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Bacteria Total Maximum Daily Load

Task Force Report



Draft

November 22, 2006


Prepared for:

Texas Commission on Environmental Quality

And

Texas State Soil and Water Conservation Board



Table of Contents

Executive Summary 1


Introduction 2
Bacteria Fate and Transport Models 4
Bacteria Source Tracking 10

Recommended Decision-Making Process for Texas 21

TMDL and Implementation Plan Development
Research and Development Needs 22
References 31
Appendix 1: Bacteria TMDL Task Force Personnel 34
Appendix 2: Models Used in Bacteria Projects 36 as Described in EPA Publications
Appendix 3: EPA Bacteria TMDL Guidelines 41
Appendix 4: State Approaches to Bacteria TMDL 50

Development


Appendix 5: Comments from Expert Advisory Group 60

Executive Summary


Introduction
One hundred and ninety-seven (197) waterbodies in Texas are impaired because they do not meet bacteria criteria established by the state to protect contact recreation use (freshwater and saltwater) and/or oyster water use. The freshwater contact recreation use criterion used to determine impairment includes both a geometric mean for E. coli of 126 per 100 ml and a single sample maximum of 394 per 100 ml. The saltwater contact recreation use criterion includes both a geometric mean for Enterococci of 35 per 100 ml and a single sample maximum of 89 per 100 ml. Finally, the oyster water use criteria includes a median fecal coliform concentration of 14 colonies per 100 ml and no more than 10% of samples may exceed 43 colonies per 100 ml. The ongoing Triennial Water Quality Standards Review process will be re-examining these criteria.
As required by Section 303(d) of the Clean Water Act, Texas has committed to complete TMDLs for these bacteria impaired waterbodies within 13 years of the listing date (i.e. 2017 for new waterbodies listed on the 2004 list). In order to identify the best and most cost- and time-effective methods to develop bacteria TMDLs and TMDL Implementation Plans, the Texas Commission on Environmental Quality (TCEQ) and the Texas State Soil and Water Conservation Board (TSSWCB) established a joint technical Task Force on Bacteria TMDLs on September 27, 2006. The Task Force was charged with:

  • reviewing U.S. Environmental Protection Agency (EPA) Total Maximum Daily Load (TMDL) guidelines and approaches taken by selected states to TMDL and implementation plan (I-Plan) development

  • evaluating scientific tools, including bacteria fate and transport modeling and bacterial source tracking (BST)

  • suggesting alternative approaches using bacteria modeling and BST for TMDL and I-Plan development, emphasizing scientific quality, timeliness and cost effectiveness

  • identifying gaps in our understanding of bacteria bacteria fate and transport requiring additional research and tool development

Task Force members are Drs. Allan Jones, Texas Water Resources Institute; George Di Giovanni, Texas Agricultural Experiment Station–El Paso; Larry Hauck, Texas Institute for Applied Environmental Research; Joanna Mott, Texas A&M University–Corpus Christi; Hanadi Rifai, University of Houston; Raghavan Srinivasan, Texas A&M University; and George Ward, University of Texas at Austin. Dr. Allan Jones was named Task Force Chair by TCEQ and TSSWCB.
Approximately 40 Expert Advisors (Appendix 1) with expertise on bacteria related issues have also provided significant input to the Task Force during the process. Included in this group are local, state and federal agencies with jurisdictions impacting bacteria and water quality offered guidance to the Task Force.
Recommendations from the Task Force are intended to be used by the State of Texas, specifically TSSWCB and TCEQ, to keep Texas as a national leader in water quality protection and restoration.
Bacteria Fate and Transport Models
This section, coordinated by Drs. Hanadi Rifai and Raghavan Srinivasan, describes the strengths and weaknesses of several bacteria fate and transport models that have been used. A more complete list of modeling tools taken from EPA publication is in Appendix 2.
Bacterial pollution in surface water bodies is a complex phenomenon to model because of their numerous sources in a given watershed and the various fate and transport processes that control their behavior and distribution in water systems. Bacterial indicators such as E. coli, Enterococcus spp., and fecal coliform, although typically nonpathogenic, are used to identify the potential for the presence of other disease causing organisms. These originate from human and non-human sources and they are released into water bodies via point sources (such as wastewater treatment plant effluent and runoff from stormwater drainage networks) as well as dispersed (or nonpoint) sources (such as direct runoff from residential yards and streets, on-site sewage disposal, deposition from birds, and re-suspension of bacteria from stream sediment). Bacteria are present in water and sediment, and experience re-growth and death within a water body. Furthermore, bacteria loads into a stream vary spatially and temporally because of the variability of flow within the stream network and of the different loads coming from the various sources at different times into the stream. Bacteria are living organisms and do not behave like chemical water quality parameters. These factors and considerations, along with the need of a tool that is predictive to simulate measures to obtain allowable loads for WLA and LA and the need for a means of determining allowable loading and percent reduction required, motivate the desire for using models in the bacteria TMDL process to estimate resulting in stream bacteria concentrations. However, selecting an appropriate model for bacteria TMDLs is a challenge in and of itself, due to the numerous water quality models that are available. Characteristics of each watercourse and the nature of the pollutant loads must drive the selection. Thus, establishing the goal of the modeling within the context of a TMDL is a very important and critical step that is undertaken early in the Texas TMDL development process in consultation with stakeholders and using professional services (modelers under contract, etc.).
Since bacteria TMDLs estimate the maximum bacteria load that a waterbody can receive and still meet water quality standards. TMDL development involves estimating both existing and allowable loads, the instream water quality effects of these loads, and as well as the reductions that would be required to meet standards. TMDL implementation, on the other hand, involves designing realistic bacteria reduction strategies for different sources and examining their effects on water quality. These differing goals between TMDL development and implementation may necessitate the use of different bacteria models with different levels of sophistication.
The two basic modeling strategies that have been used for developing and implementing TMDLs involve: (1) the use of statistical models or mass balance models that rely on available flow and water quality data, and (2) the use of mechanistic (process or physically based) hydrologic/water quality models combined with landscape loading models. The most common models within the two strategies that have been used for bacteria TMDLs are described below.
Statistical and Mass Balance Bacteria Models
The most common of the statistical models used in bacteria TMDLs has been the Load Duration Curve (see Appendix 4). Mass balance methods, on the other hand, while commonly used, are not uniform in their approach and tend to be watershed specific.
Load Duration Curves (LDC)

This method has been successfully used in TMDL development for estimating existing and allowable loads, and the reductions required to meet water quality standards. It can be done from a desktop, based on information that is readily available. This method can only be used in a generic sense to allocate sources to end-of-pipe and nonpoint sources. The LDC method, however, is not as well suited for TMDL implementation and development of strategies for load reductions within the watershed because it cannot be used to estimate loads from specific sources within the watershed.


Briefly, the LDC method involves developing a flow duration curve or a representation of the percentage of days in a year when a given instream flow occurs. The allowable bacteria load curve is calculated using this flow duration curve by multiplying the flow values by the applicable bacterial criterion. The observed bacteria loads in the water body are plotted on the developed curve and the points that fall above the allowable bacteria load curve indicate exceedances while the points that fall below the curve indicate acceptable loads.
The advantage of this method is its simplicity, and minimal data requirements (flow and bacteria concentrations). However, in order to ensure accurate estimates, a large number of both flow and bacteria observations are required. Existing loading and load reductions required to meet the TMDL water quality target can be calculated under different flow conditions or range of flow. The main disadvantage, as mentioned previously, is the method does not allow estimating loads from specific sources within the watershed, and does not presume that there are spatial and temporal variations in source or in-stream loads and can not be used to evaluate load reduction strategies and control measures. However, LDCs can be done on a seasonal basis and at intervals along the length of the stream to integrate spatial and temporal variations. Other disadvantages include (a) the inability of managers to assess water quality responses for varying implementation or load reduction scenarios; (b) older observed data may skew the TMDL towards sources that are no longer relevant due to changes in the watershed; (c) the LDC only applies to points in the stream at which samples were taken; and (d) the TMDL duration and frequency targets cannot be directly compared to the LDC.
Mass Balance Method

The method, as the name implies, involves undertaking a mass balance between source loads entering the water body and the bacteria load within the stream. Sources are typically inventoried, quantified and compared to existing and allowable in-stream loads at specified points within the stream (typically, where the TMDL is sought) for different flow conditions. Mass balance methods require more data than the LDC method, but are more amenable for use in TMDL implementation. These methods have typically been developed using spreadsheets. The main advantages of the mass balance method are that they can be used for tidal and non-tidal water bodies, for TMDL development and implementation, and more importantly for watersheds where the distinction between end-of-pipe and nonpoint sources is not apparent at the different flow levels (in other words, both categories of sources come into play at low flow and high flow). The main disadvantage is that the mass balance method, similar to the LDC method, is static and does not allow for temporal variations in loading. The mass balance method, however, does account for spatial variations since it estimates the various sources within the watershed.


In Texas, one of the more recent mass balance applications is described in Petersen (2006). The Bacteria Load Estimator Spreadsheet Tool (BLEST) has been used to calculate bacteria loads from all sources and land uses on a subwatershed basis for Buffalo and White Oak Bayous. The loads are accumulated by segment and calculated for low, median and high flow conditions in a stream. Sources include wastewater treatment plants, septic tanks, nonpoint source runoff, sanitary sewer overflows and bypasses, sewer leaks and spills, in-stream sediment and wildlife and domesticated animals. BLEST was used to calculate existing loads and allowable loads and to estimate the load reductions that would be required to meet the standard.
Mechanistic Hydrologic/Water Quality Bacteria Models
A number of simulation models exist that describe (in mathematical form) the mechanisms of water movement as well as the transport of pollutants. Both researchers and managers desire to have a means to create scenarios to simulate environmental outcomes in response to specific activities. Models are used to predict the water quality in a water body based upon changes in pollutant loading and various allocation strategies. These models can be used both for TMDL development and implementation and for evaluating spatial and temporal variation of bacteria loading within a watershed. These models, however, suffer from their extensive data requirements, their level of sophistication that necessitates a significant investment in resources, and their complex predictive nature. In general, in-stream water quality models are steady-state or transient and they are typically hydrologically driven (via rainfall) or hydrodynamically driven (via velocities in the water body). A steady-state model does not allow for variations over time, and, in essence, shows a “snapshot” of water quality in a stream. A dynamic or transient model, on the other hand, allows for changes over time and can be used to estimate bacteria loads and concentrations at different points in time anywhere in the stream.
Ward and Benaman (1999) identified a number of models as being appropriate for use in Texas TMDLs. Their list includes: ANSWERS, CE-QUAL-W2, DYNHYD, EFDC, GLEAMS, HSPF, POM, PRMS, QUALTX, SWAT, SWMM, TxBLEND and WASP. Their assessment categorized these models based on the watercourse type and the scale of resolution for time. So for example, HSPF, SWAT, PRSM, SWMM and ANSWERS were characterized as watershed type models that can be used for “slow time variation” and “continuous time variation,” and all but SWAT can be used to model the time scale for a single storm event. There are also additional tools that have been used to develop inputs for bacteria models. These include the Bacteria Source Load Calculator (BSLC) and the Bacteria Indicator Tool (BIT). Both of these have been used for TMDL development. BIT has been used in Texas. ArcHydro, although not considered a typical model, is another tool used to develop bacteria loading estimates. This was done for Galveston Bay.
Of the above list of models identified by Ward and Benaman (1999) for use in Texas TMDLs, the most commonly used for bacteria include: HSPF, SWAT, SWMM, and WASP, with HSPF being the most commonly used of the four. These models have many similarities and differences. They all share the characteristics of being data and time intensive, i.e., all four models require many input variables, a substantial investment in set-up, calibration and validation time, and have a steep learning curve. The differences between the four models are discussed below.

HSPF (Hydrological Simulation Program – FORTRAN)

HSPF has been in extensive use since the 1970’s, and is distributed by EPA’s Center for Exposure Assessment Modeling. This watershed hydrologic model has been commonly used for TMDL development of a variety of conventional water quality parameters in Texas and other states. The required data include land use, watershed and subwatershed boundaries, location and data for rainfall gages and surface water quality monitoring stations, detailed descriptions of stream geometry and capacity, detailed information about sources within the watershed, sedimentation and re-suspension characteristics, and bacteria die-off rates, to name a few. Development of an HSPF model for a given watershed is both complex and time consuming and involves a calibration and validation step. The advantage of HSPF is it can be used for most types of watersheds (except possibly tidally influenced streams) regardless of the land use, and it relies on hydrologic and hydraulic models as well as GIS data layers for its input. HSPF allows for a detailed spatial resolution within the watershed and allows for estimation of bacteria loads from runoff from the land surface as well as re-suspension from the streambed and from direct deposition sources. Additionally, HSPF simulates in-stream water quality. The disadvantages include the inherent difficulty in its application, its poor documentation and inadequate simulation of bacteria fate and transport processes (for example, transport of bacteria associated with sediment, sedimentation and re-suspension, re-growth and die-off processes are simplified and end up being treated as calibration variables).


SWAT (Soil and Water Assessment Tool)

The SWAT model is a continuation of nearly 30 years of modeling efforts conducted by the United States Department of Agriculture (USDA) Agricultural Research Service (ARS). SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool as evidenced by international SWAT conferences, SWAT-related papers presented at numerous other scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the EPA Better Assessment Science Integrating Point & Nonpoint Sources (BASINS) software package and is being used by many federal and state agencies, including the USDA within the Conservation Effects Assessment Project (CEAP). Reviews of SWAT applications and/or components have been previously reported, sometimes in conjunction with comparisons with other models (e.g., Arnold and Fohrer, 2005; Borah and Bera, 2003; Borah and Bera, 2004; Steinhardt and Volk, 2003). (Gassman, et. al 2005).


This model, developed as an improvement over SWRRB (Simulator for Water Resources in Rural Basins), was primarily developed to estimate loads from rural and mainly agricultural watersheds; however, the capability for including impervious cover was accomplished by adding urban build up/wash off equations from SWMM. A microbial sub-model was incorporated to SWAT for use at the watershed or river basin levels. The microbial sub-model simulates (1) functional relationships for both the die-off and re-growth rates and (2) release and transport of pathogenic organisms from various sources that have distinctly different biological and physical characteristics. SWAT has been used in Virginia and North Carolina for bacteria TMDL development. .
SWMM (Storm Water Management Model)

This model was developed primarily for urban areas. SWMM simulates real storm events based on meteorological data and watershed data, and that has been the most common way for applying the model, although it can be used for continuous simulations. While SWMM was developed with urban watersheds in mind, it can be used for other watersheds. The biggest advantage of SWMM is in its ability to model the detailed urban drainage infrastructure including drains, detention basins, sewers and related flow controls. One of the key disadvantages of SWMM, however, is it does not simulate the in-stream water quality or the quality within the receiving stream. This limitation can be circumvented by linking it to WASP. Perhaps the best application for SWMM can be to characterize the bacterial pollution from the urban drainage infrastructure but this somewhat limits the usefulness of SWMM within a bacterial TMDL context to implementation rather than TMDL development.


WASP (Water-quality Analysis Simulation Program)

This model is also distributed by EPA’s Center for Exposure Assessment Modeling. It is a well-established water quality model incorporating transport and reaction kinetics. Unlike HSPF, however, WASP is not rainfall-driven, rather it is velocity-driven, thus it is usually coupled with a suitable hydrodynamic model such as DYNHYD or EFDC. WASP is typically used for main channels, reservoirs and for bays and estuaries and not for modeling watershed-scale processes. Problems studied using WASP include biochemical oxygen demand and sources of bacteria. dissolved oxygen dynamics nutrients and eutrophication, organic chemical and heavy metal contamination.



Bacteria Source Tracking
Overview (someone suggested more divisions??)
This section, coordinated by Drs. George Di Giovanni and Joanna Mott, describes the strengths and weaknesses of several bacterial source tracking (BST) tools that have been used. The USEPA has also issued a microbial source tracking guidance document (USEPA 2005) which provides technical details on many different BST methods, quality control measures, project design, and case studies.
The premise behind BST is that genetic and phenotypic tests can identify bacterial strains that are host specific so that the original host animal and source of the fecal contamination can be identified. Often Escherichia coli (E. coli) or Enterococcus spp. are used as the bacteria targets in source tracking (for example, (Parveen, Portier et al. 1999; Dombek, Johnson et al. 2000; Graves, Hagedorn et al. 2002; Griffith, Weisberg et al. 2003; Hartel, Summer et al. 2003; Kuntz, Hartel et al. 2003; Stoeckel, Mathes et al. 2004; Scott, Jenkins et al. 2005)). While there has been some controversy concerning host specificity and survival of E. coli in the environment (Gordon, Bauer et al. 2002), this indicator organism has the advantage that it is known to correlate with the presence of fecal contamination and is used for human health risk assessments. Bacterial source tracking of E. coli, therefore, has the advantages of direct regulatory significance and availability of standardized culturing techniques for water samples, such as EPA’s Method 1603 (USEPA 2005).
There have been many different technical approaches to bacterial source tracking (Scott, Rose et al. 2002; Simpson, Santo Domingo et al. 2002; Meays, Broersma et al. 2004), but there is currently no consensus on a single method for field application. Genotypic (molecular) tools appear to hold promise for BST, providing the most conclusive characterization and level of discrimination for isolates. Of the molecular tools available, ribosomal ribonucleic acid (RNA), genetic fingerprinting (ribotyping), repetitive element polymerase chain reaction (rep-PCR), and pulsed-field gel electrophoresis (PFGE) are emerging as a few of the versatile and feasible. BST techniques Antibiotic resistance profiling, a phenotypic characterization method, also has the potential to identify the human or animal origin of isolates, and variations of this technique have been applied in several BST studies. (See Table 1 below)


Technique

Acronym

Target organism(s)

Basis of characterization

Previously Used in Texas

Used in other states

Accuracy of source identification

Size of library needed for water isolate IDs

Capital cost

Cost per sample (reagents and consumables only)

Ease of use

Hands on processing time for 32 isolates

Time required to complete processing 32 isolates

Enterobacterial repetitive intergenic consensus sequence polymerase chain reaction

ERIC-PCR

Escherichia coli

(E. coli) and Enterococcus spp.



DNA fingerprint

Yes

(Di Giovanni)



Yes

Moderate

Moderate

$20,000

($15,000 BioNumerics software, $5,000 equipment)



$8

Moderate

3 h

24 h**

Automated ribotyping (RiboPrinting)†

RP

E. coli and Enterococcus spp.

DNA fingerprint

Yes

(Di Giovanni)



Yes

Moderate

Moderate

$115,000

($100K RiboPrinter, $15K BioNumerics software)



$40

Easy

1 h

24 h

Pulsed field gel electrophoresis

PFGE

E. coli and Enterococcus spp.

DNA fingerprint

Yes

(Pillai and Lehman)



Yes

High

Large

$30,000

$40

Difficult

10 h

5 days

Kirby-Bauer antibiotic resistance analysis‡

KB-ARA

E. coli and Enterococcus spp.

Phenotypic fingerprint

Yes

(Mott)


Yes

Moderate*

Moderate

$35,000

$15

Easy

3 h

24 h**

Carbon source utilization

CSU

E. coli and Enterococcus spp.

Phenotypic fingerprint

Yes

(Mott)


Yes

Moderate

Moderate

$15,000

$10

Easy

4 h

24 h**

Bacteriodales polymerase chain reaction

Bacterio-dales PCR

Bacteriodales species

Genetic marker presence or absence

(not quantitative)



No

Yes

Moderate to high for only human, ruminant, horse, and pig sources

Not applicable

$5,000

$8

Easy to moderate

3 h

8 h**

Enterococcus faecium surface protein polymerase chain reaction or colony hyb.

E. faecium esp marker

E. faecium

Genetic marker presence or absence

(not quantitative)



No

Yes

High for only human

Not applicable

$8,000

$8 to $12

Easy to moderate

3 to 6 h

8 to 24 h**

ERIC and RP 2-method composite

ERIC-RP

E. coli

DNA fingerprints

Yes

(Di Giovanni)



No

Moderate to high

Moderate

$120,000

$48

Moderate

4 h

24 h

ERIC and KB-ARA 2-method composite

ERIC-ARA

E. coli

DNA and phenotypic fingerprints

Yes

(Di Giovanni)



No

Moderate to high

Moderate

$55,000

$23

Moderate

6 h

24 h

KB-ARA and CSU 2-method composite

ARA-CSU

E. coli and Enterococcus spp.

Phenotypic fingerprints

Yes

(Mott)


Yes

Moderate to high

Moderate

$50,000

$23

Easy to moderate

7 h

24 h
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