of Indicator to Assess stormwaterImpacts(as in • from. Clavtor and Brown 1996)

~ ConsideraBle amounts of existing data describe acute andchronic toxicity limits for various species.

~ Toxicity testing can be used to evaluate the effectiveness ofstormwater BMPs and other stormwater pollution reductionmeasures.

~ Results of toxicity testing can be used by waterShed managersto identify areas of high concern and to establish restorationpriorities.

~ Phase 1,n, and illTIE procedures can beused to help identifyspecific pollutant sources.

~ Trends in NPS pollutant loadings can be compared to land usechanges or implementation of BMPs to assess potentialincreases orreduetion in NPS pollution.

~ .embe used to help identify major land uses that are signifiClDtsources of NPS pollution.

Criteria Used to Evaluate Indicator(SEIDP 1999)

~ Is toxicity present?

~ Are toxicity time trends observed?

~ Are toxicity trends correlated to measured water quality?

~ Have pollutant loads changed during the monitoring period?

~ How much of the variability in pollutant loadings due tohydrologic factors?

~ Can the influence of hydrologic factors be accounted for, andhow much monitoring would be required to determine a given

. change in pollutant loads with and without accounting forchanges in hydrology?

Programmatic Indicarors .

Industrial/Commercial PollutionPrevention

BMP's Installed., Inspected andMaintained

~ em be used to assess industry's perception ofeffectiveness ofstormwater BMPs and methods for improvement.

~ Can be used to assemble and compare implementation costsbetween different industries and geographic locations.

~ em be a component of an industry stormwater educationalprogram which incorporates results into future pollutionprevention effons.

~ Can fOSter partnerships with industry and help managersmen· site conditions of which th ma be unaware.

~ Can expose weakness in BMP design, reveal maintenanceneeds, and determine needs for enforcement actions.

~ Determine if existing BMP's are sufficient in scope and size.~ Propose improvements to the design criteria for future BMPs.

~ Can it identify effectiveness of stormwater BMPs?

~ Can it determine if BMPs are cost effective?

~ em it provide guidance for improvements to future pollutionprevention efforts?

~ Does it provide information to refine BMP design andmaintenance strategies?

~ Are BMPs adequately installed, maintained and in the correctlocations?

Table 6-1: 5 of 6


IndicatorUtility of Indicator to Assess Stormwater Impacts Criteria Used to Evaluate Indicator(as interpreted from Claytor and Brown 1996) (SEIDP 1999)

=::) Provide useful data when conducting stormwater retrofitinventories.

Industrial Site Compliance =::) Can help to evaluate the performance of structural and non- =::) Can it identify the effectiveness of stormwater BMPs?

Monitoring structural stormwater BMPs. =::) Can it show correlation between industrial pollutants and water=::) Determine the industry's contribution to overall water quality quality?

degradation or improvement. =::) Can it be used to educate the public and influence political=::) Induce public education, support and activism. leaders and planning officials?=::) Solicit political pressure and support for planning issues. =::) Can it identify stormwater management needs, water quality=::) Determine industrial stormwater management needs, water trends and restoration sites?

quality trends and target restoration efforts.

=::) Identify areas where technical support or research are neededto help address problems.

Table 6-1: 6 of 6

Table 6-2. Recommended Refmements to Stormwater Environmental Indicators

Evaluate relationships between hydrology and water quality.=> Monitoring during dry winters or selecting lower flow events may help overcome the

influence of constituents in eroded soils.=> It might be more feasible to detect trends in pollutant concentrations in storm drain

discharges, rather than creek flows, with potential for continuous monitoring usingsondes.

Non-pomt Source oa gs.

Excee ce FrequenCles 0 WaterQuality Standards

=> Evaluate event hydrology to ensure flows are adequately measured.=> Incorporate dilution series into test design, especially for stations where acutely toxic

concentrations are expected.=> Consider factors such as duration ofexposure and temperature when interpreting results.

=> Screen event flow for large events or conduct a separate analysis for events ofdifferentreturn frequencies to ensure loads are not unduly biased by trends in flow. Adequate flowmonitoring is necessary to ensure accurate measurements throughout the flow range.

=> Monitor as many storms as possible within a season to provide a range of flows andquality data.

=> Monitor for multiple years to capture the effects of year to year changes in hydrology.

The indicator could be successfully applied only in cases where many exceedances haveoccurred for several parameters.Tracking the number of exceedances of total metals WQOs (as opposed to WQCs)showed more promise as an indicator of water quality.Estimate background concentrations either through a paired watershed design orevaluation of upstream or rural areas.Perform power analysis to determine adequate sampling frequency for trend detection.Note: stormwater sampling periods (typically 24 hours) don't necessarily coincide with thewater-quality criteria that apply to exceedances (e.g., 1-houracute toxicity or +day chronictoxicity criteria).

As with Indicator #1, trends may be more apparent when this indicator is applied tosediments in storm drains rather than in creeks.Improvements to samplingprotocolAddition ofiron, aluminum, lithium and other tracer metals to the analytical set in orderto enable analysis ofenrichment ofcontaminants relative to background conditions andassist in source identification.Sieve sediments through a 200 micron sieve prior to analysis.Establish background concentrations of the target analyres (lOcluding major elements tobe used for normalization) in the primary soil classifications contained in the watershedusing consistent methodology.Consider use of sediment traps to augment grab samples.Extend sampling program into tributaries and stormwater conveyances.Schedule sampling during the storm season at intervals based upon seasonal rainfallaccumulation.

For monitoring impacts related to impervious surfaces, this indicator is best used in smallwatersheds that lack major dams, have few or no channel modifications, and minimalalterations to the stream rhannel from mines and quarries.

=> Regardless of the context in which they are applied, stream geometty measurements areuseful only when they are appropriately monumented and repeated.

=> The use ofcross-section geometry measurements is preferable to interpretation ofaerialphotos.

=> To assess stream quality, it is important to characterize streams based on their structureand function.

Table 6-2: 1 of 4


Habitat Monitoring

Increase F 00 Frequency

Stream emperature Momtoring

Indicator Refmements

~ Use of habitat assessment approach that is adapted to regional stream conditions and datacollection standards is recommended.

~ In a smaller, less complex watershed. changes in study design to compare habitatcondition above and below outfalls. or catchments with and without BMP controls. mayimprove evaluations of urban runoff effects and effectiveness ofa stormwater program.

~ Indicator may best be used prospectively, in conjunction with a suite of indicators, inwatersheds that are beginning to urbanize. in order to develop temporal baseline dataoften lackin in hi hl urbanized re ions.

~ Measure changes in streamflows rather than location, frequency. and magnitude offloodevents. This is panicularly relevant for watersheds in which existing dams regulate thenatural hydrography.

~ Identify and estimate the impacts of instream infrastructure and channel modifications oncreek h dro ra hs.

~ Consider including other water quality parameters, particularly DO. which are useful forinterpreting the relative influences of water quality and habitat on biological indicators.

~ Consider using aerial photo interpretation rather than temperature monitoring where theprimary objective is to identify stream reaches needing riparian restoration.

~ Consider using temperature probes in stormdrain inlets, which would indicate whetherstormwater temperature is significantly different than receiving water.

Correlate assemblage shifts observed from historical data with major changes in humanactivities as evident from landscape changes (e.g., dam construction, channelmodification, etc.).

~ Correlate assemblage shifts observed from historical data with changes in streamhydrology - both caused by precipitation variation, and flow regulation.

~ protocols used for application of this indicator should be selected based on methodsdeveloped and recommended for the panicular region where it is to be implemented

Improvements to California Rapid Bioassessment sampling protocol~ Single sampling event may be sufficient and time of sample may be determined by natural

histoty of fish species (e.g. Spring season for rainbow trout)

~ Consider using single pass electroftshing method rather than three pass to increaseefficiency; method employed may be dependent on type of habitat sampled.

~ Data on length of individual ftsh was not used. Eliminating length or recording class sizewould reduce field time.

~ Field duplicates are highly variable and are not cost effective.

~ Monitoring of dissolved oxygen concentration was useful for interpretation of fishsampling results.

ges ~ Consider using sampling protocol that is adapted to regional stream conditions and datacollection standards.

~ To conduct temporal comparisons. it is critical that taxonomy be identified to a similarlevel in previous studies. In addition, it is imponant to maintain regional consistency insampling protocol to enhance data comparisons

~ Monitoring needs to be appropriately designed with respect to location and timing offactors contributing to degradation. This might require that the monitoring program beprospective (i.e. put in place prior to a significant change such as a stream restorationeffon or development of a tributary area) rather than retrospective.

Improvements to sampling protocol~ Sample effectiveness is limited in stream reaches that have low velocity and are

predominately silt and clay; sampling these habitat types may not be efficent.

~ The number of organisms counted and identified from a composite sample. and thenumber of subsamples varies among researchers. The California Department ofFish andGame is establishing standardized protocols.

Table 6-2: 2 of 4


Retitle indicator to "Public Attitude and Behavior".=> Focus on the behavioral changes in the public to evaluate Program effectiveness.=> Use a combination of broad and specific level surveys to obtain information to evaluate

stormwater Program effectiveness.

User Perception

BMP's InstMaintained

=> Clearly inform businesses of the intent and focus of stormwater pollution preventioninspections and conducting follow-up re-inspections on a timely basis.

=> Site visits were found to be very effective for information exchange. The written surveysalone would not have been as effective in determining the Program's impact and theindustries' understanding of the Program.

=> Conclusions regarding program effectiveness need to be based on pre-defmed,measurable program goals, and must be specified prior to evaluation.

=> Pre-establish standard formats and requirements for follow-up evaluations ofoutreachprograms, so that results are well documented.

=> Stormwaterprograms may most successfully apply user perception to justify resourcesand focus pollution prevention efforts.

=> There are many external variables that influence user perception and thus limit theusefulness of this indicator.

Consider the following when applying the indicator:: trends in population, transportation,and development growth; tormwater program self-evaluationsj changes in stormwaterprogramS, including targeted sectors, changes in staffand training, increased enforcement,and the use of new technology; increased outreach efforts; length of time program hasexisted.

=> Revising categories used for IClD incident reporting so that associated pollutants reflectthe category title.

=> Storing ICID incident data in arelational database that includes me street address and mepollutants likely associated with the incidents.

=> Georeferencing ICID data to identifyspatio-temporal patterns of incidents, guide futuretargeted pollutionprevention efforts, and facilitate efforts to analyze impacts ofpollutantson a uatic communities.

=> Consistent documenting inspections in an efficient record-keeping system. A relationaldatabase is suggested to systematically organize documentation by site visit, potentialpollutants, and best management practices.

=> The indicator's usefulness might be limited to businesses that are required to installspecific devices to prevent stormwater pollution rather than operational BMPs (e.g.,housekeeping, materials storage, and waste disposal).

=> Include in the indicator whether, and how, programs prioritize meir inspections.(Evaluatingprogta.m4 based onlyon the number of inspections does not indicate whet1lerindUStries with the greatest potential to discharge the most toxic pollutants are prioritizedfor inspections, nor the frequency of inspections.)

=> Encourage state regulatory agencies to integrate and coordinate implementation ofmeirstatewide general permits for specific activities with implementation of municipalstormwater NPDES permits.

=> Encourage state regulatory agencies to implement systematic review and documentationof SWPPPs, annual reports and updates to SWPPPs, and monitoring reports.

=> Encourage state regulatory agencies to provide businesses covered under general permitswith guidance for sampling and analyzing runoff. In addition, regulatory agencies shouldenter submitted water quality data into an accessible database that can be linked toeoreferenced facUi locations.

Table 6-2: 3 of 4


Indicator Indicator Refmements

Growth and Development Techniques for Estimating Imperviousness

=> Consider alternative methods to estimate existing and future percent imperviousness.Criteria for method selection should include the accuracy and availability of data sourcesas well as watershed size.

=> Document data sources and methods so that valid comparisons can be made to otherstudies.

=> Consider how estimates of imperviousness can be used to estimate runoff coefficients andpollutant loads

=> Distinguish among components of imperviousness attributable to major land uses.Consider how this information might be used in a strategy to reduce the effects ofimperviousness.

Application ofIndicator

=> To more closely associate impacts associated with imperviousness with aquatic ecosystemfunctions, consider selecting a subwatershed or catchment scale for analysis.

=> Compare changes in percent imperviousness with implementation of structural BMPs.

=> Track whether results from imperviousness analyses are influencing land-use planning.

=> Consider how increased imperviousness is accommodated by drainage systemimprovements.

=> Compare existing imperviousness to projected imperviousness to estimate the potentialurban impacts to streams, and to guide development and implementation of watershedmanagement strategies. Re-evaluate watersheds on a 5-year cycle, and reexaminewatershed management strategies.

=> After estimating the rate and pattern of how imperviousness is changing, consider usingthe indicator to select monitoring locations for hydrological, physical, and biologicalindicators.

Industrial Site Compliance Monitoring => Providing inspectors with routine refresher training. Inspectors should receive updateson Program activities and on new or revised BMPs. The inspectors should also be trainedto effectively communicate this information to site managers during inspections.

=> Better documentation of inspections would make it possible for stormwater programs touse the indicator to target outreach and document progress.

Table 6-2: 4 of 4


the test design, especially for stations whereacutely toxic concentrations are expected. Otherfactors such as duration of exposure andtemperature need to be considered wheninterpreting results.

Most importantly, samples from storm.drains may not accurately represent in-streamconditions, and so cannot be directly related topotencial ecological effects. Substances thatcontribute to toxicity may be diluted to non-toxiclevels or may complex with sediment and organicmaterials in-stream, greatly reducing theirbioavailability.

6.3 Nonpoint So~tceLoadings

We were able to identify a trend towardreduction in concentration of some pollutants,and to a lesser extent event loads, in the WalshAvenue Catchment. These may be attributable toimproved housekeeping and use of BMPs.

For all. parameters with significant trends(solids, lead, and nickel), decreases inconcentration were easier to detect and morestatistically significant than decreases in eventloads. This is due to the increased variability inevent loads because of the changing event flows.Use of loads for the assessment required adetailed examination of both the concentrationdatabase and the hydrology database.

Compared with Water Quality PollutantConstituent Monitoring (Indicator #1) thisindicator adds another layer of complexity andvariability while providing little additionalrelevance to protection of beneficial uses. This isparticularly true in semi-arid climates, where yearto-year differences in rainfall will strongly affectthe quantity of any runoff constituent (regardlessof natural or anthropogenic source) that reachesreceiving waters. One might ask the relevance ofachieving, say, a 20% reduction in average annualrunoff pollutant loading, when the year-to-yearvariation in loading (due to differences in rainfall)could be ten times that amount.

Much of the dataset used for this project wascompromised due to incomplete hydrology dataand problems with pipe surcharging (backing up)during high flows. (When flow is backed up,samples are no longer accurately flow-weighted,and an event mean concentration cannot be

126

calculated). Installation of a weir (to allowcalculation of flow) in the sampling manholeprobably contributed to surcharging. Analternative might be to use velocity sensors torecord flow.

The following information should beconsidered when applying Indicators #1 (WaterQuality Monitoring) and #3 (Nonpoint SourceLoading) to small catchments:

=:) Changes in businesses and business types.

=:) Annual and seasonal trends in event flows.

=:) Adequate flow monitoring to ensure accuratemeasurements throughout the flow range.

=:) Concentrations of pollutants relative tourban background.

=:) Length of time program has existed.

To enhance the utility of Indicator #3 werecommend:

=:) Evaluating event hydrology to ensure flowsare adeqUately measured.

=:) Screening event flow for large events orconducting a separate analysis for eventsofdifferent return frequencies to ensure loadsare not unduly biased by trends in flow.

=:) Monitoring as many storms as possiblewithin a season to provide a range of flowsand quality data.

=:) Monitoring for multiple years to capture the.effects of year-to-year changes in hydrology.

In industrial areas, sampling of storm drainsediments may be a useful substitute or adjunct towater-quality sampling. See Section 6.5.

6.4 Exceedance Frequencies ofWater QualityStandards

This indicator measures the frequency withwhich water quality standards have been exceeded;however, to understand potential effects onbeneficial uses, it is also necessary to review eachspecific standard and the degree to which it hasbeen exceeded. For example: Is a large exceedanceby a single parameter worse than smallerexceedances by many parameters?


In addition, it may be impossible toaccurately apply this indicator to stormwatersampling data, because stormwater samplingperiods (typically 24 hours) don't necessarilycoincide with the water-quality criteria that applyto exceedances (e.g., 1-hour acute toxicity or 4-daychronic toxicity criteria).

The indicator could be successfully appliedonly in cases where many exceedances haveoccurred for several parameters. Improvement inthe environmental health of the water body wouldthen be indicated by a decrease in the number ofexceedances and possibly in the number ofparameters that exceed. In Coyote Creek, despitethe variability in the data, tracking the number ofexceedances of total metals WQOs (as opposed toWQCs) showed more promise as an indicator ofwater quality. A high percentage of copper andlead samples still exceed the criteria, whichprovides an' opportunity for changes to beobserved.

Background levels of some constituents mayexceed the water-quality objective for chroniceffects. The objectives are based on the waterhardness. Objectives are lower in softer water.Stormwater mooff tends to be softer than baseflow (which is primarily groundwater), andtherefore, lower standards apply during stormevents then apply before and after. However,erosion and sediment transport during stormevents raise the concentrations of metals naturallyfound in sediments above dry weather levels.Since the standard is lower and the backgroundconcentrations are higher the chance ofbackground concentrations exceeding theobjectives is increased during the storm events.

In the case of Coyote Creek, since there areso few parameters that exceed the criteria fordissolved metals and the few that do only exceedchronic criteria (which is usually an overlyconservative criteria for most storm events)tracking the number of exceedances of dissolvedcriteria did not provide a mechanism to showadditional improvements in stream water quality.

6.5 Sediment Characteristics andContamination

In Coyote Creek, lead and cadmium, and to aless extent mercury, demonstrated spatial

differences that corresponded to urbanization.Mercury concentrations in Coyote Creeksediments appear to have decreased significandyover 20 years. Other metals (copper, chromium,arsenic) showed neither spatial nor temporaltrends.

Lead and cadmium concentrations in streamsediments appears to have a distinct relationshipto the influence of urban drainage, and may beuseful to correlate the general influence of thatdrainage on creeks over time. The measurementof lead concentrations over time could have theadditional benefit of characterizing the expectedpattern of slowly decreasing concentration instream sediments, a delayed result of reductions inthe use of tetraethyl lead as a gasoline additive.This may provide perspective on the behavior ofother ''legacy'' pollutants, such as PCBs orchlorinated pesticides.

Monitoring pollutants in sediment influencedby urban runoff appears to be a more robustindicator compared to the characterizing pollutantconcentrations in mooff (Indicator #1, WaterQuality Pollutant Monitoring). It is also more costeffective because sediments are easier to sample(i.e. "catching" storm events is not required) andbecause fewer samples are required to characterizethem (because there is less variation sample-tosample).

As with Indicator #1, the relationship of theindicator to beneficial uses will be somewhatindirect in cases where (as in Coyote Creek) theconcentrations measured are not associated withimpacts to biota.

As with Indicator #1, trends may be moreapparent when this is applied to sediments instorm drains rather than in creeks. Measurementof pollutant concentrations in storm drainsediments might be useful in connection withtargeted pollution prevention measures (e.g.BMPs), at specific industrial sites or in industrialareas, and may be an improvement over mandatedrequirements to sample runoff from these sites. Itwould be difficult to make these connectionsusing in-stream data.

The usefulness of this indicator could beimproved by a number of modifications to theprotocol and study design. The current studycould have been improved by including moredownstream sites to fully cover the urban portion

127


of the Creek. This study also demonstrated theimportance of consistent methodology in order toallow better spatial and temporal comparisons.Sampling and analytical protocols were not welldocumented in the historical data set and particlesize information associated with the older data set

, were incomplete. These factors limited the abilityto compare the two data sets.

One disadvantage of using this indicator isthe lack of accepted sediment quality criteria.However, if sampling and analytical protocols areadequately documented, temporal comparisonscan be made, as can comparisons with other localsites with similar land use and geology.

Several changes in the sampling protocolcould enhance the utility of this indicator:

=> Add iron, aluminum, lithium and other tracermetals to the analytical set to enable analysisof enrichment of contaminants relative tobackground conditions and assist in sourceidentification.

=> Sieve sediments through a 200 micron sieveprior to analysis.

=> Establish background concentrations of thetarget analytes (including major elements tobe used for normalization) in the primary soilclassifications contained in the watershedusing consistent methodology.

=> Consider use of sediment traps to augmentgrab samples.

6.6 [Indicator not Tested]

6.7 Stream Widening and Downcutting

Application of the indicator in Coyote Creekyielded inconclusive results because suitablebaseline (pre-development) data were unavailable.Even if such data were available, we consider itunlikely that widening and downcuttingmeasurements could be used to demonstrateimpacts of urbanization in this watershed. Dams,water diversions, channel modifications, historicalinstream mining, as well as infrequent catastrophicflow and sediment events, are all confoundingfactors affecting stream morphology in CoyoteCreek.

128

In highly variable and complex watersheds,the channel widening and downcutting indicatormay be useful for detecting the susceptibility ofreaches to bank erosion as a result of changingflow regime over time. In such conditions,however, this indicator does not appear to beuseful in identifying changing land use as theprimary cause for these changes.

For monitoring impacts related toimpervious surfaces, this indicator is best used insmall watersheds that lack major dams, have fewor no channel modifications, and minimalalterations to stream channel from mines andquarries. Stream segments above dams orheadwater areas that are being developed mayprovide the appropriate conditions.

As described in Section 4.1.1, we found itmore useful, when working at a watershed scale,to develop a geomorphic stream classificationbased on natural (geology, lithology, elevation,hydrology, and creek planform), andanthropogenic (land use and modified channelcharacteristics) factors.

To assess stream quality, it is important tocharacterize streams based on their structure andfunction. This facilitates an understanding of thevariables affecting a target assessment group, andleads to a more meaningful evaluation of thefactors affecting beneficial uses. Classifyingstream reaches also provides a mechanism forextrapolating site-specific data to other streamreaches that have similar characteristics.Classification serves as a tool for predicting futurestream responses to perturbations and as a basisfor selecting future sampling sites andmethodologies for field monitoring.

Basic stream processes have beencharacterized as part of a continuum (Vannote etal. 1980), in which headwater reaches typicallydeliver sediment to a stream, lower-elevation,lower-gradient reaches typically transportsediment, and reaches traversing relatively flatvalley floors typically receive sediment. Bothnatural and human factors influence how streams"work" to move water and sediment. Naturalfactors such as topography, elevation, gradient,geology, lithology, soils, vegetation, and drainagedensity influence stream hydrologic processes,channel dynamics, and ultimately habitat qualityfor aquatic organisms. Human factors such as


dams/reservoirs, diversions and discharges,percolation facilities, modified channels, miningactivities, and development, also modifybiogeochemical processes and alter streamstructure and function. Because both natural andhuman factors fundamentally influence streamstructure and function, they can be used to classifystreams into reaches or segments based onstructure and function.

The number of classes chosen to characterizestream structure and function should representthe range of relevant variation in a region and thelevel appropriate for detecting effects of humanactivity on stream function and biologicalcondition; however, it is critical to neither overnor under-represent the number of classes. Weclassified eight reaches that characterize differentfunctions in Coyote Creek. (fable 1, Figure 1).

For initial assessments we recommendevaluating the following three stre~ functions.This approach has been used successfully onCoyote Creek as part of a separate project(Buchan et al., 1999) and is consistent withnational guidelines developed for evaluatingfunctions of riverine wetlands8 (Brinson et al.,1995).

=> Maintenance ofCharacteristic Hydrologic Procemsand Channel Dynamics: This function refers tothe extent to which a stream reach exhibitsphysical processes and structural attributeswithin the natural range of variability of thehydrologic and hydraulic regimes. Physicalprocesses, structural attributes, andecological conditions include the amount,timing and duration of discharges, watershedcondition (e.g., percent ofurbanized/impervious surface), sedimentdynamics, and condition and behavior of thechannel and adjacent riparian vegetation.Variables used to assess this function mayinclude alteration to the hydrologic regime,sediment delivery to the channe~ andconditions of the watershed, flood-pronearea, riparian habitat and channel.

=> Maintenance ofAquatic Habitat Variation andRichness: This function refers to the capacity

8 Riverine wetlands are broadly defined to include thestream channel and adjacent riparian areas.

of a stream reach to support assemblages ofnative aquatic organisms through themaintenance of heterogeneous habitats.Variables used to assess this function mayinclude alteration to the hydrologic regime,sediment delivery to the chann~ riparianhabitat condition, surface water persistence,surface hydraulic connections, instreamchannel cover, instream and maero-/mierotopographic complexity.

=> Maintenance ofLAndscape-'UveIAquatic HabitatConnectivity: This function refers to thecapacity of a stream to allow for theupstream, downstream, and/or lateraldispersal and/or migration of native aquaticorganisms within a watershed via pennanentor intermittent channels or floodplain areas.This function encompasses opportunities foraquatic organisms to utilize and movebetween existing stream environments,colonize new habitats, or recolonize aquatichabitats following local extinctions.Variables used to assess this function mayinclude alteration to the hydrologic regime,sediment delivery to the chann~ channelcondition, surface hydraulic connections,longitudinal connections to up- and downgradient aquatic habitats, and condition offlood-prone area.

Initial pilot assessments based on thesefunctions should provide a programmatic basisfor prioritizing additional monitoring andassessment throughout the watershed. Byidentifying specific reaches and locations whereresources are exceptional (or may be threatened),and by documenting preservation andenhancement projects eurrendy underway, thepilot watershed assessment should help watershedstakeholders (including stonnwater programs) totarget future analyses and collection of field data.

For example, if the initial assessmentsuggests that a surplus or paucity of sediment isaffecting an otherwise high-quality reach, the nextstep may be an analysis of upland hillslopeprocesses and development of a sediment budget.Likewise, if modifications to the hydrologicregime appear to impair biological beneficial uses,subsequent steps may be to begin a dialogue with

129


agencies and entities that influence water supplyto identify opportunities for restoring base flows.As another example, uncertainty about suitablewater quality or temperature might lead tomonitoring of macroinvertebrate populations in aspecific reach and an in-depth analysis of changes

. in the distribution of riparian canopy over time.

6.8 Physical Habitat Monitoring

As implemented, this indicator was useful forcharacterizing restoration potential and causes ofhabitat degradation and in some cases indicatedwhether habitat and/or water quality were factorslimiting aquatic biodiversity. Measurements ofriparian canopy cover and streambank vegetationindicated that most of the creek was wellvegetated Exceptions included one of the ruralstations (R-3) that had been previously mined andwas virtually denuded. Disturbance at this sitewas also evident from measurements of substratewhich indicated unusually high percentages ofcobbles and gravels. Together, suchmeasurements clearly supported the cause ofhabitat alteration, and indicated potential forrestoring the habitat composition typical forreaches in this part of the creek. However, thedecision to implement such restoration woulddepend on the management objectives. Forexample, because this station supported extremelyhigh abundances of prickly sculpins and norainbow trout, it would meet an objective ofmanaging for native warmwater but not coldwaterfishes.

As another example, high flow volume belowAnderson Dam correlated with evidence ofscoured substrate, indicating habitat degradationat the R-S station. Data collected for thetemperature indicator, however, indicated thatmanaged flows released from the Dam maintainedcool water and enhanced habitat for cold waterfisheries, yet no rainbow trout were found at thissite. Thus, the combination of these dataindicated how urban factors influence habitat andbiological assemblages and identified the potentialfor restoring coldwater fisheries habitat within thecontext of managed hydrology.

In a smaller, less complex watershed, wherestormwater (as opposed to hardscaping,dams/diversions, etc) is more clearly the main

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human influence to a stream, changes in studydesign to compare habitat condition above andbelow outfalls, or catchments with and withoutBMP controls, might be one method to improvethe indicators usefulness for evaluating impacts(both positive and negative) associated withincreased urban runoff and effectiveness of astormwater program. However, the indicator maybest be used prospectively, in conjunction with asuite of indicators, in watersheds that arebeginning to urbanize, in order to developtemporal baseline data that is often lacking inhighly urbanized regions.

6.9 [Indicator Not Tested]

6.10 Increased Flood Frequency

We found this a difficult indicator toimplement in the Coyote Creek watershed largelydue to the highly modified nature of the creek'shydrology, the watershed's size, and the paucity ofaccurate historical flood data. Construction oflevees and Anderson Dam increased channelcapacity, reduced flows to the urbanized from thenonurbanized portion of Coyote Creek, anddecreased the incidence of flooding that hadotherwise previously occurred. This confoundedour ability to relate changes in land use and

,imperviousness to flood incidence.The effects of urbanization on floods of

different return periods have been summarized byHollis (1975) (Figure 8):

1. floods with a return period for 1 year or moreare not appreciably affected by a 5% pavingof their catchment area;

2. small floods may be increased by a factor of10 or more depending upon the degree ofurbanization;

3. floods with a return period of 100 years maybe doubled in size depending upon the degreeof urbanization of a catchment if thaturbanization results in at least 30% paving ofthe catchment;

4. the effect of urbanization declines in relativeterms as flood recurrence intervals increase.


eo

0 50llJ>«a.z 40iiic(III 30

~llJC) 20:!z~ 10

~

o~--------.....,...---------:"'.:----------:;::::--~0.' 1.0 10 100 200

FLOOD RECURRENCE INTERVAL (YEARS)

Figure: 6-1: Effect of Urbanization on Flood Peaks

Figure 6-1 illustrates that impacts ofurbanization on flooding are primarily caused bysmall floods. For small events in undevelopedwatersheds, a relatively large percentage of therainfall infiltrates into the soil and does notgenerate runoff. This explains the small runoffcoefficients usually assigned to undeveloped landuses. Rainfall on paved land uses produce largeamounts of runoff, thus large runoff coefficientsare usually assigned to impervious surfaces andhighly developed land uses. Large flood eventsgenerally occur when soils are highly saturated andhave correspondingly low infiltration rates.Therefore, even undeveloped areas have largerunoff coefficients when large flood events, suchas a 100-year flood, occur. Under thesecircumstances, the runoff coefficient onlyincreases by a small amount for paved areas.

About 15 - 22% of the watershed belowAnderson Reservoir is impervious. Under thiscondition, the 100 year flood would be increasedby about 10%, the 10-year by about 50% and the5-year by about 100%. Based on the analysispresented by Hollis, it is difficult to determine theimpacts of urbanization in the Coyote Creekwatershed on these large events since so few ofthem have occurred since urbanization, andbecause the expected increase in flow rate is small.

We had limited data with which to assessimpacts of urbanization on local flooding, but

found some support for this in a subwatershed(penitencia) of the Coyote Creek watershed. Hereincreased urbanization correlated with peak flowsand increased urbanization. Therefore, weconcluded that while this indicator is more usefulfor identifying relationships between urbanizationand small rather than large flood recurrenceintervals, it would be an even more usefulindicator if it was designed to track not onlyflooding incidents, but also to measure changes instream flow and identify instream infrastructurethat influences stream hydrology.

6.11 Stream Temperature Monitoring

Application of this indicator is dependentupon regional climatic conditions. This indicator,as implemented, may have more applicability inclimates where summer rainfall contributes tohigh flows, because impervious surfaceseffectively heat runoff, contributing to increasedstream temperatures, and this indicator may beused to assess this impact. In semi-arid climates,however, this effect is not present since themajority of the rainfall occurs during coo~ wintermonths (November - March). In such climatesurban runoff may contribute significandy tostream flows during summer months when streamflows are low (e.g., it can provide the majority ofstream flows). Thus, this indicator may be used toidentify where temperature changes along creeks.

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The ability to link temperature effects to astonnwater program, however, depends upon themonitoring design and a watershed's complexity.

In streams· such as Coyote Creek, dams,diversions, gravel pits, and distribution of ripariancanopy influence stream temperature, in addition

. to urban runoff. Detecting the relative influenceof each of these factors requires an intensive longterm monitoring effort, which may be impracticalfor stormwater programs. We placed five probesalong Coyote Creek. While that was not asufficient number to distinguish among all factorsinfluencing stream temperature, it provided usefulinformation for interpreting some of the data wecollected for the biological indicators, anddistinguishing between water quality and habitatinfluences on biological assemblages (as discussedabove).

An alternative to instream motUtoring is toplace temperature probes within stormdrains.This would indicate .whether stormwatertemperature is' significantly different thanreceiving water. To be useful, this would requirecomparison with a historic record.(or reasonableestimate) of in-stream temperature prior todevelopment. As with Toxicity Testing (Indicator#2), this approach assumes a verifiable nexusbetween storm drain discharges and potential instream effects. Stormwater may be found to havesignificantly different temperature differencesthan what would be expected in receiving water,however, determining the magnitude of anynegative effects on in-stream conditions, especiallyin a complex watershed like Coyote, would stillrequire an intensive monitoring effort to clarify.

To a limited extent, our temperature datacould be used, as suggested by Claytor andBrown, to indicate reaches that could benefit fromriparian restoration and/or riparian bufferenhancement. However, analysis of aerial photos,where available, may also accomplish thisobjective and require many fewer resources.Particularly in headwater streams wheredevelopment has not, or is just beginning tooccur, and few if any other non-natural factors areinfluencing a stream, monitoring streamtemperature may, as suggested by Claytor andBrown, be an effective means of protecting coolwater streams by demonstrating warming effectscaused by development. In streams where

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development is beginning, temperature probesmay be placed above and below developed areas.In pristine areas, data may be collected andcompared to other data collected in st.reams withdifferent levels of development.

6.12 Fish Assemblage

We found several types of comparisons ofCUttent and historical fisheries data very useful forcharacterizing the overall condition of the CoyoteCreek watershed. Statistical comparisons offisheries metrics between station groupings and topercent subwatershed imperviousness indicatedthat the extension of urbanization upstream didcorrelate with observed shifts in fisheriesassemblages. However, its utility for evaluatingstormwater program effectiveness was limited,because distinguishing the relative contributionmade by stormwater to changes in fisheriesassemblages is difficult in a complex watershedsuch as Coyote, where a multitude of factorsrelated to urbanization are at work.

Comparisons to historical fisheries andhydrology data helped identify the relativeinfluence of natural and anth.ropogenic factors onhydrologic fluctuations that influence fisheriesassemblages. Understanding observed changes inthe distribution and composition of fishassemblages requires identifying the relativecontributions of natural and human factors. Fishhabitat changes in response to hydrologicfluctuations, which as already discussed, can beconsiderable in western streams, caused by naturaland urban factors - those both related tostormwater programs (e.g., imperviousness), andthose beyond a program's purview (e.g., instreaminfrastructure related to water supply and floodcontrol, mining activities). Biotic interactions,particularly those caused by nonnativeintroductions also greatly influence fishassemblages.

One method to understand the relativeimportance of human impacts is to correlateobserved assemblage shifts with major changes inhuman activities as evident from landscapechanges (e.g., dam construction, channelmodification, etc.). Adopting this approachprovides an understanding of the trajectory ofchange that fish assemblages have experienced. It


may facilitate understanding how change wasoccurring in the absence of a stormwaterprogram, and what, if any change may beattributed to the establishment of a program.This approach also provides a method ofestimating reference conditions in regions wherethey have not yet been established (Buchan andLeidy in preparation).

It is important to emphasize that protocolsused for application of this indicator should beselected based on methods developed andrecommended for the particular region where it isto be implemented. In addition, use of methodssimilar to those used in historical studies willresult in more accurate temporal comparisons.Use of Rapid Bioassessment Protocols inCalifornia might be refined based on our results asfollows:

:=) Seasonal variations were not statisticallysignificant for any of the metrics. A singlesampling event per year could suffice andbased on the 3 events in 1999 (May, June &September) May is the best month forcapturing rainbow trout.

:=) A test could be done comparing 1 pass and 3pass electrofishing, but limited evidencesuggests that 1 pass may be sufficient andcost effective.

:=) Data on length of individual fish was notused. Eliminating length or recording classsize would reduce field time.

:=) DNA analysis on rainbow trout fin tissuescould determine what percent of the troutare wild.

:=) Station selection is critical: water depth,velocity, turbidity, and conductivity all canlimit electrofishing efficiency.

:=) Field duplicates are not cost effective. Dataanalysis showed significant differences in thefield duplicates, indicating high spatialvariability. The RBP methodology requiresfield duplicate to be immediately upstream ofa station. This does not take into accountpotential habitat similarities or differencescomparing the upstream and downstreamreaches.

:=) Metrics analyzing number of native species,number of native individuals and biomass ofnative fish were found to be the most useful.Other metrics need to be fine-tuned forCoyote Creek (and the Bay Area).

:=) Continued coordination with the CaliforniaDepartment of Fish and Game should beencouraged in order to address the regionalneed for unimpacted reference sitescomparable to the Bay Plain portions of SanFrancisco Bay area creeks.

:=) Monitoring of dissolved oxygenconcentration was useful for interpretationof fish sampling results.

6.13 Macro-Invertebrate Assemblages

Similar to the Fish Assemblage Indicator, wefound the indicator useful for' characterizingaquatic ecosystem health, and for predicting theexistence and severity of aquatic degradation increek reaches. The tolerance/intolerance metricwas particularly useful in this regard Thisindicator has potential to measure responses toshort term impacts; however, during our samplingperiod we did not detect any community shifts orchanges in water quality parameters that wouldindicate short-term impacts. Annual comparisonof metrics could indicate impacts to themacroinvertebrate community. However,understanding the cause(s) of such shifts requiresmonitoring and analyzing simultaneous changes inthe same factors mentioned above.

The metrics showed that the total number oftaxa and the total number of EPT taxa are greaterat the reference stations than at the rural andurban stations. However, it should be noted thatthere were some dissimilarities in gradient,substrate, and stream flow at the referencestations compared to those stations below thereservoirs, thus confounding interpretation of theresults.

The percent of individual EPT insects issimilar at the reference and rural stations, buthigher than at the urban stations. In other words,the total number of taxa relates information aboutdiversity, but not population composition. Thereference stations have the greatest number andpercent of low tolerance taxa, as well as percent of

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low tolerance individuals. The rural stations havemore low tolerance taxa and individuals than theurban stations. Evaluating low tolerance taxaappears to be a more discriminating measure thanhigh tolerance taxa. The high-tolerance taxaappear everywhere (albeit in varying numbers) but

. low-tolerance taxa only exist where the waterquality is suitable. Since the tolerance metric isclirecdy related to water quality it should beincluded in any future macroinvertebrateassemblage analysis.

The potential for this indicator to screenpossible sources and causes of degradation,evaluate performance of watershed restorationmeasures and performance of stormwater BMPs,while not demonstrated in this study, may befeasible if monitoring is appropriately designedwith respect to location and timing of influence ofcontributing factors. This might require that themonitoring program be prospective (i.e. put inplace prior to a significant change such as a streamrestoration effort or development of a tributaryarea) rather than retrospective. The three primaryvariables in the Coyote watershed, for example,are the regulation of stream flow, habitatlimitations, and water quality. There are· nodischarges of treated sanitary effluent into CoyoteCreek, thus the causes of degradation of waterquality are attributable to urba0i2ation in thebroadest sense, including impacts to habitat, andits concomitant non-point source flows. Thesefactors would need to be accounted for in asampling design to distinguish effects due tostormwater and program effectiveness.

The indicator will be most useful formeasuring stormwater program effectivenesswhen evaluated in combination with otherindicators and as a consistendy generated, longterm data set is developed for Coyote Creek.Also as the California Department of Fish andGame refines their Index of Biological Integrity(IB!) for first-to-third-order tributaries to theRussian River (Harrington et. al. 1999) the datafrom Coyote. Creek may be able to be evaluatedusing this mI.

All of the following refinements relate tosampling protocols:

=> Water depths and unconsolidated substrateslimit sampling effectiveness. The general

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direction of macroinvertebrate sampling inCalifornia is to sample only riffle habitats.The only inherent problem with this strategyis that if there are no riffle habitats in aparticular reach of a creek, then theopportunity to use macroinvertebrates as anindicator is lost. Perhaps in these cases, i.e.locations where the water velocity is low andthe substrate is predominandy silt and clay,the composition of the macroinvertebratecommunity is obviously limited andsampling is not required to confirm theobvious.

=> The number of organisms counted andidentified from a composite sample variesamong researchers. The SEIDP 1999 studyidentified 400 (± 20%) organisms from each

. sample. Harrington et.al. (1999) used 300organism samples and Carter and Fend(2000) used 500 organisms. The CaliforniaDepartment of Fish and Game is establishingprotocols, if 300 organisms is the sample sizeselected for analysis, it will decrease sortingtime and thus costs.

=> The number of kicks/jabs/rubs also variesamong studies. The SEIDP 1999 studyfollowed the RBP protocols and collected 20subsamples to create the composite. Carterand Fend collected five subsamples andHarrington et. al. collected three subsamples.Jerry Terhune (personal communication)collects ten subsamples. Clearly the numberof subsamples collected to represent amonitoring station can be reduced from 20.Local protocols, such as the CaliforniaDepartment of Fish and Game's threesubsamples, can be used to guide the numberof subsamples.

=> Two sampling protocol items that should beclarified are: how deep to sample in gravelsand how long to rub rocks..

=> In order to have comparable data sets it iscritical that the taxonomy be carried to asimilar level in subsequent studies. Also,regional consistency in sampling protocolswill improve the ability to compare data sets.





6.17 Public Attitude Surveys

Public attitude surveys are tools to gatherdata which may assist Programs to evaluate theireffectiveness. Measuring awareness and behaviorcan be useful when the results are used to focusfuture pollution prevention programs inproblematic areas and evaluate the effectivenessof specific outreach efforts. For example, theProgram's survey results indicated that gardeningand yard care continue to be the most commondo-it-yourself activities in Santa Clara County, yetmany are not aware of the harm that can becaused by leaves, grass clippings, or water used toirrigate lawns.

Broad telephone surveys, the methodemployed to evaluate this indicator, are helpful inthat they provide a statistically significantoverview of the attitudes and behaviors of all arearesidents in a program's jurisdiction. In addition,these surveys allow for meaningful comparisonsbetween a wide variety of demographic,geographic, and attitudinal subgroups. The factthat these surveys are conducted periodically everyfew years also makes it possible to measuremodest changes in attitudes and behaviors overtime.

However, the Program's survey findingssuggested the following limitations in measuringawareness alone to gauge program effectiveness(Fairbanks et al. 1999):

~ increased awareness is not enough tomotivate a behavior change

~ the results can not be directly correlated toimprovements in water quality;

~ .population subgroups, often the focus ofspecific outreach efforts, may not berepresented in sufficient numbers in an areawide survey to detect subtle changes in theiropinions or behaviors;

~ area-wide surveys conducted once every fewyears may not capture the results of

education efforts that have a limited durationand conclude prior to the time in which thesurvey is conducted;

~ area-wide telephone surveys gather primarilyself-reported data; respondents may indicatethat their awareness, perceptions or behaviorhave changed in certain ways, but there is noway that those claims can be independentlyverified;

~ broad scale surveys are expensive anddifficult to design.

Broad evaluations may best be used to trackthe trend of performance over time and toformulate programs and work plans. Specificevaluations on individual components ofstormwater programs should also be usedwhenever possible9• Ideally, program managersshould embed specific evaluation tools withinprograms to obtain continual feedback on aprogram's effectiveness.

6.18 Industrial/Commercial PollutionPrevention

The Program was able to use this indicator todetermine the information and outreach gapsbetween the Program and the industry.

We attributed our success in applying thisindicator to our site visits. The written surveysalone would not have been as effective indetermining the Program's impact and theindustries' understanding of the Program.However, the surveys followed by the site visitsprovided a general assessment of the attitudes andlevel of understanding of the individual sitemanagers with respect to the storm waterprogram. Site managers seemed to be morecomfortable with verbal communication ratherthan written, and more willing to ask questionsand provide answers in detail. Technicalinformation, e.g. regarding potential pollutants orBMPs, was better received and comprehended bysite managers when it was presented in this casualand direct manner.

In addition, visits to the site to discuss thesurvey were essential for visual and intuitive

9 Personal communication, Rufus Browning, PublicResearch Institute, January, 2000.

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feedback to Program and City staff. Through thesite visits, Program and City staff gainedfamiliarity with the facility operation and staff, andof the conditions under which potential pollutantsmay occur. With a more relevant context andperspective in mind, City and Program staff werebetter able to interpret the survey responses and

, their validity, evaluating the Program's deficienciesand strengths from the point of view of theindustry.

6.19 Public Involvement and Monitoring

Public involvement has the potential tomodify polluting behaviors and enhance pollution·prevention by increasing residents' appreciation oflocal watersheds and promote understanding and.support for local stormwater programs.Application of this indicator can help Programsidentify which types of activities would be themost successful in a given community and canhelp in planning outreach strategies in the future.However, conclusions regarding programeffectiveness need to be based on pre-defined,measurable program goals, and must be specifiedprior to evaluation. For example, increasedparticipation in annual events, as was documentedfor many SCVURPPP activities, is a goodindication that community involvement hasincreased awareness and concern over urbanrunoff pollution.

We found that the utility of the indicator waslimited due to insufficiency of documented resultsfrom public involvement and monitoringactivities. This limitation can be avoided ifstandard formats and requirements for follow-upevaluations of outreach programs are preestablished. For example, the Program couldrequire that annual reports include specificinformation on numbers of participants or resultsof evaluation surveys. .

However, the arbitrary use of quantitativemeasures (e.g. number of participants) does notnecessarily improve the indicator's usefulness.Information on event organization andaccomplishments, percent of communityparticipation, or follow-up evaluations of publicattitudes and perceptions would be a more usefulmeasure of effectiveness. Additionally, as withthe Public Attitude Surveys indicator, it is difficult

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to correlate public involvement and monitoringactivities with actual changes in pollutingbehaviors, especially since it may take several yearsfor behavioral changes to occur.

6.20 User Perception

This indicator was somewhat useful ingauging the value that the general public or atargeted group places on a water body or otherurban runoff pollution issue. Stormwaterprograms may most successfully apply userperception to justify resources and focus pollutionprevention efforts.

However, a principal disadvantage of thisindicator is that user perception can be heavilyinfluenced by external socio-economic variablessuch as education level of the populace, crimerate, economy, cost of living,. unemployment rate,and transportation issues, all of which can heavilyinfluence the perception and importance of waterpollution issues. The results of the Program'ssurveys reinforce this argument (see AppendixXX - indicator tech memo - for Program specificinformation).

The awareness, perceptions and ultimatevalue that a community places on urban runoffpollution prevention are also dependent on theurban environment (e.g., in a suburban areaplanned around open space, residents may place ahigher emphasis on creeks, rivers, and other waterbodies than in a highly urbanized area), as well asthe extent to which waterways are actuallypolluted. The perception of water pollution may·be higher in areas where water pollution isextremely acute and visible in contrast to low-levelchronic pollution problems.

However, perhaps the most importantlimitation of this indicator is that perceptions maynot be consistent with reality. User perceptionshould be applied to gauging the effectiveness ofstorm water programs cautiously. A populace mayperceive a water body to be polluted when it isactually in good health. Clearly, user perceptionshould not be used to measure the success ofwater quality efforts unaccompanied by otherindicators.


6.21 Number of Illicit ConnectionsIdentified/Corrected

The 70-fold decrease in reported illicitconnections reported 1993-1998 suggests thecities' surveys and monitoring of storm drains(particularly a 1993-1994 initial effort) effectivelyeliminated illicit connections to storm drains. The1993-1998 trend toward fewer illegal dumpingincidents (as reported for most categories)suggested that the Co-permittees' efforts wereeffective. These efforts included outreach(advertising, workshops, events, industrial/commercial inspections), inspections of industrialand commercial facilities, response to dumpingincidents, and enforcement.Most illegal dumping incidents are identified byinspectors responding to public reports, citydepartment referral, or through routineinspections of industrial/commercial orconstruction sites included on the State's NPDESpermit. Therefore, it is difficult to evaluate whatpercentage of these incidents that actually occurare subsequendy discovered, reported, anddocumented.

Using available data generated from Copermittee ICID programs enabled us to identifythe majority of pollutants associated with illegaldumping incidents and the minority of pollutantsassociated with illicit connections. However, wewere unable to determine the accuracy of ICIDreports (e.g. correct categorization of incident,description of pollutant). Some illegal dumpingreports noted the color of the liquid dumped, butthere was no additional information available todetermine pollutant type. Few ICID dischargesare tested in analytical laboratories to determineconstituent types or concentrations.

Despite difficulties in implementation, thisindicator was found to be good indicator ofProgram effectiveness, tying into both the CleanWater Act and one of the major foci ofstormwater programs. However, program effortsneed to be taken into account, because increasedoutreach and awareness may actually increase,-porting of illicit discharges/connections, thusconfounding the results.

The following information should beconsidered when applying the indicator:

~ Trends in population, transportation, anddevelopment growth.

~ Stormwater program self-evaluations.

~ Changes in stormwater programs, includingtargeted sectors, changes in staff and training,increased enforcement (e.g., approval to applyand/or increase citations and civil penalties toviolators), and the use of new technologysuch as cellular phones to improvecommunication and response time toincidents.

~ Increased outreach efforts.

~ Length of time program has existed.

6.22 Number ofBMPs installed, inspected,and maintained

The utility of this indicator was found to belimited as applied in the Walsh Avenue catchmentdue to lack of sufficient documentation. TheCity's NPS inspectors do not routinely documentBMPs being implemented at the site. Withoutthis documentation, . (and without muchdocumentation of previous industrial inspections),it was not possible to determine the number ofBMPs installed, inspected and maintained.

For many facilities in the Walsh Avenuecatchment, the appropriate BMPs are operationalBMPs (e.g., housekeeping, materials storage, andwaste disposal), and require no specific capitalequipment that would need to be installed ormaintained. Further, many of these operationalBMPs are implicidy required by otherenvironmental and health and safety regulatoryrequirements. The indicator's usefulness might belimited to businesses that are required to installspecific devices to prevent stormwater pollution.

6.23 Permitting and Compliance

As described in Claytor and Brown (1996),this indicator is specific to industrial andconstruction sites covered under NPDESrequirements. The usefulness of the indicator islimited by the quality and availability ofcompliance data.

Industrial facility non-filing is known to beprevalent and difficult to quantify (Duke and

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Bauersachs 1998). For example, Duke (1999)found that about 50% of heavy industries thatshould be filing under the Permit were not.Primary compliance is low because, in part, filingis voluntary and requires that facility operatorsinterpret whether their on-site industrial activities

, are "typical of" any of a list of SIC codes.As Duke (1999) noted "No U.S. agency

assigns businesses or facilities an SIC intended todescribe its activities, and in fact facilities mayreport under different SIC codes for differentpurposes such as corporate' revenue statistics,facility employment data, and wastewaterdischarge characteristics." Thus, differentdatabases are likely to associate different SICcodes with the same business, preventing the useof a single database to identify the population offacilities that should be filing under the Permit.Duke (1999) also concluded that facility operatorshave a disincentive to file under the Permitbecause of the cost of filing and because, undercurrent cirCumstances, they are unlikely to bediscovered by regulatory agencies. The RWQCB isdrafting a notice of violation to send to knownnon-filers infonning them of their regulatoryobligation and the civil penalties that can beenforced if they do not come into compliance(Barsamian 1999). The effectiveness of this effortto increase compliance will partly depend on howcomplete their list of non-filers is.

City of San Jose staff reported severalobstacles to enforcing compliance. Many SICcodes recorded in San Jose's business licensedatabase did not correspond to those included inthe SWRCB NOI database. In addition~ manyrecords contained only general SIC codes, whichdid not reflect activities that can contribute runoffpollutants. City staff addressed this problem bylooking up the SIC code most accuratelyassociated with the "primary activity" listed foreach business. City staff also reported that someinformation in the NOI database (includingaddresses and operating status of some facilitie~)

was not current. This impeded their ability toefficiendy schedule and conduct site inspections.

City of San Jose staff reported that storinginspecti,on data in a relational database (beginningin FY 95-96) improved their ability to summarizeand review inspection results and to prioritizefuture inspections. However, the State did not

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provide updated lists of the NOI filers in theirjurisdictions (City of San Jose 1995, 1996, 1997,1998). This problem was recendy remedied. Inearly 1999, the SWRCB began posting monthlyupdates to the NOI database on their website.However, the State has yet to create a mechanismfor Co-permittees to report non-filers or to trackstate actions to bring non-filers into compliance.

Within the Walsh Avenue Catchment,reporting compliance was poor, particularly formonitoring reporting. The RWQCB is developinga database to track submittal of annual reports andmonitoring reports submitted by NOI filers. TheSWRCB has not announced any plans to includeNOI filer report contents in a database.Therefore, it is difficult currendy to accessmonitoring data (except for the paper copies ofreports stored at RWQCB offices). If a databasetracking report submittal were operational, itwould be possible to calculate the percentage ofrequired .reports that are submitted by NOI filersfor any study area, and compare suchperformance regionally.

Both report submittal and monitoring datawould be required to evaluate accurately (e.g., toimprove upon our estimate of potential pollutantsand polluters based on SIC codes) two of thepotential benefits that the Center for WatershedProtection listed for this indicator: .

=> Identify potentially significant contributors ofpollutants.

=> Identify types of untreated pollutants enteringsurface waters via stonndrain system.

One of the two active industrial NOI siteswithin the Walsh Avenue Catchment had aSWPPP on-site, but it had not been updated sincei 996. The SWRCB has no program to insure thatindustrial SWPPPs are updated annually, nor toinsure that SWPPPs are implemented incompliance with the statewide permit. Inaccordance with SCVURPPP's ModelPerformance Standard for Industrial/CommercialDischarge Control, inspectors ask, during siteinspections, to see a copy of the facilities' NOIand SWPPP. However, Co-permittee inspectorsdo not, as a rule, review the SWPPPs to insurethey are current or complete.


In general, industries covered under thestatewide Permit did not document the numberand type of BMPs implemented in conjunctionwith the Permit.

As we found in assessing the Number ofBMPs Installed, Inspected and Maintained(Indicator #22), non-structural BMPs such ashousekeeping, preventative maintenance, spillresponse, and material handling and storage arenot typically documented by site operators, noreasy to quantify. Thus, assessing the "trackingBMPs" component of this indicator was difficultdue to both the nature of non-structural BMPsand the paucity of their documentation ininspection reports. Structural BMPs such asoverhead coverage, retention ponds, controldevices that re-route runoff, secondary catchmentstructures, and treatments (inlet controls,infiltration devices, detention ponds, vegetatedswales, etc.) are easier to identify and track, andinspection reports may better document thesetypes of BMPs.

We identified some pollutants that may bedischarged by industrial NOI facilities in theCoyote Creek watershed by listing pollutantstypically associated with the primary SIC codesand linking this information to the group offacilities identified from the SWRCB NOIdatabase. With the exception of sediment,pollutants discharged by construction sites areharder to characterize, being dependent on pastand present use of toxic materials on thedevelopment sites. By georeferencing both theNOI facilities and the stormdrain outfalls, wewere subsequendy able to identify which streamreaches within the Coyote Creek watershed couldbe affected by site discharges. Because we couldnot access monitoring report data for all sites inthe watershed, we did not identify which NOIfacilities might have the most significant impacton pollutant loads.

Even if the monitoring data had beenaccessible, the lack of quality assurance and qualitycontrol (QA/Qc) for the data would havepresented additional obstacles. SWRCBregulations require that NOI facilities monitortwice annually, but the SWRCB does not provideguidance to insure that samples are representativeof pollutant loads nor that sampling and analysesare subject to QA/QC.

Georeferencing locations of permitted sitesmade it possible to link potential runoff pollutantswith specific stream outfalls. However,inconsistent use of Standard IndustrialClassification (SIC) codes made it impossible touse other databases (e.g. business licenses) toestimate the number of businesses that operatedwithout required stormwater NPDES permits.The SWRCB does not track whether permittedsites file required annual reports or semiannualmonitoring data, and does not keep informationthat is submitted in an accessible format.Examination of two permitted sites at thecatchment scale showed relatively poorcompliance with SWRCB monitoring andreporting requirements.

Tracking administrative compliance withindustrial and construction stormwater NPDESpermit requirements could be a usefulprogrammatic indicator if the data were betterorganized, more complete and more readilyavailable, and if better estimates were available ofthe number and type of industrial businesses thathave failed to file (and are therefore not in thecompliance database). The other suggested usesof the Permitting and Compliance indicator aremore elusive, not only because of poor reportingand data compilation, but because of the inherentproblems in quantifying implementation ofoperational BMPs (Indicator #22) andcharacterizing the quantity, fate, and potentialeffects of runoff pollutants (Indicators #1, #3).

6.24 Growth and Development

Implementing the indicator as recommendedby Claytor and Brown (1996), we were able toestimate imperviousness at different spatial scalesand associate these estimates with selected aquaticmetrics. Such comparisons indicate cumulativeinfluences of urbanization, but could not be usedto direcdy evaluate stormwater programeffectiveness. We also demonstrated amethodology for estimating futureimperviousness for the purpose of guidingselection of monitoring sites and BMPimplementation.

In addition, we demonstrated a methodologyfor examining the relative contribution to overall

139


watershed imperviousness contributed bydifferent land uses. This methodology may beuseful in identifying aspects of urbanization thatmay be causing the greatest harm to aquaticecosystem functions, and could also informdecisions in planning, zoning, site design, andBMP sdection. The following section explores

'how the indicator might be implementeddifferendy to evaluate stormwater programeffectiveness.

Imperviousness is a general indicator ofurbanization. Urbanization - itsdf a complexphenomenon - is associated with a number ofcomplex, detrimental effects on aquaticecosystems. Because urbanization is associatedwith many confounding effects that influencewatershed hydrology and aquatic ecosystemfunctions, the effectiveness of stormwaterprograms may be most discernible when theindicator is applied to small watersheds orcatchments. In larger, more complex watersheds,the imperviousness can be used as, a generalindicator ofwatershed health.

Stormwater programs can work with landuse planners and developers to:

=> Reduce imperviousness.

=> Implement structural BMPs (e.g. detentionbasins) to maintain the hydrologicalcharacteristics and quality of runoff.

The creation of newly impervious area innew devdopments can be minimized through anintegrated strategy of minimizing paved area,directing runoff to landscaped, pervious areas,using permeable pavements, and incorporatingconcave vegetated surfaces into landscape design(BASMAA 1997).

As Schueler (1994) has pointed out, thetransportation component of imperviousnessoften exceeds the rooftop component, but zoningregulations more often regulate housing densitythan road density. Further, streets and highwaysare typically direcdy connected to drainagesystems. Narrower roadway widths, perviouspavements, and less clirecdy connected streetdrainage must be incorporated into such astrategy.

Structural BMPs may be implemented so thatincreased development does not concurrendy

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increase urban runoff impacts to streams. Forexample, detention swales and ponds can preventor decrease the rate at which increased runoffreaches storm drains and streams (Roberts 1999).

To fully demonstrate the effects of theseactions, it would be necessary to show:=> That specific program actions have led to

reductions in imperviousness or to anincrease in the number of BMPsimplemented

=> That these measures are protecting andenhancing watershed hydrology and aquaticecosystem function.

BMPs and Reductions in Imperviousness

To demonstrate the first point above, it maybe useful to implement the imperviousnessindicator in conjunction with programmaticindicators.

Tracking Structllral BMPs Implemented.Booth and Jackson (1997) concluded that

structural BMPs, such as storm detention ponds,frequendy fail to achieve standards for duration ofdetention and accommodation of peak flows.

,Moreover, in many communities, municipal planreview staff report difficulty in getting infiltrationand detention features incorporated into thedrainage design for newly developed sites.Typically cited concerns include geotechnicalstability, maintenance costs, and allocation ofspace on the site. Some municipal staff reportthat, given a choice, devdopers will chooseprefabricated BMPs (e.g., screening filters that fitinto storm drain inlets, precast buried baffleddetention tanks) instead of implementing anintegrated strategy of imperviousness reductioncombined with swales and detention ponds (EricAnderson, City of Mountain View, personalcommunication, 2000). Implementation of anappropriate integrated strategy can be hindered bycompeting priorities and lack of communicationamong the municipal staff responsible for publicworks, planning, environmental, and economicdevelopment.

Consideration ofImperviousness in ProjectApprovals.In addition to tracking whether BMPs are

being implemented in newly developed areas,stormwater programs might also track the extent


to which development project environmentalreviews have considered specific potential impactsof imperviousness on the functions of localaquatic ecosystems. A review of local land usepolicies within the flood plain and ripariancorridor may also reveal whether potential impactsof imperviousness are being addressed by localdecision-makers.

Imperviousness ofIndividiiaiSubdivisions.Another approach to implementing this

indicator might be to track the imperviousness ofnew subdivisions designed and built before andafter program implementation. The data might benormalized by comparing total impervious areaversus total housing units created. Transportationright-of-ways should be included in this analysis,since they typically comprise a large percentage ofwatershed imperviousness.

Effects on Hydrology and Ecosystems

The imperviousness indicator will be mostuseful in showing that Programs are successfullyprotecting and enhancing watershed hydrologyand ecosystem function, when the imperviousnessindicator is implemented in combination withother indicators to illustrate the links betweenland development, drainage, and ecologicalimpact.

Estimating Hydrologic Impact ofImperviousnw.To measure the effects of BMPs and other

measures to reduce the effects of imperviousness,it may be useful to apply this indicator inconjunction with measures of changes in flowfrequency (taking into account related urbanfactors such as dams, diversions, and channelmodifications). Homer et al. (1996) found thatincreased hydrologic fluctuations were an earlyindicator of impacts associated with even lowlevels of imperviousness by comparing percentwatershed imperviousness to the ratio of the 2-yrpeak flow to winter base flow. In the semi-aridwest, this method would need to account forextreme inter-annual variation.

Lnking Imperviousnw to Pollutant LJading.Many researchers have estimated pollutant

loads by using estimates of imperviousness fordifferent land uses in conjunction with modelspredicting urban runoff (Woodward Clyde

Consultants 1991, Homer et al. 1996, Shamsi andFletcher 1996). However, as discussed previously,variation in rainfall is such a strong determinant ofpollutant loadings that any change to loading dueto changes to imperviousness is likely to beundetectable.

Tracking Drainage System "Improvements'~

It may be useful to examine, quantify, andtrack the expansion of urban drainage facilities(e.g. number of stormdrain outfalls andproportion of "improved" stream channel) inconjunction with changes in impervious area. Indeveloping urban areas - particularly in theerodable landscapes of the West - increasedrunoff is generally accommodated by new pipes,culverts, and "improved" stream channels. Thesenew facilities may be constructed prior to orconcurrent with development, or may be builtpost-development, often in response to increasedflooding frequency or stream widening anddowncutting. (One of the benefits of reducedimperviousness is to reduce the need for thesefacilities, avoiding both their costs andenvironmental impacts.)

Target Moniton'ng ofStream CharacterirticsThe geographic distribution of

imperviousness, and projections of increasedimperviousness associated with land development,can be used to select locations for monitoring ofphysical and biological characteristics of streamsbefore and after development.

Summary of Suggested Refinements

Selecting a technique to estimate percentimperviousness depends on the precision requiredof the estimate, the available budget and datasources, and the watershed size. For the purposeof estimating impacts of imperviousness onaquatic health, we chose to augment a current(April, 1999) land use base map of fine spatialresolution with multiple data sources. Comparisonof subwatershed impervious estimates derivedfrom the compiled, current land use data set tothose derived using an older (1995), coarserresolution (1-hectare) land use data setdemonstrated that estimates using these lessaccurate data were 5- 39% higher in urbansubwatersheds.

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This difference demonstrates the need to'consider the accuracy of the estimate required toachieve study objectives, and also shows the valueof using alternative techniques to generateestimates. It also highlights the need toinvestigate methods used before making anycomparisons to estimates of imperviousness citedfor other studies or regions. Booth and Jackson(1997) concluded similarly.

Researchers frequendy report thresholdvalues for percent imperviousness at which shiftsin biological communities and/or habitats areobserved; yet the data and methods used to derivethese estimates of imperviousness are not welldocumented. If estimates of imperviousness arederived from data of different resolution andaccuracy, then comparisons across studies will beinvalid, and erroneous conclusions may be drawnabout the level of imperviousness that appears toimpact stream conditions. The same will holdtrue if imperviousness is calculated differendy, forexample, as cumulative percent subwatershed'imperviousness in one study but not in another.For this reason, we have not attempted tocompare numeric values derived from ourassociations between imperviousness and fisheriesmetrics with those from other studies.

Using parcel-based land use data to estimatepercent imperviousness was advantageous bothdue to its high spatial accuracy relative to otheravailable sources, and because its spatial resolution(ownership parcel) provided a mechanism forlinking results to zoning and master planning.Augmenting this basemap using additional datasources was valuable for improving its accuracy.Using data that provided a precise estimate of thetransportation right-of-way (mcluding roads andsidewalks) was especially useful sincetransportation is the primary land use that isentirely and direcdy connected to the stormdrainsystem, (typically comprising greater than 60% ofwatershed imperviousness in suburban areas (Cityof Olympia 1995)), and it clirecdy corresponds tomitigating design features such as narrower roads.

We were unable to estimate the percenteffective impervious area (%EIA) because welacked the necessary data on parking, driveway,and rooftop area. It is possible to estimate theexpected contribution of these surfaces byrandomly sampling within different land uses (City

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of Olympia 1995). The size of the Coyote Creekwatershed, however, precluded such effort in thisstudy. In smaller watersheds, it is more feasible todirecdy measure driveway, parking, and rooftopcontributions in similar~ developedneighborhoods and thereby derive estimates of%EIA that are specific to development patterns.In some cases, municipal public works orplanning departments may be able to provideinformation about patterns of rooftop drainageand driveways. Thus, the feasibility of thisapproach depends largely on watershed size,complexity, and data availability and format (e.g.,hardcopy, or stored electronically in a relationaldatabase).

We recommend consideration of thefollowing refinements to the Claytor and Brown(1996) methodology for applying this indicator:

Techniquesfor Estimating Imperoiousness

=> Consider alternative methods to estimateexisting and future percent imperviousness,including accuracy and availability of datasources and select methods appropriate tothe watershed size.

=> Include documentation of data sources andmethods used so that valid comparisons canbe made to other studies.

=> Consider how estimates of imperviousnesscan be used to estimate runoff coefficientsand pollutant loads.

=> Distinguish among components ofimperviousness attributable to major landuses. Consider how this information mightbe used in a strategy to reduce the effects ofimperviousness.

Applications ofIndicator

=> To more closely associate impacts associatedwith imperviousness with aquatic ecosystemfunctions, consider selecting a subwatershedor catchment scale for analysis.

=> Compare changes in percent imperviousnesswidl implementation of structural BMPs.

=> Track whether results from imperviousnessanalyses are influencing land-use planning.


=> Consider how increased imperviousness isaccommodated by drainage systemimprovements.

=> After estimating the rate and pattern of howimperviousness is changing, consider usingthe indicator to select monitoring locationsfor hydrological, physical, and biologicalindicators and to guide development andimplementation of watershed managementstrategies.


6.26 Industrial Site Compliance Monitoring

lbis indicator was useful in gauging theeffectiveness of the City's industrial stormwaterinspection efforts. Compliance monitoringidentified potential sources of stormwaterpollutants and ways to prevent those pollutantsfrom reaching storm drains. It was also aneffective way to begin outreach to site managers.

The results of this indicator reinforced ourconclusions from applying theIndustrial/Commercial Pollution Preventionindicator: businesses are implementing BMPs forreason of general hygiene and efficientorganization, and to comply with safetyregulations and other non-stormwaterenvironmental regulations. The compliancemonitoring conducted by Santa Clara inconnection with this study revealed the need foradditional outreach, in connection with siteinspections, to insure that site managersimplement and maintain stormwater BMPs.

Application of the indicator also revealed theneed for follow-up inspections. Successiveinspections showed that several of the businessesremained in non-compliance while others wereinconsistently in compliance. Most violations werein repeated problem areas. Better documentationof inspections would make it possible forstormwater programs to use the indicator to targetoutreach and document progress.

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7. Usefulness Of The Stormwater Indicator Methodology

Claytor and Brown (1996) propose a 2-phasemethodology (Figure 7-1) for using environmentalindicators to assess stormwater pollutionprevention programs. The Level I phase calls for areview of management responsibilities, historicaldata, potential receiving water impacts, andavailable resources before selecting a group ofindicators to assess baseline conditions. The LevelII phase calls for a statement of program goals,review of ongoing efforts, and implementation ofa management program before applying theindicators to assess the results of programimplementation.

The results from any measurement, whethernarrowly programmatic or broadly environmental,are useful to the extent that they can be placed incontext and used as a guide to action. Stormwaterenvironmental indicators will be useful only to theextent that stormwater programs, regulatoryagencies, and the public have a consensus view ofthe program's role, responsibilities, and ability toachieve specific aims.

Analysis of the results of applying theindicators revealed the advantages of beginning anindicator-based monitoring project with:

=> A consensus understanding of the generalrelationships between urbanization,functioning of stream ecosystems, andbeneficial uses.

=> A consensus understanding of Programgoals, objectives, practices, and procedures.

=> A consensus definition of Program"effectiveness."

We found that our ability to use indicatorresults as a guide to future action depended, inlarge part, on these conditions. At the same time,some of the indicators proved useful in achievingthese conditions - i.e., in providing input to theprocess of building a consensus understanding ofthe watershed and the Program's role inprotecting and enhancing the watershed.

In other words, some indicators were mostuseful in assessing general conditions andidentifying problems in the urban watershed(corresponding to Level I of Claytor and Brown's

methodology) and other indicators were useful inassessing and continuously improving themanagement program (corresponding to Level II).

With this in mind, for the purposes ofevaluating the stormwater indicator methodology,we found the indicators grouped into twocategories:

=> Watershed-related indicators.

=> Program-related indicators.

This categorization relates to Claytor andBrown's categorization as follows:

Watershed-related indicators include Claytorand Brown's water quality indicators, physical andhydrological indicators, and biological indicators(Indicators 1 - 16).

As discussed in Chapter 6, we found Growthand Development (Imperviousness, Indicator#24) to be useful in characterizing watershedconditions, rather than in evaluating programactivities.

Program-related indicators include three ofClaytor and Brown's programmatic indicators (#21, Number of Illicit ConnectionsIdentified/Corrected; #22, Number of BMPsInstalled, Inspected and Maintained, and #23,Permitting and Compliance) and the one siteindicator we tested (#26, Industrial SiteCompliance Monitoring). Two of Claytor andBrown's social indicators, (#18,Industrial/Commercial Pollution Prevention, and#19, Public Involvement and Monitoring) werealso useful for directly evaluating the pollutionprevention activities.

The other two social indicators (#17, PublicAttitude Surveys and #20, User Perception) werenot very useful as measures of programeffectiveness, but did provide information thatcan be used to target future outreach efforts.

7.1 Watershed-related indicators

The watershed-related indicators includedphysical, water-quality, and biological indicatorsthat are usually applied at a watershed scale toassess the health and level of function of aquaticecosystems, to understand the natural and

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anthropogenic factors influencing those functions,and to identify potential problems and problemareas. For this study, these indicators were usefulwhen applied to the Level I phase of the Claytorand Brown methodology. However, they are oflimited use in the Level II phase.

7.1.1 Application to uvel1.The results of these indicators, by

themselves, are not sufficient to help stakeholdersachieve a consensus understanding of therelationships between urbanization, functioning ofstream ecosystems, and beneficial uses. Theseindicators require a framework for understandingthe relationships among and between naturalfactors (e.g., flow regime, hydrogeomorphology,habitat, temperature, water quality, sedimentsupply) and urbanization (e.g., site design, streetdesign, imperviousness, alterations "to drainagedensity and stream channels, flooding, mooffpollutants). In the absence of such a framework,stormwater programs and watershed stakeholderswill find it difficult to select indicators or toevaluate the results of applying them.

A regulatory-based framework such as thosetypically adopted by water-quality programs mightexpand consideration of pollutant effects toexamine other "stressors" such as changes to flowregime and habitat change. Within such aframework, biological indicators are regarded asintegrative of the cumulative effects of humaninfluence. Comparison to relatively unimpactedreference sites, or a reference condition, is used tomeasure the degree of cumulative impact. Followup study may be required to allocate the degree ofimpainnent among various stressors.

The limitations of this approach stem fromreductionism; i.e., the approach is focused onmeasuring overall health or impairment ratherthan on gaining a general understanding of theinterrelationships among human activities andstream characteristics. The attempt to develop asingle group of indices that can be applied withinor across watersheds - i.e. that can sum up theentire natural and human history of a watershed,or even compare one watershed against another- may lead to use of abstract measurements thatreflect very generalized differences between"uninfluenced" and "influenced" biotic

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commumues. Some methodologies suggest thatthe index itself should be refined and calibrated byselecting metrics that best correlate with obviousevidence of "human influence" within awatershed This might ultimately result in an indexthat correlates well to perceived "humaninfluence" but is of little practical value inwatershed planning and urban design.

A framework focused on management ofresources - i.e. on opportunities to changestream conditions so as to maximize theirfunction as habitat, or to avoid potential threats tostream functions - provides better opportunitiesto use physical and biological indicators. Such anapproach typically begins with investigation andanalysis of changes to the way that streamstransport water and sediment and change theirshape in response to flood flows. Thesehydrogeomorphic functions are then related tohabitat functions, which, in turn, provide contextfor the examination of the abundance anddistribution of fish and other aquatic life. (SeeSection 6.7.)

To be useful in assessing the attainment ofaquatic life beneficial uses, biological, waterquality, and other watershed indicators should beapplied and interpreted in the context of thehydrogeomorphic and habitat functions ofspecific watersheds.

Ultimately, the purpose of such "anassessment is not to measure differences betweenwatersheds or stream reaches that are"influenced" or "less-influenced" by humanactivity. Rather, the purpose of investigating andmeasuring the physical, chemical, biological andecological characteristics of streams should beintegrally tied to the complex and collective workof preserving and enhancing beneficial uses.

To facilitate that collective work, theindicators must be applied within a resourcemanagement framework and must also convey amessage or story that impels stakeholders toaction.

Kimberly Walesh of CollaborativeEconomics has illustrated the role of indicators asshown in Figure 7-1 (Walesh 2000).

Each of these elements is necessary, because:

~ Without a compelling, well-communicatedstory, it is difficult to catalyze action.

CHAPTER 7 - USEFULNESS OF THE STORMWATER INDICATOR METHODOLOGY

Indicators

Data

---1~. Understanding --. Action

Figure 7-1. Relationship of Indicators to Actions

=:> Without an organizing framework to providea unifying theme, it is difficult to choose aconsistent set of indicators.

=:> Without indicators that organize the data, thedata are impenetrable.

=:> Without timely, relevant data, a story'ssignificance is dramatically reduced.

To inform and motivate watershedstakeholders, physical, hydrological, chemical andbiological indicators are best used to "tell thestory" of how a watershed has changed, and ischanging, in response to human influences. Acompelling, well-communicated story canstimulate actions to improve the watershed.

We developed useful techniques andinformation for understanding the relationshipsbetween urbanization and stream functionthrough implementation of the Growth andDevelopment Indicator (#24), the StreamWidening and Downcutting Indicator (#7), and toa lesser extent, the Increased Flooding FrequencyIndicator (#10). Additional valuable informationcame from the survey of stream conditions inconnection with the Physical Habitat Indicator(#8) and the Sediment Characteristics andContamination Indicator (#15). However, theseuseful results were achieved largely byreinterpreting the Claytor and Brown indicatorprofiles in the context of a general assessment ofthe relationship between hydrogeomorphicfunctions (i.e. the transport of water and

sediment, and the flood-induced construction andreconstruction of physical habitat within thecreek) and biological functions (as represented bypresence/absence and relative abundance of fishand macroinvertebrates).

The usefulness of the watershed-relatedindicators was facilitated by their application inthe context of the Program's ongoingparticipation in the SCBWMI. This participationestablished the expectation that assessment ofwatershed conditions would lead torecommendations of specific actions, within theCo-permittees' authority and capabilities, topreserve and enhance beneficial uses. Perhapsmost significantly, the SCBWMI established themechanism for the SCVURPPP and Copermittees to share the responsibility for thewatershed among many stakeholders and to sortout what specific actions it was most appropriatefor each stakeholder to implement. Outside thiscontext, the Co-permittees may have been lesslikely to acknowledge and accept the connectionbetween the results of the physical and biologicalindicators and the goals of the urban runoffpollution prevention program.

The benefits of the indicator project suggestthe potential for refining and perhaps streamliningsome of the indicators and incorporating theminto an general methodology that uses streamhydrogeomorphology, together with the mostrelevant and easily characterized measures ofurbanization (e.g. extent of outfalls and altered

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stream channel, extent of riparian area,concentration of pollutants in stream sediments)to illustrate key watershed relationships.

If the indicator methodology were simplifiedand streamlined, it would be easier and faster touse indicators to motivate watershed stakeholders

. to preserve and enhance riparian corridors,improve value and connectivity of habitat, managestreamflow, avoid further disturbance within thestream channel, avoid construction of additionalengineered chainage, and (most relevant to theSCVURPPP) avoid summer discharge of oxygendemanding substances to urban reaches.

7.1.2 Application 10 Lvel II.Application of watershed-related indicators

to evaluating the effectiveness of stormwaterprograms requires a detailed consensusunderstanding of stormwater program activitiesand the expected effects these may have onwatershed characteristics. This consensusunderstanding is best developed in the context ofa stakeholder-based watershed managementeffort.

Stormwater program participation inwatershed management can be measuredprogrammatically, e.g., by funds and staff effortcontributed and by review of the specific planningproducts produced. As a specific example,SCVURPPP and its Co-permittees contributednearly all the administrative and technical supportthat the SCBWMI has required in its watershedplanning efforts so far, and the results aredocument the first of three planned volumes ofthe Santa Clara Basin Watershed ManagementPlan.

Watershed-related indicators are best used tomeasure the effects of comprehensive efforts torestore stream functions, or to monitor expectedchanges due to urbanization. Attempts tocorrelate changes in watershed-related indicatorswith the implementation of BMPs and controlmeasures have nearly always yielded inconclusiveresults. Stormwater programs should considercarefully the selection of indicators and design ofstudies to examine these relationships.

Correlations can sometimes be shown wherepollutant sources are unusually rich and effects aremeasured immediately downstream.

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7.2 Program-related Indicators

Program-related indicators, targeted atmeasuring specific program activities, were usefulfor documenting and understanding pollutionprevention efforts and yielded insight into ways toimprove both the pollution-prevention measuresand the means of documenting them. For thisstudy, these indicators somewhat useful fordocumenting existing conditions (Level I of themethodology) and were more useful for evaluatingthe effectiveness of the Program (Level II).

7.2.1 Application to Lvel IConsideration of the basic stormwater

pollution-prevention mandates - to eliminatenon-stormwater discharges and to requireimplementation of BMPs - points to the need tocharacterize and quantify the relevant existingconditions: How common are illicit connectionsand illegal dumping incidents? What BMPs arebusinesses and homeowners alreadyimplementing?

We applied these questions to data collectedat the inception of the Program, including illicitdischarge data from the Coyote Creek watersheddating back to 1992, and data collected as part ofthe pilot industrial inspection program in theWalsh Avenue catchment around the same time.

This data provided a needed baseline fromwhich to gauge the success of Program efforts,and also provided some indication of the extentand potential impact of non-stormwaterdischarges.

7.2.2 Application to Level II. We found that the programmatic indicators,

as with the watershed indicators, were mosteffective when there was a consensus amongstormwater programs, regulatory agencies, and thepublic regarding the program's role,responsibilities, and ability to achieve specificaims.

In the case of the programmatic indicators,this meant a consensus understanding of Programgoals, objectives, practices, and procedures. Thisconsensus is codified in Performance Standardsthat apply to each Program element. The

CHAPTER 7 - USEFULNESS OF THE STORMWATER INDICATOR METHODOLOGY

Performance Standards have been drafted by the 'Program and Co-permittees, in cooperation withregulatory agency staff and interested parties, andapproved as an integral part of the Program'sNPDES permit.

Program elements are continuouslyimproved through the Program's process ofannual review (Figure 1- ).

The Program's Performance Standards andcontinuous improvement process facilitated theeffectiveness of the indicators because:

~ Under the Performance Standards, the Copermittees maintained systems to collect andreport data (e.g. illicit discharge incidents,industrial inspections) that were used toevaluate the indicator.

~ This data made it possible to review spatialand temporal trends in the parametersmeasured.

~ Trends in the data revealed opportunities tobetter target municipal staff efforts andimprove efficiency. We found this to be theprimary benefit from applying the indicators.

~ The continuous improvement processprovided a ready mechanism to implementour findings from implementing theprogrammatic indicators. These mechanismsincluded budgets required to investigatefeasibility, plan and design improvements,and a process to involve the municipal staff(e.g. industrial inspectors and streetmaintenance supervisors) responsible forimplementing the Performance Standards.

In describing the implementation andbenefits of the Number of Illicit ConnectionsIdentified/Corrected Indicator (#21) and theNumber of BMPs Installed, Inspected, andMaintained Indicator (#22) Claytor and Brown's(1996) indicator profile sheets conjoin the benefitsof objectivdy reviewing incident and inspectionrecords with the benefits of the inspectionsthemselves. For example, the description ofIndicator #21 begins: "This indicator involves theidentification and correction of illegal and/orimproper waste discharges into storm drainagesystems ..."

At first look., the practice of implementing ofan activity, while simultaneously applying anindicator meant to measure the results of thatactivity, could confound any objective assessmentof program effectiveness. However, ourexperience suggests the benefits of integratingimplementation and assessment far outweighpotential drawbacks. Involving line staff inmeasuring the results of their own work particularly when it is known that the results willbe reviewed and reported by management and byregulatory staff - tends to lead line staff to placegreater value on the completeness and accuracy ofrecords. Further, it generates enthusiasm andinterest in the task at hand.

The value of involving line staff in theassessment process was also apparent in ourimplementation of the Industrial/CommercialPollution Prevention Indicator (#18) and thePublic Involvement and Monitoring Indicator(#19). In this case of these two indicators, theprocess of one-on-one and small-group outreachand education is linked with gaining feedback onthe results of that outreach and education.

Implementation of these indicatorshighlighted the potential benefits of improvingdata-gathering and record-keeping associated withindustrial inspections, cleanup of illegal dumpingincidents, and outreach to facility managers andthe public. Some of the program-rdated indicatorsconfirmed anecdotal evidence, already beingdiscussed by the Co-permittees, of the need forimprovements in the way pollution-preventionmeasures were being implemented anddocumented. Some of these neededimprovements had already been established as"continuous improvement items" and projectshad already begun to be scoped for the followingbudget year. Outside the context of an establishedcommitment to continuous improvement, the Copermittees would have been less likely to committo specific actions in response to the indicatorresults.

These planned improvements in recordkeeping should help the Co-permitteesdemonstrate that they are implementing theseProgram elements to the "maximum extentpracticable" and facilitate continuousimprovements to their methods. The data may

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also be useful in measuring and improvingproductivity of the municipal line departments.

The usefulness of this portion of themethodology - i.e. the application of programrelated indicators to assess and re-evaluatemanagement programs - depends on:

. => Integration of data-gathering and reportingrequirements into Program PerformanceStandards.

=> Involvement of municipal line staff indesigning the record-keeping process so thatit is integrated into inspection, clean-up andenforcement activities.

=> An established commitment to, and processfor, continuous improvement.

7.2.3 Toolsfor Targeting OutreachTwo of the social indicators we tested

#17, Public Attitude Surveys, and #20, UserPerception - are designed as broad gauges ofpublic awareness and sentiment. For reasonsdiscussed in Sections 6.17 and 6.20, we did notfind these indicators particularly useful inevaluating program effectiveness. However, theywere useful in assisting SCVURPPP to targetfuture public education resources.

By implementing Indicator #17, the Programwas able to establish, or reaffirm in someinstances, the Program's primary target audiences,key messages, and most effective modes ofcommunication to reach target audiences. Theindicator also provided the Program with profilesof residents' awareness and behaviors thatcontribute to urban runoff pollution. Theindicator also reaffirmed Santa Clara Valleyresidents' support for expending public funds toeducate people and businesses about urban runoffpollution. This information is useful in helping theProgram to formulate work plans and budgetswith more certainty that the public will value andsupport these efforts. Finally, the indicatorprovided the Program with critical information onthe public's levels of awareness of watershedissues and allowed the Program to gauge how wella future campaign will be received.

Indicator #20 particularly useful inidentifying the public's misconceptions, which intum, will aid in targeting future education and

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outreach programs. For example, area residentscontinue to perceive large industrial ormanufacturing companies .as the majorcontributors to water pollution, and attribute farless responsibility to private residents. Futureefforts could be improved by using thisinformation as a platform to educate the publicabout the sources of urban runoff pollution andthe collective impact the general public possessesfor stormwater pollution.

#20 particularly useful in identifying thepublic's misconceptions, which in tum, will aid intargeting future education and outreach programs.The results of this indicator also reinforced theProgram's 1996 conclusions regarding the naturalfeatures that are perceived to make the mostimportant contributions to the Valley's quality oflife.

8. Conclusion

An evaluation of the effectiveness of astonnwater program must account for:

::::) The specific requirements applied to theprograms as described in Section 402(P) ofthe Clean Water Act, USEPA's stormwaterregulations, and the requirements of thestates.

::::) The complex nature ofwatersheds and theresponse of streams and other water bodiesto land use within their watershed.

::::) The natural and human history ofwatersheds, including the legacy of logging,mining, agriculture, ranching, and other nonurban impacts.

::::) The multifaceted effects of urbanization,including the changes to hydrology, flooding,drainageways, and water quality, as well asthe damming and diversion of stream flow,that typically accompany agricultural andurban development,.

::::) An understanding of sources, fate, transport,and effects of pollutants throughout thewatershed.

::::) The relationship between BMPimplementation and watershed effects,including reductions in pollutant loads.

::::) The problems of natural and randomvariability, as well as uncertainty inmeasurement, associated with environmentalsampling.

::::) Variability in the needs, characteristics, andcapabilities of local government, includingthe local social and political, as well asenvironmental, milieu.

::::) The specific goals of the local stormwaterprogram.

No single indicator, or combination ofindicators, is likely to meet the needs of everystormwater program. Stormwater programs willneed to choose indicators, and an approach toimplementing them, that best suits their needs.

Most stormwater programs seem toemphasize two goals: meeting the requirements oftheir NPDES permit, and determining the statusof beneficial uses in local waters. Many also seekto educate the public about stormwater pollutionprevention.

The results of this study suggest twocategories of indicators: watershed-relatedindicators, which are best applied in the context ofstakeholder-based watershed managementplanning, and program-related indicators, whichprovide feedback that can be used to focus andimprove municipal stonnwater pollutionprevention programs.

Table 8-1 summarizes our perspectiveregarding the usefulness of the indicators and theframework in which they should be applied.. .

8.1 Watershed-related Indicators

The usefulness of these indicators dependson their integration into a narrative that educatesand motivates watershed stakeholders to protectand enhance stream beneficial uses. To developsuch a narrative, the indicators must first beorganized around a framework that provides aunifying theme. The development of that theme- and the process of focusing a watershedmanagement plan around it is bestaccomplished by watershed stakeholders workingtogether. Our experience in the Coyote Creekwatershed suggested that the most usefulframework incorporates an understanding of howstream hydrogeomorphology - the flow of waterand sediment, and the continual re-creation of instream structures by natural and human influences-relates to stream biological functions and theassociated aquatic life beneficial uses.1O

We were able to use the watershed-relatedindicators to characterize some of these watershedrelationships in Coyote Creek. The indicator

10 In particular, we would recommend the approachesdescribed in Brinson, et. al (1985), Riley (1998), andFederal Interagency Stream Restoration Workgroup(1999). For specific application of a functionalapproach to Coyote Creek, see Buchan and Leidy(1999).

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Table 8-1. Summary of Indicator Usefulness.

Indicator Usefulness Key conditions and Usefulness for Level Key conditions Additional or Alternativefor Level 1- requirements for n - Assessment of and requirements IndicatorsProblem enhancing Usefulness Management for enhancing .Identification in Level I Assessments Program usefulness inand Baseline LevelnConditions Evalutions

#24 - Growth and Development Useful Use indicators within May be possible to use Requires long-term Dam. releases and flowC; (Imperviousness)'" framework linkingurban physical condition of data sets and diversions, amount oru'bh #7 - Stream Widenine: and Downeuttine: Limited dninage patterns to streams and extent of COIlSlStel1t proportion ofaltered vs. natural..9

0 #8 - Ph",,;,..,,! H,hitat Monitorinlt Useful impacts on stream dninage modifications protocols. Most channel, inventory of storm43 #10 -Increased Floodinlt Frequency T;...;.......I hydrogeomorphic as an indicator of effective when used drain outfalls and design flows,~ #11- Stream Temperature Monitoring Limited functions and habitat success in Watershed to measure specific extent of floodplain, extent of~ functions. Management. temporal effects of riparian area."5 land use change or." watershed

." '>"... A management0 p..,...13 aaions.

:.a #1 Water vualltY Monitorinlt T ;...;.......1 May be useful to May be applied at site Continuous monitoring ofoS

appears tot- 1/2 - ToxicitY Testine: Limited illustrate relative degreee or catchment scale to be a more robust dissolved oxygen during

." 1ILl 1/3 - Non-point Source T.1: Not Useful of influence of runoffon supplement indicator thanstorm summer months. Consider other...d

."

Ct 1/4 - Exceedance Frequencies of Water Quality Not useIi.if different stream reaches, programmatic measures flows. Best used to indicators ofurban influence on...ILl ...... but not necessarily ofBMP monitor response to stream sediments, e.g. visual<II ILl Standards~

...<II

#5 - Sediment Characteristics and Contamination Somewhat linked to beneficial uses. implementation clean up of specific observations or oillgrease.~

Useful sites or catchments.

#12 Fish Assemblage Very Useful Use Use to correlate and Long-term Fish and macroinvenebrates

"5#13 - Macroinvenebrate Assemblage VeryUsefUf for data colleaion and confirm effects of consistent seem to be the best biological

.... analysis; refine selection physical and monitoring at indices because methods arebO of indices. hydrological changes selected sites. Select established and links to stream0-0 and changes in water indices based on function and beneficial uses are....

I:Q quality. stakeholder goals widely understood.and practicability.

# 18- Commercial Pollution Very UsefUf Conduct on-site Can testerr-ectiveness Use to measure Similar approach could bePrevention interviews in context of of specific outreach success of specific applied to other groups, e.g.

site inspections. messages. outreach campaigns mobile cleaners, landscapers,restaurant manalters.

#17 - Public Attitude Useful Use to gauge general Measure behaviors." awareness of watershed instead of attitude.......0

~pollution-prevention Focus on everyday<IIu:.a ISSUes. activities that can

oS affect water quality.3 #20 User Perception Limited Importance tou

0 pollutionpreventionandenwatershed issues isaffected by social andeconomic conditions.

Table 8-1: 1 of 2

Table 8-1. Summary of Indicator Usefulness.

Indicator Usefulness Key conditions and Usefulness for Level Key conditions Additional or Alternativefor Level 1- requirements for II - Assessment of and requirements IndicatorsProblem enhancing Usefulness Management for enhancingIdentification in Level I Assessments Program usefulness inand Baseline Level IIConditions Evalutions

'" # 21 - No. of lllicit Connections Very Useful Establish Consider appropriateb Identified!Corrected programmatic programmatic indicators for00 # 22 - No. of BMPs Installed, Inspected, and Somewhat Useful indicators to public agency activities, new:.a Maintained complement development, other program~u # 23 - Permitting and Compliance Useful Performance elements,

';:l

I # 26 - Industrial Site Compliance Monitoring Useful Standards and use

# 19 - Public Involvement and Monitoring'" Limited as part of Consider programmatic.. continuous indicators for participation inbl)0 improvement watershed management process...

p... process.

Table 8-1: 2 of 2


results will be applied, and expanded upon, asSCVURPPP participates in a watershedmanagement process to plan management of flow,in-stream activities, and preservation andenhancement of riparian areas. To be successful,this process must also integrate flood

.management planning and a review of how landdevelopment policies contribute to these goals.

As this watershed management processproceeds, it should be possible to plan the use ofindicators to measure the success of elements in awatershed management plan, e.g., to measureconditions before-and-after changes in damreleases, in-stream restoration actions, or urbandevelopment along a specific reach.

8.2 Program-related indicators

Stormwater pollution prevention programsshould adapt the program-related indicators tocorrespond to the their specific permit conditionsand approved work plans. The indicators shouldcombine reporting the programs' level of effort(e.g. number of businesses inspected, numberattending outreach events) with measurement ofvariables direcdy related to that effort (e.g.improvement in pollution-prevention compliance,knowledge of pollution-prevention techniques).

The use of program-related indicators will bemost effective when program managers andregulators have agreed on a policy of continuousimprovement. This requires acknowledgementthat "maximum extent practicable" is different foreach locality and changes over time.

As part of the continuous improvementprocess, the indicator results should be openlydiscussed in meetings that include municipal staff,regulatory agency staff, and interested parties.

8.3 Linking Watershed Management andStormwater Pollution Prevention

Development of stormwater environmentalindicators stemmed from the desire to redirectNPDES-permitted stormwater pollutionprevention programs to focus on receiving waterbody quality and the beneficial uses that thecommunity desires for that water body.

The results of this project confirm that themyriad effects of urbanization on water bodiescannot be adequately addressed within the limits

154

of the NPDES-permitted programs. Municipalstormwater programs cannot, by themselves,develop and implement plans to preserve andenhance urban watersheds. These tasks require awatershed management approach.

Municipal stormwater pollution preventionprograms, like other municipal programs, aremost effective when they are guided by a specificplan, with objectives, tasks, timelines, and budgetsfor each plan element, and a process for periodicevaluation and adjustments. The programs willcontribute most effectively to watershedmanagement when they combine ongoingparticipation in the stakeholder process withdevelopment of specific work plans - i.e., whenthey define specific tasks within the generalstakeholder effort that the municipal program isbest qualified to implement.

During the planning stages, stormwaterprogram contributions to the stakeholder effortmay include conducting studies, managing data,acting as fiscal agents, managing joint projects,and facilitating stakeholder meetings. As thestakeholder process builds a commonunderstanding of how urbanization has affecteduses in the local water body - and buildsconsensus on objectives and actions to preserveand enhance those uses - changes to themunicipal program may include targeting specificsources, areas, or pollutants. .

In this way, stormwater pollution preventionprograms can develop, define, and measure theeffectiveness of their contributions to watershedmanagement.

This project has confirmed SCVURPPP'sworking definition of a successful stormwaterpollution prevention program as one that

=> meets the obligations stated in its NPDESpermit and management plan;

=> makes decisions openly and is responsive tocontributions and new ideas from regulatorsand the public;

=> is continuously improving;

=> is actively involved in broad, stakeholderbased efforts to control pollution and toprotect and enhance beneficial uses of localwaters.

9. References

Barsamian, L. 1999. Notice of Violation: discha'l,ingpollutants to waters of the United States without apermit. California Regional Water QualityControl Board, San Francisco Bay Region,Oakland, California.

California Regional Water Quality Control Boardfor the San Francisco Bay Region (1986). WaterQuali!y Control Plan jOr the San Francisco Bf!)' &gion.(Basin Plan).

CH2MHill and EOA, Inc. (1987). NonpointSource Evaluation Action Plan.

City of San Jose. 1995. Ci!y ofSan Jose Urban runoffManagement Plan Annual Report 1994 - 1995.Environmental Enforcement Division,Environmental Services Department, San Jose,California, 7Opp.

City of San Jose. 1996. Ci!y ofSan Jose Urban runoffManagement Plan Annual Report 1995 - 1996.Environmental Enforcement Division,Environmental Services Department, San Jose,California, 7Opp.

City of San Jose. 1997. Ci!Y ofSan Jose Urban runoffManagement Plan Annual Report 1996 - 1997.Environmental Enforcement Division,Environmental Services Department, San Jose,California, 7Opp.

City of San Jose. 1998. City ofSan Jose Urban runoffManagement Plan Annual Report 1997 - 1998.Environmental Enforcement Division,Environmental Services Department, San Jose,California, 7Opp.

Claytor, R.A. and Brown, W.E. (1996).Environmental Indicators to Assess Stormwater ControlPrograms and Practices, Final Report. Center forWatershed Protection, July 1996.

California Regional Water Quality Control BoardSan Francisco Bay Region (1990). Pennit No.CA 0029718, Order No. 90-094.

California Regional Water Quality Control BoardSan Francisco Bay Region (1995). NPDESPennit No. CAS029718, Order 95-180.

California Regional Water Quality Control BoardSan Francisco Bay Region. 1992. "Guidance forWho Needs an Industrial Storm Water Pennit."Memorandum to Interested Parties fromThomas Mumley.

California State Water Resources Control Board.1995. Annual Report for Storm WaterDischarges Associated with Industrial Activities,1994 - 1995. State Water Resources ControlBoard, Sacramento, California.

Duke, LD., and Bauersachs, L.A. 1998.Compliance with storm water pollution preventionregulations by metalplating industry fadlities. Joumalof the American Water Resources Association.34(1): 1-12.

Duke, L.D., KP. Coleman, and B. Masek. 1999.''Widespread failure to comply with U.S. stormwater regulations for industry, Part I: Publiclyavailable data to estimate number of potentiallyregulated facilities." Environmental EngineeringScience.

Duke, L.D., and KA. Shaver. 1999. Widespreadfailure to comply with U.S. storm waterregulations for industry, Part II: Facility-levelevaluations to estimate number of regulatedfacilities. Environmental Engineering Science.

Fairbanks, Maslin, and Maullin. 1999 Public OpinionSurvey, Final Summary Report. Prepared for theSanta Clara Valley Urban Runoff PollutionPrevention Program, September, 1999.

Habitat Restoration Group. 1995. SpreaderDams Fisheries Study 1994 Annual ReportSummary of Fidd Work, November 1993through October 1994 and Five-Year ProjectSummary 1989-1994. Prepared for the SantaClara Valley Water District, March 1995.

Harrington, J.M., Ode, P., Montalvo, A., Post, D.,Sheehy, C. and M. Dawson. 1999. Biologicaland Physical/Habitat Assessment of CaliforniaWater Bodies - Russian River Index ofBiological Integrity (RRIBI) for First to ThirdOrder Tributary Streams. California Departmentof Fish and Game, Office of Spill Prevention

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°

1

and Response, Water Pollution ControlLaboratory. May 1999.

H.T. Harvey and Associates and Coyote CreekRiparian Station. 1995. Coyote Creek Reach 1A1994-1995 Monitoring Report. Prepared for theSanta Clara Valley Water District, San Jose,Californ.ia. 17pp and appendices.

Kinnetics Laboratories, Inc. (KLI), 1999. QualityAssurance Project Plan for the StormwaterEnvironmental Indicators DemonstrationProject Part II: Coyote Creek. Santa ClaraValley Urban Runoff Pollution PreventionProgram, Sunnyvale, CA.

Leidy, R 1981 (Unpublished data). USEPAWetlands Division. Personal communication,September 1999.

Rensselaerville Institute, 1995. Report onStormwater Indicators Meetings ConductedApril-June 1995 by the Rensselaerville Instituteas part of a cooperative agreement with EPA. InAppenclix A to Claytor and Brown (1996). .

Santa Clara Valley Urban Runoff PollutionPrevention Program (1997). Urban RunoffManagement Plan. September 1, 1997.Sunnyvale, CA.

SCVWD. 1999 (Unpublished). AndersonReservoir temperature data (1996-99). DonDaves, personal communication.

State Water Resources Control Board 1997.Industrial Activities Storm Water General Permi/.State Water Resources Control Board,Sacramento, California.

StatSoft, Inc. (1999). Electronic StatisticsTextbook. Tulsa, OK: StatSoft WEB:http://www.statsoft.com/textbook/stathome.html

Sylvester, M.A. 1986. Water Quality and Flow ofStreams in Santa Clara Valley, California, 197981. USGS Water-Resources InvestigationsReport 84-4196. Prepared in cooperation withthe Santa Clara Valley Water District USGS,Sacramento, CA.

Woodward-Clyde Consultants (1990). LoadsAssessment Results and ImplementationProgram, (3 volumes).

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a.j. · larry a. roesner, ph.d, p.e., dee colorado state university geoffbrosseau bay area...

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