Multiple Pathways to Bacterial Load Reduction by Stormwater Best

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Multiple pathways to bacterial load reduction by stormwater best management practices: tradeoffs in performance, volume, and treated area Jordyn Wolfand, Colin D. Bell, Alexandria B Boehm, Terri Hogue, and Richard G Luthy Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b00408 • Publication Date (Web): 20 Apr 2018 Downloaded from http://pubs.acs.org on April 21, 2018

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Environmental Science & Technology

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Multiple pathways to bacterial load reduction by

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stormwater best management practices: tradeoffs in

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performance, volume, and treated area

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Jordyn M. Wolfand1,2, Colin D. Bell1,3, Alexandria B. Boehm1,2, Terri S. Hogue1,3, and *Richard

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G. Luthy1,2

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NSF Engineering Research Center for Re-inventing the Nation’s Urban Water Infrastructure

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(ReNUWIt), 2Department of Civil and Environmental Engineering, Stanford University,

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Stanford, California, 94305, United States, 3Department of Civil and Environmental

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Engineering, Colorado School of Mines, Golden, Colorado, 80401, United States

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KEYWORDS: stormwater control measures, BMPs, low impact development, green

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infrastructure, fecal indicator bacteria, Los Angeles

ACS Paragon Plus Environment

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TOC ART

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ABSTRACT

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Stormwater best management practices (BMPs) are implemented to reduce microbial pollution in

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runoff, but their removal efficiencies differ. Enhanced BMPs, such as those with media

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amendments, can increase removal of fecal indicator bacteria (FIB) in runoff from 0.25-log10 to

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above 3-log10; however, their implications for watershed-scale management are poorly

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understood. In this work, a computational model was developed to simulate watershed-scale

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bacteria loading and BMP performance, using the Ballona Creek Watershed (Los Angeles

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County, CA) as a case study. Over 1,400 scenarios with varying BMP performance, percent

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watershed area treated, BMP treatment volume, and infiltrative capabilities were simulated.

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Incremental improvement of BMP performance by 0.25-log10, while keeping other scenario

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variables constant, reduces annual bacterial load at the outlet by a range of 0 to 29%. In addition,

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various simulated scenarios provide the same FIB load reduction; for example, 75% load


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reduction is achieved by diverting runoff from either 95% of the watershed area to 25,000

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infiltrating BMPs with 0.5-log10 removal or 75% of the watershed area to 100,000 infiltrating

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BMPs with 3-log10 removal. Lastly, simulated infiltrating BMPs provide greater FIB reduction

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than non-infiltrating BMPs at the watershed scale. Results provide new insight on the tradeoffs

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between BMP treatment volume, performance, and distribution.

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INTRODUCTION

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Worldwide urban population growth increases the extent and density of urban areas while

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simultaneously stressing existing stormwater infrastructure.1 Urbanization results in more

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impervious surfaces, reducing natural infiltration of stormwater into soil and increasing runoff.2

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This increased runoff can carry pollutants including microbes, oils and grease, pesticides,

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nutrients, and metals.3 One particular concern is human pathogens such as bacteria (Salmonella,

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Campylobacter, and Escherichia coli), viruses (norovirus, adenovirus, and rotavirus), and

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protozoa (Cryptosporidium and Giardia), which are detected in stormwater and pose a risk to

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human health.4–6 Fecal indicator bacteria (FIB) such as E. coli, total and fecal coliform, and

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enterococci are routinely measured in surface waters to assess the potential for pathogens to be

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present.

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Elevated concentrations of fecal indicator bacteria are a primary cause of surface water

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impairment in the United States (US).7 Many cities have municipal separate storm sewer systems

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(MS4s) that convey urban runoff to local water bodies and often discharge runoff without

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treatment. Other urban areas may have combined sewer systems (CSS) in which urban runoff is

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combined with sanitary sewage for treatment when flows are within sewage treatment plant

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capacity. Water bodies that do not meet water quality standards are subject to federal regulations

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that mandate total maximum daily loads (TMDLs),8 which identify the maximum amount of a


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pollutant, including microbial pollutants, that a body of water can receive while still meeting

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water quality standards. Implementing TMDL programs costs the US billions of dollars

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annually,9 and in addition to money spent on compliance, microbial pollution results in

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significant loss of tourism revenue in coastal areas.10 Sources of FIB in urban areas include leaky

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sewer pipes, illicit sanitary connections to MS4s, overflows from CSS, failing septic systems,

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homeless encampments, domestic pets, urban wildlife, and livestock. FIB may also be present in

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non-fecal sources such as sand, sediment, and vegetation.11,12

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To manage FIB and comply with TMDLs, cities look to a variety of pollution removing

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techniques often called best management practices (BMPs). BMPs include both non-structural

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measures such as source control, street sweeping, and education, and structural measures that

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capture, treat, infiltrate and/or release stormwater. Examples of structural BMPs include

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bioretention systems (also known as biofilters or raingardens), grassed swales, permeable

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pavements, and green roofs. For the purposes of this study, we use the term BMP in reference to

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structural stormwater control measures. BMPs provide urban watershed benefits such as reduced

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peak flow and thus flood protection, increased water available for groundwater recharge and

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potential reuse, and ancillary aesthetic and economic benefits such as green spaces for cities and

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increased property values.13

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Despite the documented benefits of BMPs, when it comes to pollutant removal under true field

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conditions, their performance is variable and limited.14 Log10 removal values, regardless of

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BMP-type, range from -2 to 4, with a median of about 0.35, corresponding to about 55%

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bacterial removal (Figure 1). Negative log10 removal indicates net export of FIB. Situations

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where the BMP acts as a source rather than a sink for FIB may arise because FIB grow within

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the BMP media, there are sources within the BMP (mammals, invertebrates, or birds), or FIB


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previously sequestered within the BMP are released. Because of these low and uncertain removal

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rates, several researchers have investigated how BMP performance can be improved through

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creation of new BMP structures and enhancement of existing ones. Current strategies include

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optimizing treatment media, hydraulic behavior, vegetation, and redox conditions.15,16 Modifying

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sand bioretention units by adding geomedia such as granular activated carbon and zeolite with

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different surface modifications (such as copper and titanium dioxide) results in FIB log10

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removal rates from 0.2 (20% zeolite) to 3.44 (53% zeolite coated with copper).17 Another

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promising geomedia is biochar; Mohanty et al.18 report log10 removal for E. coli ranging from

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0.83 to 3.62, depending on the type of biochar and concentration of natural organic matter in

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stormwater. The addition of a submerged zone, or saturated organic-rich layer near the base of

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bioretention, can increase FIB log10 removal by up to 1.2.19 Further, the choice of bioretention

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plantings may increase reduction efficiency as vegetation type alters infiltration rate;

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Chandrasena et al.20 report over an order of magnitude (>1-log10) difference in E. coli removal

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depending on vegetation type, and that removal highly correlates with infiltration rate. The

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effects of other vegetation mechanisms are uncertain.16 Soil, vegetation, redox, and hydraulic

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modifications may not only benefit FIB removal but also promote removal of other pollutants

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including nutrients, metals, and trace organics.15,21–28

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It is not clear how widespread deployment of BMPs might reduce total pollutant loads in a

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watershed, especially in semi-arid and arid regions.29 Empirical and modeling studies have

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demonstrated water quality improvements are often a product of runoff reductions alone, despite

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the potential for even greater improvements.29 Further, mitigation of both water quantity and

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quality by BMPs may be undetectable if a sufficient amount of the impervious area is not

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treated.30,31


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Therefore, the goal of the present study is to investigate the performance of enhanced BMPs at

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the watershed scale by exploring the water quality tradeoffs between BMP performance, BMP

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type (infiltrating or non-infiltrating), watershed area treated, and number of BMPs installed. The

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US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) is

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coupled to both the EPA System for Urban Stormwater Treatment and Analysis IntegratioN

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(SUSTAIN) and a new stochastic BMP treatment model to determine bacterial load reductions

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and TMDL concentration exceedances in the case study of the Ballona Creek watershed, in Los

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Angeles County. This work fills existing gaps in the literature in that it examines the impacts of

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installing BMPs with enhanced removal efficiencies, investigates water quality on the watershed

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scale, and uses a semi-arid watershed as a case study. Results also inform the adoption of

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enhanced BMPs and provide insights for water quality compliance in a large urban watershed.

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Figure 1. A box and whisker plot showing observed bacterial removal efficiencies for BMPs implemented in the field.32 Positive values indicate a decrease in outflow concentrations relative to inflow, while negative values indicate the opposite. Midline of the box represents the median of observed values, the bottom (left) and top (right) of the box represent the 25th and 75th percentiles, respectively. The bottom (left) and top (right) whisker represent the 10th and 90th


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percentiles, respectively. The symbols represent data points below and above the 10th and 90th percentiles. The red line indicates no removal. The n value provides the number of observations available in the database. Definition of each BMP can be found in Table S3.

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METHODS

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Study Site

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The Ballona Creek Watershed, located in Los Angeles (LA) County, is roughly 330 km2 and

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highly developed, with about 64% of land use residential, 8% commercial, 4% industrial, and

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only 17% open space (Figure 2). Overall, the watershed is 52% impervious. The catchment starts

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north in the Santa Monica mountains and drains into Santa Monica Bay adjacent to Marina Del

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Ray. Ballona Creek is ~15.5 km long and divided into three sections: Ballona Creek Reach 1,

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Ballona Creek Reach 2, and Ballona Estuary.33 Major tributaries to Ballona Creek include

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Sepulveda Channel, Benedict Canyon Channel, and Centinela Creek. Ballona Creek itself is

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mostly concrete-lined (since 1939) and most of its tributaries are concrete channels or covered

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culverts (since the 1950s).34 Precipitation in LA is seasonal, with most rain falling between

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October and March. Dry-weather flows from irrigation and car washing have significant impact

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on the Ballona Creek Watershed hydrology and water quality.35

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Currently, the watershed has established TMDLs for metals (copper, lead, selenium, and zinc),

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FIB (E. coli, fecal coliform, enterococci, and total coliform), toxics (chlordane, DDT, and

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PCBs), and trash.36 The National Pollution Discharge Elimination System (NPDES) MS4 permit

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for Los Angeles (No. CAS004001) offers two ways to achieve compliance: (1) retain the

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standard runoff volume from the 85th percentile, 24-hour storm, or (2) achieve the necessary

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pollution reduction targets. Like most bacterial TMDLs, the FIB TMDL in Ballona Creek is

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concentration-based. The Ballona Creek bacteria TMDL was adopted in June 2006, revised in

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June 2012, and requires compliance by 2021.37 For FIB, concentration targets can be met


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through attainment of geometric mean or single sample exceedance limits. Exceedance days for

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single samples are split into two periods: dry-weather days and wet-weather days, with the latter

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defined as days with 0.1 inch (2.54 mm) of rain or greater as well as the three days following the

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rain event. If water quality sampling is conducted weekly, 1 dry weather and 2 wet weather

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exceedance days are allowed. If routine sampling is conducted daily, 5 dry weather and 15 wet

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weather exceedances are allowed.37 The geometric mean is calculated weekly for six-week

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periods, starting all calculation weeks on Sundays, and may not be exceeded at any time.

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For purposes of this modeling study, we examine the watershed above Ballona Creek bacteria

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station #5 (BCB-5), which has an area of 290 km2. The monitoring point is located within

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Ballona Creek Reach 2, just upstream of Ballona Estuary, and is designated for limited water

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contact recreation (LREC). Reach 2 has a single sample limit for E. coli of 576 MPN/100 mL

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and a geometric mean limit of 126 MPN/100 mL.37 The Los Angeles Region Water Quality

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Control Board has adopted an Amendment to the Los Angeles Region Basin Plan that

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established a High Flow Suspension of recreational beneficial uses in Ballona Creek Reaches 1

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and 2. This means that bacteria criteria are suspended on days with ≥0.5 inch (1.27 mm)

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precipitation and the following day thereafter. In this study, we take a conservative approach and

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do not consider the High Flow Suspension, instead opting to meet water quality criteria

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regardless of flow conditions.

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The watershed is an ideal case study for several reasons: it is (1) highly urbanized and not in

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compliance with its TMDLs; (2) located in a semi-arid climate susceptible to drought where

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stormwater capture is increasingly viewed as an alternative water supply; and (3) well-studied,

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with significant publically available data. Several peer-reviewed papers have examined pollutant


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loading in the watershed, specifically in regard to metals, bacteria, chlorinated pesticides,

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polychlorinated biphenyls, and pyrethroids.38–40

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Figure 2. Ballona Creek Watershed, located in Los Angeles County, California and monitoring locations for precipitation, flow, and water quality parameters. Runoff from the subcatchments draining to Ballona Creek bacteria station #5 (BCB-5, yellow dot labeled 5) was modeled in the present study. Map was created using ArcGIS® software by Esri.

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Modeling Overview

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Several existing computational models were linked with novel algorithms to predict FIB removal

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in BMPs at the watershed scale (Figure S1). Briefly, a stormwater runoff model was created with

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EPA SWMM41 to determine runoff at the watershed scale, and flow through bioretention units

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was simulated in EPA SUSTAIN.42 This was coupled with a novel stochastic bacteria loading

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and removal model, created in R, to simulate bacterial concentrations entering and exiting

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bioretention units (R code available at https://github.com/jwolfand/sw-log-removal-model).


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Stormwater Runoff Model

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A hydrologic model using EPA SWMM41 was created to predict hourly runoff in the Ballona

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Creek Watershed from water year (WY) 1997 to 2015. The watershed was broken into nine

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subcatchments, based on the natural topography in addition to the storm drain network, that have

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also been used in previous modeling studies.43 Inputs to the SWMM model include soil

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properties, slope, imperviousness, evapotranspiration, land use types, and precipitation. Soil

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property data was downloaded from the LA County GIS Data Portal. The Green-Ampt method

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of infiltration was used,44 and saturated hydraulic conductivity and suction head were matched to

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soil types based on soil texture classes.45 An area-weighted average was used to determine

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representative soil properties for each subcatchment. Slope was calculated based on elevation

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data, which was retrieved from the National Elevation Dataset.46 Imperviousness was calculated

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for each subcatchment from 1/3 arc-second (~10 m) resolution data from the National Land

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Cover Database.47

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Daily evapotranspiration data was retrieved from the California Irrigation Management

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Information System (CIMIS). Precipitation data was obtained for nine LA County Automatic

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Local Evaluation in Real Time (ALERT) and two National Climatic Data Center (NCDC) gages

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located within or adjacent to the Ballona Creek Watershed. The inverse distance square

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weighting method48 was used to approximate precipitation within each of the subcatchments

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(locations shown on Figure 2). The centroids of each subcatchment were determined in ArcGIS,

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and the inverse distance square for the closest five gages to each centroid was calculated,

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ignoring data gaps. A detailed list of modeling parameters is provided in Table S1.

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For calibration and validation of the model, flow data at Sawtelle gage (red star in Figure 2) was

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provided by the LA Department of Public Works. Baseflow was separated from surface runoff


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using the local-minimum method,49 and data gaps were replaced with the median record

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baseflow. Baseflow, which includes dry-weather flow, was included as a background time series

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in the model.

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Weekly FIB monitoring data, collected at the locations shown in Figure 2 (yellow circles), were

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obtained from the City of LA, Bureau of Sanitation, Watershed Protection Division. This data

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was collected from 2009–2016 to measure compliance with the Ballona Creek FIB TMDL. Each

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monitoring station was sampled for the FIB regulated within that reach; samples from Ballona

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Creek Reach 2 were sampled for E. coli. All samples were grab samples, taken in the morning

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(between 8–11 am) generally on Thursdays.

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Modeled discharge was manually calibrated to discharge from the Sawtelle gage at an hourly

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time step using WYs 1998 to 2006. Three SWMM subcatchment model parameters were varied

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(Manning’s roughness coefficients, depression storage, and catchment width)41 to minimize

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percent bias and maximize Nash-Sutcliffe Efficiency (NSE; where NSE = 1 is a perfect fit).

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Three other metrics were calculated to assess model performance: absolute error, root-mean-

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square-error (RMSE), and the coefficient of determination (R2) as described in equations S1–5.

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The hydrologic model was validated using discharge from WY 2007 to 2015 using the same

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performance metrics. Baseflow was subtracted from both observed and modeled flow for

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calibration and validation.

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BMP Flow Model

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The validated stormwater runoff model was used to predict flow at BCB-5 from WY 1998 to

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2015 under different BMP implementation scenarios. Because of the large scale of the

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watershed, an aggregate BMP approach was used to simulate flow through BMPs using EPA


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SUSTAIN.42 This approach allows assessment of the combined impact of multiple BMPs on

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watershed runoff, without explicit identification of their spatial distribution and routing

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characteristics. That is, instead of placing BMPs throughout the watershed, all runoff upstream of

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BCB-5 was divided equally to a collective quantity of individual BMPs, ranging from 0 to

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100,000, receiving runoff in parallel. An aggregate BMP approach is supported by EPA

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SUSTAIN and has been extensively used in the literature.50–55

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Flow through an aggregate BMP consisting of bioretention units was simulated. The footprint of

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each bioretention unit was based on a review of LA design documents.43 Soil and underdrain

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specifications within the bioretention units were based on those detailed in the Ballona Creek

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Enhanced Watershed Management Program report (Table S2).56 Briefly, each bioretention unit

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consists of a 0.610 m (2.0 ft) depth of geomedia over a 0.457 m (1.5 ft) deep subbase of gravel

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(for detailed model parameters see Table S2). Each bioretention unit has a static storage volume,

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including void space, referred to as treatment volume herein, of up to 43.4 m3 (1,534 ft3) of

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stormwater. Evapotranspiration, on a per-BMP basis, wasn’t calculated explicitly, but instead

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assumed to follow the daily estimates downloaded from CIMIS (previously described and part of

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the overall runoff model). During storm events, when water is captured in BMPs,

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evapotranspiration was assumed to be an insignificant part of the water budget given reduced

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radiation inputs.

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Both infiltrating and non-infiltrating bioretention units were simulated. For those with

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infiltration, flow enters via overland flow (runoff) and exits through either seepage via Green-

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Ampt infiltration, underdrain flow, or overflow. For those without infiltration, the unit is lined,

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and flow exits only via underdrain flow or overflow. Groundwater aquifers in the watershed

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were not explicitly modeled; Ballona Creek is mostly channelized and concrete-lined, so water


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infiltrated by BMPs generally does not return as direct baseflow to the channel. We hypothesize

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that infiltrated water migrates vertically and a portion contributes to aquifer recharge. Time

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series flow through bioretention units, including seepage and overflow was determined from the

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BMP flow model and used as input to the stochastic bacterial removal model.

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Stochastic Bacterial Removal Model

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A stochastic approach was used to determine hourly E. coli concentrations entering the aggregate

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BMP. Each hour, a concentration value was randomly sampled from log10-normal distributions

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for either dry- (µ= 2.71 and σ = 2.78 log10 MPN/100 mL) or wet- weather (µ= 3.21 and σ = 4.58

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log10 MPN/100 mL), depending on the flow condition. These distributions were based on

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observed E. coli monitoring data at BCB-5 (n = 370). The units of E. coli concentration before

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log10-transformation are MPN/100 mL.

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Wet and dry weather were distinguished using a technique modified from Hewlett & Hibbert

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(1967).30,57 A line with constant slope of 5.47 x 10-4 (m3/s)/km2/hr (0.05 cfs/mi2/hr) was used to

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separate baseflow from stormflow at BCB-5. The identified storm event was expanded to one

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hour before identification and two hours after to capture the rising and falling limbs. Individual

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storm events were defined as any event with a precipitation total of 0.127 mm (0.005 in) or

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greater, which corresponds to about the 5th percentile of events identified using the algorithm.

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During dry weather, the E. coli concentration entering the aggregate BMP varied hourly. For wet

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weather, a unique E. coli event mean concentration (EMC) was applied during the duration of

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each storm event. Compliance with the TMDL was determined by comparing the simulated daily

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10:00 h E. coli concentration with the single sample exceedance limit (576 MPN/100 mL).

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Figure 3 shows the distributions of observed E. coli concentrations at BCB-5 and the simulated

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concentrations sampled at the same time points as the observed data.


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An algorithm to simulate E. coli log10 removal was created in R58 and used to estimate

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concentration of E. coli exiting bioretention BMPs. Removal performance was measured as log10

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reduction, where 1-log10 removal results in 90% reduction, and 2-log10 removal results in 99%

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reduction, etc. (Eq. 1, adapted from Crittenden et al.59). 1 = 10

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Where:

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Cin = concentration of flow into the BMP

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Cout = concentration of flow out of the BMP

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logrem = log10 removal value

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It was assumed that bioretention cells are completely and instantaneously mixed. Only flow

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through the bioretention system exiting through the underdrain was considered treated; no

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removal is applied to the weir overflow, as well as any flow bypassing the BMPs. This

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assumption does not account for the physical reality that water passing through BMPs will have

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a range of residence times, but for this work is considered appropriate given the scale of the

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analysis and required computational time.

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Simulations were run for varied BMP performance (log10 removal of FIB), percent watershed

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area treated, number of bioretention units, and either infiltrating or non-infiltrating BMPs,

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totaling 1440 scenarios (Table 1). FIB log10 removal ranged from -0.25 to 3 to encompass

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removals expected for both existing and enhanced BMPs. Infiltration in the BMPs was restricted

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to test the hypothesis that BMPs treating and releasing lower concentration runoff to the surface

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drainage network (rather than returning it to the subsurface) will more effectively meet


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concentration-based TMDLs because the treated runoff would dilute untreated runoff with higher

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concentrations. The number of bioretention units was converted to equivalent static treatment

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volume (43 m3 or 1534 ft3 per unit) for reporting purposes. Annual average FIB load, TMDL wet

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weather exceedances, and TMDL dry weather exceedances were evaluated for each simulation

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scenario.

Cumulative Probability

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Figure 3. Observed vs. simulated E. coli concentration distributions in Ballona Creek. Observed data are matched with corresponding simulated data in this plotted distribution (n = 326). The reporting limit for observed data is 100 MPN/100 mL. The dotted vertical line is the single sample exceedance limit under the Ballona Creek bacteria TMDL (576 MPN/100 mL).

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Table 1. Simulation scenarios. All possible variable and value combinations were used, resulting in 1440 total scenarios. Variable Values -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 3 Log10 Removal 25, 50, 75, 90, 95, 100 Percent Watershed Area Treated Number of Bioretention Units 0, 100, 500, 1,000, 2,500, 5,000, 7,500, 10,000, 25,000, 50,000, 75,000, 100,000 Yes, No Infiltrating


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RESULTS AND DISCUSSION

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Hydrologic Model

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Calibration and validation of the stormwater runoff model shows good model prediction of storm

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flow, both for hourly and aggregated daily time steps (NSE = 0.688 and 0.946, respectively;

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Figure 4 and S2). The hydrologic model has improved NSE compared to previously reported

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runoff models for the watershed, and offers high resolution predictions (hourly) for a continuous

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and long term simulation record (18 years). For example, a daily storm flow model of the

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Ballona Creek Watershed created in HSPF by Ackerman et al. noted an NSE ranging from 0.60

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to 0.66.60 Additionally, Muleta et al. created a 15-minute runoff model in SWMM for the

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watershed and noted an NSE ranging from 0.88 to 0.94, but only for a 13-day simulation.61

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Figure 4. Observed vs. modeled daily storm flow in Ballona Creek. The calibration period was WY 1998 to 2006; the validation period was WY 2007 to 2015. Only storm flow was used to calculate performance statistics (base and dry weather flow was subtracted).

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Compliance with TMDLs

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Simulation scenarios show that the Ballona Creek bacteria TMDL can be met with conventional

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infiltration-based BMPs (with 0.25-log10 removal), for both dry and wet weather (Figure 5A and

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5E). However, runoff from 100% of the watershed area must be treated to comply with the wet-


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and dry- weather TMDLs, with a minimum of 7,500 units installed (with a footprint equivalent

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to 0.2% of the total watershed area) regardless of BMP performance. Neither the dry- nor the

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wet- weather TMDL can be met with conventional (0.25-log10 removal) non-infiltrating BMPs

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(Figure 5C and 5G). When non-infiltrating BMPs are enhanced to have 3-log10 bacterial

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removal, it is possible to meet the wet- but not the dry-weather TMDL if runoff from 100% of

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the watershed area is treated (Figure 5D and 5H). Compliance with the TMDL is not sensitive to

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BMP performance if infiltration-based BMPs are installed, as reduction in flow is the primary

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mechanism of FIB removal within the model (i.e., panels A/B and C/D in Figure 5 are nearly

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identical).

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There is also a tradeoff between percent watershed area treated by BMPs, the number of BMPs

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installed (treatment volume), and BMP performance. The most sensitive factor is the percent

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watershed area treated; in our simulations, runoff from 100% of watershed area must be treated

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in order to meet the Ballona Creek bacteria TMDL (Figure 5). Any flow untreated by BMPs may

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result in exceedances; for example, treating runoff from 100% of the watershed area with 25,000

328

(infiltrating) bioretention units results in about 5 annual wet weather exceedances but treating

329

runoff from only 95% of the watershed area results in about 22 annual wet weather exceedances.

330

This effect would be magnified if untreated areas drain hotspots of FIB concentration. For

331

maximum pollutant reduction, BMPs should be carefully sited to capture the majority of

332

pollutant load, either in downstream areas or areas with known high pollutant load. However,

333

placement opportunities due to availability of land or stakeholder buy-in may not align well with

334

maximum pollutant loading. Treating near 100% of dry weather flow may be possible if the

335

Ballona Creek Watershed Management Group’s (a coalition of stakeholders responsible for

336

management of the watershed) plan to install low flow treatment facilities to capture, disinfect,


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337

and return dry weather flow along Ballona Creek Reach 2 and Sepulveda Channel is

338

implemented.56

339

As previously mentioned, the LA County MS4 permit offers an additional mechanism to comply

340

with water quality regulations: by capturing and infiltrating or treating runoff from the 85th

341

percentile storm, which corresponds to about a 25.4 mm (1 in) storm depth.62 This equates to

342

about 10.7 mm (0.42 in) depth of runoff or 3.1·106 m3 (2,500 AF). Under simulation conditions,

343

and assuming conventional BMPs (0.25-log10 reduction), capturing the 85th percentile storm

344

volume with infiltration-based BMPs would result in about 80% annual load reduction (Figure

345

6). This would require about 71,000 bioretention units resulting in a 7 km2 (1730 ac) BMP

346

footprint, or about 2% of the total watershed area.

347

Simulation results also show that installation of 100, 500, and 1,000 BMPs increases the number

348

of wet weather exceedances (Figure 6, left panels; from 29 to 41, 41, and 32 exceedances,

349

respectively). The TMDL defines wet weather days as those with greater than 0.1 inches (2.54

350

mm) of rain and the following three days.37 An increase in wet weather exceedances is hence

351

observed as storm flow is slowly released from BMPs on days following a rain event.

352

Enhanced BMPs

353

Enhanced BMPs may offer significant load reductions compared to conventional BMPs. As

354

shown here, small improvements in BMP performance can result in large reductions of FIB

355

annual load (Figure 6). For example, when runoff from 100% of the watershed area is treated,

356

installing conventional BMPs (0.25-log10 removal) with 3.1·106 m3 (2,500 AF) of treatment

357

volume results in about 80% percent MPN reduction (Figure 6E). However, if the performance

358

of BMPs is improved just slightly, to 0.5-log10 removal, the same reduction could be achieved


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359

with about half the treatment volume (1.5·106 m3 [1,200 AF]). The same reduction (~80%) could

360

also be achieved by treating runoff from 95% of the watershed area with about 2.2·106 m3 (1,800

361

AF) of treatment volume with 0.5-log10 removal BMPs (Figure 6F). This example demonstrates

362

that there may be multiple paths to achieving load-based water quality goals.

363

Our findings support the need to continue developing ways to increase removal of FIB within

364

BMPs. Note that while 50–60% annual load reduction may be adequate for compliance with

365

TMDLs for certain pollutants such as metals,43 FIB vary by orders of magnitude, and therefore

366

multiple log10 reduction may be required for regulatory compliance. Despite the capabilities of

367

bioretention enhancements observed at the laboratory scale, future research is needed to

368

demonstrate longevity and efficacy at the field scale, develop robust cost analyses, and

369

understand maintenance requirements. Long-term BMP performance remains an area where

370

research is needed, both for enhanced and conventional BMP configurations, as BMP efficiency

371

is likely to change over time irrespective of maintenance due to degradation of physical

372

structures or accumulation of pollutants.63,64

373

Infiltrating vs. Non-infiltrating BMPs

374

Conventional (0.25-log10 removal) non-infiltrating (treat-and-release) BMPs may provide very

375

little bacterial water quality benefit, particularly compared to infiltrating BMPs, as demonstrated

376

by comparing TMDL exceedances for non-infiltrating versus infiltrating BMPs in Figure 5.

377

These findings are in agreement with Jefferson et al. who note that most pollutant removal

378

reductions are due to infiltration and not biogeochemical treatment.29 However, when non-

379

infiltrating BMPs are enhanced to provide 3-log10 reduction, the BMP effluent is treated enough

380

to dilute untreated flows, and improve bacterial water quality (Figure 5G versus 5H).


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381

Mass load reductions from non-infiltrating BMPs are sensitive to BMP efficiency because

382

without infiltration, the only mechanisms of FIB removal are the biogeochemical processes

383

within the treatment system (Figure 5). Even enhanced (e.g., 3-log10 removal) non-infiltrating

384

BMPs do not provide the same water quality benefit as conventional infiltration BMPs (Figure

385

5F versus 5H).

386

In reality, a mix of infiltrating and non-infiltrating BMPs should likely be installed to balance

387

societal benefits of water quality, groundwater recharge, and water for reuse and ecosystem

388

services. Despite the water quality benefit of infiltration-based BMPs, installation is not possible

389

in all locations due to soil and groundwater conditions.65 Where conditions prohibit infiltration,

390

stormwater could be captured for alternate uses such as irrigation, but this requires substantial

391

storage in places like Los Angeles with long dry seasons. While infiltrating BMPs improve

392

stormwater quality and increase potential for groundwater recharge, they also reduce surface

393

flow. In the case of Ballona Creek, the reduction in surface flow from BMPs may reduce flow in

394

the channel, decreasing the availability of water for ecosystems or human recreation and benefit.

395

In fact, providing water to Ballona Estuary may prove essential to complete plans to restore the

396

Ballona Creek Wetlands; a new restoration plan has been proposed to remove invasive species

397

and historic fill at the 2.3 km2 (566 ac) site surrounding Ballona Estuary.66

398

While most FIB TMDLs employ concentration-based targets, some are load-based, which has

399

vastly different implications for stormwater management. According to our simulation results,

400

concentration-based TMDLs are best met by employing infiltrating BMPs (either conventional

401

or enhanced), while load-based standards may best be met by installing enhanced BMPs. A

402

major takeaway from this work is therefore that enhanced BMPs are able to meet both


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403

concentration- and load-based water quality targets, while the main mechanism for pollution

404

removal in conventional systems is due to reductions in runoff.

405

ENVIRONMENTAL IMPLICATIONS

406

We have revealed the tradeoffs between BMP performance, BMP type (infiltrating or non-

407

infiltrating), watershed area treated, and number of BMPs installed at the watershed scale. The

408

implications of our findings are useful in planning BMP implementation across the US and

409

internationally as the scale of intervention required to manage urban stormwater globally is

410

significant. For example, New York City plans to invest over $1.4B in capital costs in its green

411

infrastructure program.67 Hence, knowing the tradeoffs between BMP efficiency versus scale

412

and distribution of implementation will prove essential.

413

This work demonstrates that implementation of enhanced BMPs can provide a means for

414

managing urban pollutants at the watershed scale, regardless of whether water quality targets are

415

concentration- or load-based. However, we also show that BMPs may not produce observable

416

differences in water quality, if they are not installed at the appropriate scale or with appropriate

417

removal efficiencies. Specifically, this work confirms that (1) decision makers with limited

418

resources should consider investing in fewer, more efficient BMPs versus more, less-efficient

419

BMPs, and (2) researchers should continue to investigate ways to improve BMP performance,

420

and aim to provide 0.5 or more long-term and continuous log10 removal (68%) in documented

421

field studies.


21


422 423 424 425 426 427 428 429 430 431

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Figure 5. Simulated annual number of wet and dry weather TMDL exceedances at Ballona Creek bacteria monitoring station #5 (BCB-5), for various types of BMPs (conventional vs. enhanced; infiltrating vs. non-infiltrating). Colored contours indicate the number of TMDL exceedances. The wet and dry bacteria TMDL may be met with infiltrating conventional or enhanced BMPs, as long as runoff from 100% of the watershed area is treated. Non-infiltrating conventional BMPs do not meet the dry or wet weather TMDL. However, when non-infiltrating BMPs are enhanced to have 3-log10 bacterial removal, it is possible to meet the wet- but not the dry-weather TMDL if runoff from 100% of the watershed area is treated.


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432

433 434 435 436 437 438 439

Figure 6. Simulated annual bacterial load reduction (right panels, A–D) and wet-weather TMDL exceedances (left panels, E–H) at Ballona Creek bacteria monitoring station #5 (BCB-5), for infiltration-based BMPs. Dotted line on left panels (A–D) signifies compliance with the wetweather TMDL (15 allowable exceedances). Dotted line on right panels (E–H) signifies capture of the 85th percentile storm volume. Simulation scenarios for 90% and 25% watershed area treated are shown in Figure S3.

440

ASSOCIATED CONTENT

441

Supporting Information

442

Model parameters, metrics to assess model performance, aggregation rules from data from the

443

International BMP database, and additional figures.


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444

AUTHOR INFORMATION

445

Corresponding Author

446

*Phone: (650) 721-2615; Email: [email protected]

447

ACKNOWLEDGEMENTS

448

We thank the City of Los Angeles, Bureau of Sanitation, Watershed Protection Division and the

449

County of Los Angeles, Department of Public Works for providing water quality and streamflow

450

data. Project support was provided by the National Science Foundation’s Engineering Research

451

Center for Re-inventing the Nation’s Urban Water Infrastructure (ReNUWIt, NSF ERC

452

1028968) and the UPS Foundation. We appreciate the constructive comments on the simulation

453

results from Mark Gold (UCLA) and on the manuscript from Jon Ball (City of Los Angeles).

454

The authors declare no competing financial interest.

455 456 457

REFERENCES (1) Hering, J. G.; Waite, T. D.; Luthy, R. G.; Drewes, J. E.; Sedlak, D. L. A changing framework for urban water systems. Environ. Sci. Technol. 2013, 47 (19), 10721–10726.

458 459

(2)

Leopold, L. Hydrology for Urban Land Planning - A Guidebook on the Hydrologic Effects of Urban Land Use. Geol. Surv. Circ. 1968, 554, 1–21.

460 461 462

(3)

Brabec, E.; Schulte, S.; Richards, P. L. Impervious Surfaces and Water Quality: A Review of Current Literature and Its Implications for Watershed Planning. J. Plan. Lit. 2002, 16 (4), 499–514.

463 464 465

(4)

McBride, G. B.; Stott, R.; Miller, W.; Bambic, D.; Wuertz, S. Discharge-based QMRA for estimation of public health risks from exposure to stormwater-borne pathogens in recreational waters in the United States. Water Res. 2013, 47 (14), 5282–5297.

466 467 468 469

(5)

Sauer, E. P.; VandeWalle, J. L.; Bootsma, M. J.; McLellan, S. L. Detection of the human specific Bacteroides genetic marker provides evidence of widespread sewage contamination of stormwater in the urban environment. Water Res. 2011, 45 (14), 4081– 4091.

470 471 472

(6)

Sidhu, J. P. S.; Hodgers, L.; Ahmed, W.; Chong, M. N.; Toze, S. Prevalence of human pathogens and indicators in stormwater runoff in Brisbane, Australia. Water Res. 2012, 46 (20), 6652–6660.


24

Page 25 of 29


473 474 475

(7)

US Environmental Protection Agency. National Summary of Impaired Waters and TMDL Information https://iaspub.epa.gov/waters10/attains_nation_cy.control?p_report_type=T (accessed Jul 13, 2017).

476 477

(8)

US Environmental Protection Agency. Federal Water Pollution Control Act; 33 U.S.C. 1251 et seq., 1972.

478 479

(9)

US Environmental Protection Agency. The National Costs of the Total Maximum Daily Load Program (Draft Report); 2001.

480 481 482

(10)

Rabinovici, S. J. M.; Bernknopf, R. L.; Wein, A. M.; Coursey, D. L.; Whitman, R. L. Economic and Health Risk Trade-Offs of Swim Closures at a Lake Michigan Beach. Environ. Sci. Technol. 2004, 38 (10), 2737–2745.

483 484 485

(11)

Boehm, A. B.; Ashbolt, N. J.; Colford, J. M.; Dunbar, L. E.; Fleming, L. E.; Gold, M. A.; Hansel, J. A.; Hunter, P. R.; Ichida, A. M.; McGee, C. D.; et al. A sea change ahead for recreational water quality criteria. J. Water Health 2009, 7 (1), 9–20.

486 487

(12)

Byappanahalli, M. N.; Nevers, M. B.; Korajkic, A.; Staley, Z. R.; Harwood, V. J. Enterococci in the Environment. Microbiol. Mol. Biol. Rev. 2012, 76 (4), 685–706.

488

(13)

US Environmental Protection Agency. Economic Benefits Of Runoff Controls; 1995.

489 490 491

(14)

Geosyntec Consultants; Wright Water Engineers. International Stormwater Best Management Practices (BMP) Database Pollutant Category Statistical Summary Report; 2014.

492 493 494

(15)

Grebel, J. E.; Mohanty, S. K.; Torkelson, A. A.; Boehm, A. B.; Higgins, C. P.; Maxwell, R. M.; Nelson, K. L.; Sedlak, D. L. Engineered Infiltration Systems for Urban Stormwater Reclamation. Environ. Eng. Sci. 2013, 30 (8), 437–454.

495 496 497 498

(16)

Muerdter, C.; Wong, C. K.; LeFevre, G. H. Emerging investigator series: The Role of Vegetation in Bioretention for Stormwater Treatment in the Built Environment: Pollutant Removal, Hydrologic Function, and Ancillary Benefits. Environ. Sci. Water Res. Technol. 2018.

499 500 501

(17)

Peng, J.; Cao, Y.; Rippy, M. A.; Afrooz, A. R. M. N.; Grant, S. B. Indicator and pathogen removal by low impact development best management practices. Water 2016, 8 (600), 1– 24.

502 503 504

(18)

Mohanty, S. K.; Cantrell, K. B.; Nelson, K. L.; Boehm, A. B. Efficacy of biochar to remove Escherichia coli from stormwater under steady and intermittent flow. Water Res. 2014, 61, 288–296.

505 506

(19)

Rippy, M. A. Meeting the criteria: linking biofilter design to fecal indicator bacteria removal. Wiley Interdiscip. Rev. Water 2015, 2 (5), 577–592.

507 508 509

(20)

Chandrasena, G. I.; Shirdashtzadeh, M.; Li, Y. L.; Deletic, A.; Hathaway, J. M.; McCarthy, D. T. Retention and survival of E. coli in stormwater biofilters: Role of vegetation, rhizosphere microorganisms and antimicrobial filter media. Ecol. Eng. 2017,


25


510

Page 26 of 29

102, 166–177.

511 512 513

(21)

Grebel, J. E.; Charbonnet, J. A.; Sedlak, D. L. Oxidation of organic contaminants by manganese oxide geomedia for passive urban stormwater treatment systems. Water Res. 2016, 88, 481–491.

514 515 516

(22)

Taylor, A.; Flatt, A.; Beutel, M.; Wolff, M.; Brownson, K.; Stamets, P. Removal of Escherichia coli from synthetic stormwater using mycofiltration. Ecol. Eng. 2014, 78, 79– 86.

517 518 519

(23)

Ulrich, B. A.; Im, E. A.; Werner, D.; Higgins, C. P. Biochar and activated carbon for enhanced trace organic contaminant retention in stormwater infiltration systems. Environ. Sci. Technol. 2015, 49 (10), 6222–6230.

520 521 522 523

(24)

Mohanty, S. K.; Torkelson, A. A.; Dodd, H.; Nelson, K. L.; Boehm, A. B. Engineering solutions to improve the removal of fecal indicator bacteria by bioinfiltration systems during intermittent flow of stormwater. Environ. Sci. Technol. 2013, 47 (19), 10791– 10798.

524 525 526 527

(25)

Jasper, J. T.; Nguyen, M. T.; Jones, Z. L.; Ismail, N. S.; Sedlak, D. L.; Sharp, J. O.; Luthy, R. G.; Horne, A. J.; Nelson, K. L. Unit Process Wetlands for Removal of Trace Organic Contaminants and Pathogens from Municipal Wastewater Effluents. Environ. Eng. Sci. 2013, 30 (8), 421–436.

528 529

(26)

Read, J.; Wevill, T.; Fletcher, T.; Deletic, A. Variation among plant species in pollutant removal from stormwater in biofiltration systems. Water Res. 2008, 42 (4–5), 893–902.

530 531 532

(27)

Payne, E. G. I.; Pham, T.; Cook, P. L. M.; Fletcher, T. D.; Hatt, B. E.; Deletic, A. Biofilter design for effective nitrogen removal from stormwater - Influence of plant species, inflow hydrology and use of a saturated zone. Water Sci. Technol. 2014, 69 (6), 1312–1319.

533 534 535

(28)

Wolfand, J. M.; LeFevre, G. H.; Luthy, R. G. Metabolization and degradation kinetics of the urban-use pesticide fipronil by white rot fungus Trametes versicolor. Environ. Sci. Process. Impacts 2016, 18 (10), 1256–1265.

536 537 538

(29)

Jefferson, A. J.; Bhaskar, A. S.; Hopkins, K. G.; Fanelli, R.; Avellaneda, P. M.; McMillan, S. K. Stormwater management network effectiveness and implications for urban watershed function: a critical review. Hydrol. Process. 2017, 1–25.

539 540

(30)

Bell, C. D.; McMillan, S. K.; Clinton, S. M.; Jefferson, A. J. Hydrologic response to stormwater control measures in urban watersheds. J. Hydrol. 2016, 541, 1488–1500.

541 542 543

(31)

Bell, C. D.; McMillan, S. K.; Clinton, S. M.; Jefferson, A. J. Characterizing the Effects of Stormwater Mitigation on Nutrient Export and Stream Concentrations. Environ. Manage. 2017, 59 (4), 604–618.

544 545

(32)

Wright Water Engineers; Geosyntec Consultants. International Stormwater BMP Database http://www.bmpdatabase.org/ (accessed Oct 1, 2016).

546

(33)

Ballona Creek Watershed Management Group. Enhanced Watershed Management


26

Page 27 of 29


547

Program (EWMP) Final Work Plan; 2014.

548 549

(34)

Liu, S.; Hogue, T.; Stein, E. D.; Barco, J. Contemporary and Historical Hydrologic Analysis of the Ballona Creek Watershed; 2011.

550 551 552

(35)

Manago, K. F.; Hogue, T. S. Urban Streamflow Response to Imported Water and Water Conservation Policies in Los Angeles, California. J. Am. Water Resour. Assoc. 2017, 53 (3), 626–640.

553 554 555 556

(36)

Los Angeles County Department of Public Works; Ballona Creek Renaissance; Los Angeles City Department of Public Works; National Park Service; Santa Monica Bay Restoration Commission; Ballona Creek Watershed Task Force. Ballona Creek Watershed Management Plan; 2004.

557 558 559 560

(37)

California Regional Water Quality Control Board. Amendment to the Water Quality Control Plan for the Los Angeles Region to Revise the Total Maximum Daily Load for Bacterial Indicator Densities in Ballona Creek, Ballona Estuary, and Sepulveda Channel (Attachement A to Resolution No. R12-008); 2012; pp 1–12.

561 562 563

(38)

Curren, J.; Bush, S.; Ha, S.; Stenstrom, M. K.; Lau, S. L.; Suffet, I. H. M. Identification of subwatershed sources for chlorinated pesticides and polychlorinated biphenyls in the Ballona Creek watershed. Sci. Total Environ. 2011, 409 (13), 2525–2533.

564 565

(39)

Stein, E. D.; Tiefenthaler, L. L. Dry-weather metals and bacteria loading in an arid, urban watershed: Ballona Creek, California. Water. Air. Soil Pollut. 2005, 164, 367–382.

566 567 568

(40)

Chinen, K.; Lau, S.; Nonezyan, M.; McElroy, E.; Wolfe, B. Predicting runoff induced mass loads in urban watersheds: Linking land use and pyrethroid contamination. Water Res. 2016, 102, 607–618.

569 570

(41)

U.S. Environmental Protection Agency. Storm Water Management Model Version 5.1. Cincinnati, Ohio 2017.

571 572

(42)

US Environmental Protection Agency. System for Urban Stormwater Treatment and Analysis IntegratioN (SUSTAIN). 2014.

573 574

(43)

Gold, M.; Hogue, T.; Pincetl, S.; Mika, K.; Radavich, K. Los Angeles Sustainable Water Project: Ballona Creek Watershed. 2015.

575

(44)

Green, W. H.; Ampt, G. A. Studies on Soil Physics. J. Agric. Sci. 1911, 4 (1), 1–24.

576

(45)

Rossman, L. A. Storm Water Management Model User’s Manual; 2015.

577 578

(46)

Gesch, D. B.; Oimoen, M. J.; Greenlee, S. K.; Nelson, C. A.; Steuck, M. J.; Tyler, D. J. The national elevation data set. Photogramm. Eng. Remote Sensing 2002, 68 (1), 5–11.

579 580 581

(47)

Xian, G.; Homer, C.; Dewitz, J.; Fry, J.; Hossain, N.; Wickham, J. The change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogramm. Eng. Remote Sensing 2011, 77 (8), 758–762.

582

(48)

Weber, D. D.; Englund, E. J. Evaluation and comparison of spatial interpolators 2. Math.


27


583

Page 28 of 29

Geol. 1994, 26 (5), 589–603.

584 585

(49)

Sloto, R. A.; Crouse, M. Y. Hysep: a computer program for streamflow hydrograph separation and analysis; 1996.

586 587 588

(50)

Shoemaker, L.; Riverson, J. J.; Alvi, K.; Zhen, J. X.; Paul, S.; Rafi, T. SUSTAIN - A Framework for Placement of Best Management Practices in Urban Watersheds to Protect Water Quality. Environ. Prot. 2009, No. September, 1–8.

589 590 591

(51)

Radavich, K. Assessing the Effect of Best Management Practices on Water Quality and Flow Regime in an Urban Watershed Under Climate Change Disturbance, Colorado School of Mines, 2015.

592 593

(52)

Beck, A. Evaluating Best Management Practice Scenarios in Ballona Creek Watershed Using EPA’s SUSTAIN Model, Colorado School of Mines, 2014.

594 595

(53)

Edgley, R. Asessing the Efficacy of BMPs to Reduce Metal Loads in the Los Angeles River Basin at the Watershed Scale, Colorado School of Mines, 2016.

596 597

(54)

Mao, X.; Jia, H.; Yu, S. L. Assessing the ecological benefits of aggregate LID-BMPs through modelling. Ecol. Modell. 2017, 353, 139–149.

598 599

(55)

Mika, K.; Gallo, E.; Read, L.; Edgley, R.; Truong, K.; Hogue, T.; Pincetl, S.; Gold, M. LA Sustainable Water Project: Los Angeles River Watershed; 2017.

600 601

(56)

Ballona Creek Watershed Management Group. Enhanced Watershed Management Program for the Ballona Creek Watershed; 2016.

602 603

(57)

Hewlett, J. D.; Hibbert, A. R. Factors affecting the response of small watershed to precipitation in humid areas. For. Hydrol. 1967, 275–279.

604 605

(58)

R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria 2017.

606 607

(59)

Crittenden, J. C.; Trussell, R. R.; Hand, D. W.; Howe, K. J.; Tchobanoglous, G. MWH’s Water Treatment: Principles and Design, Third.; John Wiley & Sons, Ltd., 2012.

608 609

(60)

Ackerman, D.; Schiff, K. C.; Weisberg, S. B. Evaluating HSPF in an Arid, Urbanized Watershed. J. Am. Water Resour. Assoc. 2005, 41 (2), 477–486.

610 611 612

(61)

Muleta, M. K.; Mcmillan, J.; Amenu, G. G.; Burian, S. J. Bayesian Approach for Uncertainty Analysis of an Urban Storm Water Model and Its Application to a Heavily Urbanized Watershed. J. Hydrol. Eng. 2013, 18 (10), 1360–1371.

613 614

(62)

County of Los Angeles. Los https://egis3.lacounty.gov/dataportal/.

615 616 617

(63)

Liu, Y.; Engel, B. A.; Flanagan, D. C.; Gitau, M. W.; McMillan, S. K.; Chaubey, I. A review on effectiveness of best management practices in improving hydrology and water quality: Needs and opportunities. Sci. Total Environ. 2017, 601–602, 580–593.

Angeles


County

GIS

Data

Portal

28

Page 29 of 29


618 619 620 621 622

(64)

Liu, Y.; Engel, B. A.; Flanagan, D. C.; Gitau, M. W.; McMillan, S. K.; Chaubey, I.; Singh, S. Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: Framework development and demonstration using a Bayesian method. J. Hydrol. 2018, 560 (March), 530–545.

623

(65)

Los Angeles Department of Water & Power. Stormwater Capture Master Plan; 2015.

624 625 626

(66)

United States Army Corps of Engineers Los Angeles District; California Department of Fish and Wildlife South Coast Region. Draft Ballona Wetlands Restoration Project Environmental Impact Statement/Environmental Impact Report; 2017.

627

(67)

NYC Environmental Protection. NYC Green Infrastructure: 2016 Annual Report; 2012.


29

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