April 10 – 13 Henry B. Gonzalez Convention Center San Antonio/media/Files/A/Atkins-Corporate/north... · Water Supply Management Using AMI Technology Bernard Dunham Delta Engineering

Texas Water 2012conference program

April 10 – 13 Henry B. Gonzalez Convention Center

San Antonio

2012 Conference Schedule....4-5Conference Highlights..........8-9Competitions........................24-25Exhibitor List..........................36-44 Facility Tours.................................58 Gloyna Breakfast........................19

Where to find...Guest Program............................58 Special Guests................................6 Host Committee.........................22 Maps..........................................27-29 Match the Name Game..........25 Presenter Contacts.............48-52

Quick Exhibitor Index.......31-34 Sponsors........................................26 TAWWA....................................56-57 Tech Sessions Schedule...12-19WEAT.........................................54-55

Water Conservation Utility Management & Workforce Issues Watershed Management Operator Forum

Demand Management Strategies for Tarrant

Regional Water DistrictBrian McDonald

Alan Plummer AssociatesMark Olson

Tarrant Regional Water District

Removing the Blinders: Using Dynamic Modeling to Promote

Financial SustainabilityJennifer Ivey

Red Oak ConsultingSkipper Shook

City of Fort Worth

Watershed Management To Address Nutrient and Sediment Issues

Paul JensenAtkins North America

Ka-Leung LeeAtkins North America

David BuzanAtkins North America

Field Testing Addresses Operations and

Budget ChallengesKathy Fretwell

Kennedy Jenks ConsultantsAurora Gonzales

Kennedy Jenks Consultants

9:30 - 10:00 am

Tools to Determine Water Savings – Engineering End-Use

Models vs. Dynamic Models: A Dallas Case Study

Fujiang WenDallas Water Utilities

Getting It Right: A Study of Cost of Service Wastewater

Treatment AllocationsSkipper Shook

City of Fort WorthJennifer Ivey

Red Oak Consulting

Engineering a Natural Solution to an Unnatural Challenge: Shoreline Stabilization and

Beautification on Lady Bird Lake Trail, Austin

Heather Harris CH2M HILL

Morgan ByarsCity of Austin

Comparing Solid State Water Meters to Positive

Displacement Meters in Residential Services

Craig HannahJohnson Controls

10:00 - 10:30 am

Untapped Potential: The Effectiveness of

Municipally-Driven ICI Water Audit Programs

Micah ReedCity of Fort Worth Water Department

Decision Making in the Face of Risk and Uncertainty

Jeffrey EdmondsURS Corporation

Evaluation Of Water Quality Models and Development of Example

EPDRiv1 and IWRS Models for San Antonio River

Sheeba Thomas San Antonio River Authority

Yu-Chun Su Atkins North America

Xin He Atkins North America

Ka-Leung Lee Atkins North America

Cost EffectiveAutomated Dead End Water Main Flushing

Aaron RussellCity of Burleson

10:30 - 11:00 am

When the Rain Stopped: Two Cities’ Pursuit of Water

During Historic Drought Conditions in Central Texas

Aaron ArcherHDR EngineeringKenneth WheelerCity of Cedar Park

Wayne WattsCity of Leander

Hiring Texas VeteransBryan Daye

Texas Veterans Commission

Effects of the 2011 Drought in Texas

Karl WintersU.S. Geological Survey

Gregory StantonU.S. Geological Survey

Water SupplyManagement Using

AMI TechnologyBernard Dunham

Delta Engineering Sales

11:00 - 11:30 am

North Texas Demonstrates Correlation Between Public Education and

Conservation BehaviorDenise Hickey

North Texas Municipal Water DistrictValerie Davis

EnviroMedia Social Marketing

Creating Your Own Workforce: City of Waco Partnership

for Water EducationTeresa BryantCity of Waco

Jonathon EcholsCity of Waco

Dynamic Water Quality Modeling in Support of a Watershed

Protection Plan for Bastrop Bayou in Brazoria County

Yu-Chun SuAtkins North America

Paul JensenAtkins North America

Ka-Leung LeeAtkins North America

Justin BowerHouston-Galveston Area Council

Operators and Engineers Working Together Provides

for Project SuccessJeff Sober

Carollo EngineersJohn Bennett

Trinity River Authority of Texas

11:30 am - N

oonTECHNICAL SESSIONS THURSDAY MORNING, APRIL 12

www.texas-water.com 15

ROOM 203-BModerators

Roger SchenkCDM Smith

ROOM 204-AModeratorsKatie McCain

Wachs Utility ServiceDean Sharp

Water Resources Management

ROOM 204-BModerators

Tom RayLockwood Andrews and Newnam

ROOM 101-A/BModeratorsSteve Fife

Baytown Water Authority

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Dynamic Water Quality Modeling in Support of a Watershed Protection Plan for Bastrop BayouAtkins: Yu‐Chun Su, PhD,PE,CFM,CPSWQ,CPESC

Paul Jensen, PhD,PE,BCEEKa‐Leung Lee, PhD,PE,CFM,CPSWQ

H‐GAC: Justin Bower

The Bastrop Bayou Watershed Located on Upper T C tTexas Coast

within 13‐county H‐GAC Region

Drains to Bastrop Bay/West Bay Part of Galveston Bay system

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The Bastrop Bayou Watershed

Includes Bastrop Bayou (1105) d t ib t i(1105), and tributaries: Brushy Creek (1105E) Flores Bayou (1105A) Austin Bayou (1105B/C

P i L d Primary Land uses: Agriculture

Undeveloped land Urban/suburban (limited)

Project Background Primary water quality challenge is bacteria

Watershed Protection Plan began in 2006; no impairment then.

Impetus was concern about impact of future growth, public health

As of 2010 303d, Flores and Brushy Bayou impaired

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Project Background Sources of Bacteria established through: SELECT modeling

Literature values Stakeholder input

Primary sources Agriculture (Cattle) Pets/Urban runoff OSSFs

WPP focus on voluntary efforts/prevention

Project Background WPP was stakeholder‐based effort

Identified BMPs to reduce /prevent bacteria loadings

EPA requires evaluation of impact with monitoring and modeling, including: Load Duration Curves SELECT

Tidal Prism/EPDRIV‐1

Atkins selected as consultant for final modeling effort

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Modeling Project GoalsThe goals of the EPDRIV‐1 modeling effort were to

levaluate:

Effect of in‐stream processes on potential bacteria loads

Tidal processes effects on contaminant removal from the watershed.

Impact of projected load reductions from stakeholder BMPs on indicator bacteria concentrations.

Subbasinsand Monitoring Stations in Stations in Bastrop Bayou Watershed

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XP-SWMM Watershed Modeling

EPDRIV1 Background

Dynamic 1D Hydrodynamic and WQ stream/river

model (not including watershed modeling)

Based upon CE-QUAL-RIV1 by USACE.

Developed by Lloyd Chris Wilson, Wilson

Engineering

Sponsored by Roy Burke III, Georgia EPD Sponsored by Roy Burke III, Georgia EPD

Funded by US EPA, Region IV

Riv1H and Riv1Q by Robert Olson, NRE, Inc.

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EPDRIV1 Setup

Hydrodynamic Input File

WQ I t Fil WQ Input File

Lateral Inflows

Withdrawals/Diversions

Cross Sections Cross Sections

Boundary Conditions

Meteorological

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EPDRIV1 Stream Network

EPDRIV1 Hydrodynamic Input

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EPDRIV1 Variable Time Steps

EPDRIV1 Lateral Inflow

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EPDRIV1 WQ Constituents

EPDRIV1 WQ Parameters

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EPDRIV1 WQ Parameters at XS

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Future Conditions

Population to increase by 50% within the next 30

yearsyears

Increase impervious cover in each subbasin by 50%.

Reduce die-off rate of all bacteria by 50%:

Higher wastewater flows.

Reduced settling in pools.

Higher nutrient concentrations.

Effects of BMPs

Existing Condition ResultsBastrop Bayou Watershed Protection Plan ‐ EPDRIV1 Modeling Results ‐ Existing Condition with 0.30/day Decay

Simulation Period: From 6/10/2009 To

Flow

(cfs)

TDS

(mg/L)

Bacteria*

(#/dL)

Avg Max Min Avg Max Min Geomean Max

Parameter

9/16/2010

BayouStream

Miles

EPDRIV1

XS

SWQM

Station

Contributing

Area

(sq. miles)

% Imp

Bastrop 19.56 1‐6 18502 15.2 23.4% 77.7 1,062 201 215 290 6.7 16.9 1,897 Enterococci



Bastrop 11.26 1‐19 18505 46.0 17.4% 116.3 2,334 200 234 1,778 1.9 13.4 1,491 Enterococci

Bastrop 7.65 1‐25 18507 196.5 10.3% 219.4 2,932 192 1,206 17,498 0.6 10.9 1,149 Enterococci



Austin 17 05 2‐29 18506 56 7 7 2% 63 7 1 819 201 309 586 21 9 52 0 3 301 E coliAustin 17.05 2‐29 18506 56.7 7.2% 63.7 1,819 201 309 586 21.9 52.0 3,301 E. coli

Austin 10.53 2‐40 none 101.6 7.8% 104.5 2,488 89 336 16,192 7.4 42.9 3,560 E. coli

Austin 5.91 2‐49 18048 128.4 8.8% 150.7 3,530 176 334 9,304 2.5 27.9 6,552 E. coli

Austin 0.00 2‐57 18507 144.1 8.3% 190.3 3,722 188 452 18,673 0.6 17.0 888 Enterococci

Flores 2.26 4‐28 18508 24.3 10.8% 38.1 1,020 201 297 404 3.1 40.0 8,990 E. coli

Brushy 5.65 3‐2 18509 15.8 15.0% 16.4 821 202 423 697 44.9 99.0 7,664 E. coli*Enterococci numbers were obtained by multiplying EPDRIV1 output E. coli numbers by a reduction factor of 0.28.

4/16/2012

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Proposed Condition ResultsBastrop Bayou Watershed Protection Plan ‐ EPDRIV1 Modeling Results ‐ Projected Condition with 0.15/day Decay


Flow

(cfs)

TDS

(mg/L)

Bacteria*

(#/dL)


Parameter

9/16/2010

Contributing

Area

(sq. miles)

% ImpBayouStream

Miles

EPDRIV1

XS

SWQM

Stationg g








( q )

p , , , ,

Austin 17.05 2‐29 18506 56.7 7.2% 68.1 1,827 201 307 586 35.3 78.9 4,178 E. coli

Austin 10.53 2‐40 none 101.6 7.8% 111.3 2,515 84 333 16,192 8.0 81.7 4,531 E. coli

Austin 5.91 2‐49 18048 128.4 8.8% 161.2 3,511 167 331 9,304 7.1 79.4 8,027 E. coli

Austin 0.00 2‐57 18507 144.1 8.3% 202.8 3,784 179 449 18,673 1.1 30.3 1,186 Enterococci

Flores 2.26 4‐28 18508 24.3 10.8% 40.8 1,039 201 296 404 3.5 51.2 9,155 E. coli

Brushy 5.65 3‐2 18509 15.8 15.0% 17.6 965 201 423 697 51.7 106.7 8,496 E. coli

*Enterococci numbers were obtained by multiplying EPDRIV1 output E. coli numbers by a reduction factor of 0.28.

4/16/2012

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4/16/2012

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Proposed Condition with BMPsBastrop Bayou Watershed Protection Plan ‐ EPDRIV1 Modeling Results ‐ Load Reduction Due to BMPs


Bacteria (#/dL)Projected Condition Load Reduction with BMPs* Parameter

%

Geomean

9/16/2010

BayouStream

Miles

EPDRIV1

XS

SWQM

Station

Contributing

Area

( )

%

ImpervousMin Geomean Max Min Geomean Max

Bastrop 19.56 1‐6 18502 15.2 23.4% 7.3 18.9 2,142 7.3 18.5 1,950 ‐1.9% EnterococciBastrop 16.67 1‐12 18503 29.8 22.5% 6.0 19.8 2,499 6.0 19.3 2,274 ‐2.3% EnterococciBastrop 14.81 1‐14 18504 34.6 20.8% 5.0 20.2 2,285 5.0 19.7 2,079 ‐2.5% EnterococciBastrop 11.26 1‐19 18505 46.0 17.4% 2.6 20.8 1,719 2.6 20.1 1,564 ‐3.5% EnterococciBastrop 7.65 1‐25 18507 196.5 10.3% 1.2 24.9 1,397 1.2 23.3 1,272 ‐6.3% EnterococciBastrop 6.09 1‐27 11475 203.6 10.1% 1.1 24.2 1,295 1.1 22.6 1,178 ‐6.7% EnterococciBastrop 0.00 1‐39 11474 217.4 10.5% 1.1 9.4 1,104 1.1 9.0 1,005 ‐3.8% Enterococci

ReducedMiles XS Station

(sq. miles)Impervous

Austin 17.05 2‐29 18506 56.7 7.2% 35.3 78.9 4,178 34.2 74.6 3,802 ‐5.5% E. coliAustin 10.53 2‐40 none 101.6 7.8% 8.0 81.7 4,531 8.0 76.3 4,123 ‐6.6% E. coliAustin 5.91 2‐49 18048 128.4 8.8% 7.1 79.4 8,027 6.9 73.7 7,305 ‐7.2% E. coliAustin 0.00 2‐57 18507 144.1 8.3% 1.1 30.3 1,186 1.1 28.2 1,079 ‐6.9% EnterococciFlores 2.26 4‐28 18508 24.3 10.8% 3.5 51.2 9,155 3.5 49.0 8,331 ‐4.2% E. coliBrushy 5.65 3‐2 18509 15.8 15.0% 51.7 106.7 8,496 49.2 100.5 7,732 ‐5.8% E. coli

*BMP Effects: 5% reduction on baseflow bacteria, 5% reduction on WWTP discharge bacteria, and 9% reduction on runoff bacteria.

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Daily Bacteria Loads from SELECT Model (Million MPN/day)

WWTP Wildlife Urban Runoff Dogs Cattle OSSF Total

2008 10,680 841,403 18,259,373 24,270,000 14,735,459 19,294,274 77,411,188

2010 11,748 838,882 19,138,940 26,172,000 14,735,459 20,880,795 81,777,824

2015 12,923 837,923 19,597,292 26,980,000 14,735,459 21,758,953 83,922,550

2020 14,216 835,530 20,847,989 28,932,000 14,735,459 23,591,083 88,956,277

2025 15 637 830 734 23 420 446 33 268 000 14 735 459 28 869 423 101 139 6992025 15,637 830,734 23,420,446 33,268,000 14,735,459 28,869,423 101,139,699

2030 17,201 825,630 26,309,130 38,044,000 14,735,459 33,391,106 113,322,525

2035 18,921 819,213 29,510,488 43,386,000 14,735,459 38,717,552 127,187,633

2040 20,813 814,208 32,274,813 48,102,000 14,735,459 43,543,825 139,491,118

Conclusions and Recommendations

Projected BMPs should improve and maintainProjected BMPs should improve and maintain water quality

Additional sampling/flow data needed for further analysis Clean Rivers Program continued to sample

Flow data source still needed

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Conclusions and Recommendations

EPD‐RIV1 model may be rerun if new SELECTEPD RIV1 model may be rerun if new SELECT results indicate significant potential loading change based on 2011 NLCD data. SELECT re‐run required by TCEQ for WPP approval Seeking to address land use change since project inception

Additional funding should be sought for implementation

Yu-Chun Suyc su@atkinsglobal com

Questions?Texas Water 2012San Antonio, TX

[email protected]

Justin [email protected]

April 12, 2012

1

DYNAMIC WATER QUALITY MODELING IN SUPPORT OF A WATERSHED PROTECTION PLAN FOR BASTROP BAYOU IN BRAZORIA COUNTY

Yu-Chun Su, Paul Jensen and Ka-Leung Lee; Atkins North America

1250 Wood Branch Park Drive Suite 300, Houston, Texas 77079

Justin Bower, Houston-Galveston Area Council

ABSTRACT

The population of the Bastrop Bayou Watershed in Brazoria County is projected to grow substantially in the coming years. Absent intervention, this growth is expected to be driven by and be similar to the pattern observed in suburban Houston. A risk assessment conducted by the Houston-Galveston Area Council (H-GAC) indicated a high likelihood of indicator bacteria problems similar to those in suburban Houston if no action were taken. To address this problem, the Texas Commission in Environmental Quality (TCEQ), H-GAC, and the Galveston Bay Estuary Program (GBEP) launched a Watershed Protection Plan (WPP) effort in 2006. One of the major goals of the WPP is to develop effective methods to address likely indicator bacteria problems along with developing public understanding and support for the methods to address the problems.

Building this public understanding and support typically requires quantitative information to explain the changes and the effects of actions. For this project a relatively new U.S. Environmental Protection Agency (EPA)-approved dynamic river model, EPDRiv1, was employed. This model is designed to simulate dynamic hydraulic and water quality conditions in rivers, with or without tidal influence, for a wide variety of water quality parameters, including indicator bacteria. For input from the watershed such as runoff and loading simulations, XP-SWMM model was used in this project.

This paper describes the project background, modeling process using EPDRiv1 focusing on its advantages and disadvantages relative to other dynamic model alternatives, and the modeling results in support of the WPP development. It also describes the basic processes that occur as a watershed develops yielding more frequent periods of higher runoff volume. Experience has shown that higher runoff volume with very high concentration of indicator bacteria tends to raise the average concentration and the likelihood of a stream not meeting criteria. The paper also describes the fundamental finding that addressing the negative effects of urbanization on water quality conditions will require some forms of Low Impact Development (LID) techniques as well as agricultural management practices reflecting the still-rural nature of the watershed. One of the major benefits of the WPP will be building the understanding of the need for LID and good agricultural runoff practices and some of the challenges involved in implementing these changes.

KEYWORDS

Watershed Protection Plan, Water Quality Models, Tidal Streams, EPDRiv1, Bastrop Bayou

2

INTRODUCTION

The Bastrop Bayou watershed is located entirely within Brazoria County, as shown on Figure 1. Ambient water quality monitoring began for the watershed under the Clean Rivers Program in August 2004. A risk assessment was completed for the watershed in June 2006. The risk assessment found that although the watershed is not currently on the 303(d) list, natural population growth in the area is a significant risk to water quality. Specifically by 2025, the watershed is expected to have a 50% growth in households. Because of the risk assessment, TCEQ, GBEP, and H-GAC began a Watershed Protection and Implementation Plan (WPIP) in 2006.

One of the goals of the WPIP is to develop a plan to ensure that the expected growth does not result in higher concentrations of indicator bacteria such that the waters would not attain water quality standards and thus go on the 303(d) list. This project provides bacteria modeling support for the WPIP (PBS&J, 2010). Modeling was performed with a relatively new model, EPDRiv1 (Georgia EPD, 2002) for the main channels, and XP-SWMM to represent runoff from the watersheds adjacent to each stream reach. The modeling included a calibration to the current level of bacteria in the streams and identified the effect of expected population growth on future bacteria levels if no action is taken. Finally, the model was used to show the benefits of management techniques that could help the area avoid increases in bacteria concentrations.

WATERSHED WATER QUALITY MODELING

LIDAR-based land elevation information was supplied by the H-GAC as the basis for developing subwatersheds. Figure 1 shows the 22 subwatersheds developed along with the land use characterization, also supplied by the H-GAC. The land use information is important because it provides a basis to estimate the amount of impervious cover that exists in the watersheds.

With the rain data available at the Angleton Airport, the various XP-SWMM subwatersheds were simulated for the period January 1, 2007 to September 16, 2010. These produced output files of flow that were fed into EPDRiv1.

STREAM WATER QUALITY MODELING

The EPDRiv1 model is based upon the CE-QUAL-RIV1 model developed by the U.S. Army Engineers Waterways Experiment Station (WES, now ERDC). The modifications to CE-QUAL-RIV1 to create EPDRiv1 were conducted by the Georgia Environmental Protection Division (EPD) of the Georgia Department of Natural Resources and the EPA.

EPDRiv1 is a one-dimensional (longitudinal) hydrodynamic and water quality model. It consists of two major components: a hydrodynamic and a water quality (WQ) component. Each of these parts is a separate computer program, with RIV1H being the hydrodynamic program and the RIV1Q being the WQ program. The hydrodynamic model component is typically executed first, and its output is saved to a file that is read by the WQ model component. EPDRiv1 can simulate

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Figure 1 – Bastrop Bayou Watershed and Delineated Subwatersheds

4

the interactions of 16 state variables (Georgia EPD, 2002), including water temperature, nitrogen species (or nitrogenous biochemical oxygen demand), phosphorus species, dissolved oxygen, carbonaceous oxygen demand (two types), algae, iron, manganese, coliform bacteria, and two arbitrary constituents. In addition, the model can simulate the impacts of macrophytes on dissolved oxygen and nutrient cycling. The model was designed for the simulation of dynamic conditions in streams.

One of the complexities of the model is the need to avoid a stream going dry. This is a significant constraint in smaller systems and should be addressed in future versions of the model, but for this application it is necessary to include a set of as small as possible background flows at the upstream end of each tributary.

MODEL DEVELOPMENT AND CALIBRATION

The first step in the stream modeling process is getting the flows simulated with acceptable accuracy. This is complicated by there being no flow measurement gage available in the system. The procedure followed was to select a portion of the Bastrop Bayou system that approximates the size and characteristics of the Chocolate Bayou gage 08078000, Chocolate Bayou near Alvin, with a drainage area of 87.7 square miles.

The best point of comparison is at the bottom of subbasins 1, 2, and 3, close to Surface Water Quality Monitoring Station (SWQM) 18506. The EPDRiv1 cross-section XS 2-29 is located at the spot, so the flow at that XS was exported from EPDRiv1. Using the GIS shapefiles, the total area of subbasins 1, 2, and 3 is 36,291.44 acres or 56.71 square miles. An adjustment factor of 56.71/87.7 = 0.65 was applied to the U.S. Geological Survey daily flows for comparison to the EPDRiv1 flows, which are at 30-minute intervals. The EPDRiv1 simulation period is from June 1, 2009 to September 16, 2010. However, the first 10-day is the initial spin-down time so data between June 11, 2009 and September 16, 2010, were used to compare the two data sets.

Figure 2 shows the flow comparison with both a linear and log scale. On the log presentation the effect of the background flows needed to avoid stream drying can be seen. Nevertheless, the background flows introduced are generally consistent with the low flow of Chocolate Bayou, except for the very dry period in summer 2009. Scaling the flow for the difference in watershed area, there is a reasonable level of agreement on both the total flow, a 2.3% difference, and the pattern of the individual rain events. Considering that there is some distance between the Chocolate Bayou watershed and the Angleton Airport rain gage, perfect agreement cannot be expected. Nevertheless, the level of agreement is considered to be acceptable.

The inflows to the EPDRiv1 model were supplied by inputs from the contributing subwatersheds, using the model XP-SWMM. The watershed inputs reflect runoff from rain followed by a declining limb composed of bank flow. With continued dry weather the watershed contribution will drop to zero and the only flow in the stream would be provided by upstream flows such as wastewater effluent.

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Figure 2 - Flow comparison with Chocolate Bayou Gage with linear and log scales

0

500

1,000

1,500

2,000

2,500

3,000

Jun 2009 Dec 2009 Jun 2010 Dec 2010

Flow

(cfs

)

EPD‐RIV1 Flow CalibrationEPDRIV1‐XS2‐29 Flow Adjusted Chocolate Bayou Flow

Total Volumes:Adjusted EPD‐RIV1: 2.55x109 ft3

Adjusted Chocolate Bayou: 2.49x109 ft3

1

10

100

1,000

10,000

Jun 2009 Dec 2009 Jun 2010 Dec 2010

Flow

(cfs

)

EPD‐RIV1 Flow CalibrationEPDRIV1‐XS2‐29 Flow Adjusted Chocolate Bayou Flow

Total Volumes:Adjusted EPD‐RIV1: 2.55x109 ft3

Adjusted Chocolate Bayou: 2.49x109 ft3

6

Typically the concentrations of parameters of interest in the runoff are represented by an Event Mean Concentration (EMC), which is the total load of a constituent during a runoff event divided by the total volume of runoff flow or a flow-weighted concentration. The typical pattern is for constituents such as the total suspended solids (TSS) and indicator bacteria to be highest at the early part of a runoff event (first flush) and decline as the rain ends and the flow drops. The EMC values will tend to vary greatly from event to event, being highest when rain intensity is highest and with a prolonged dry period prior to the storm event.

The City of Austin (2006) reported on a long-term program of runoff monitoring from small watersheds (<400 acres) with a single land use. Figures 3 and 4 show volume-weighted average EMC values for two common water quality parameters, TSS, and Total Nitrogen (TN) for 38 sites with impervious cover percentages ranging from near zero to near 100%. It can be seen that the concentration in active runoff is essentially independent of land use or impervious cover. Figures 5 and 6 show similar values for two indicator bacteria tests (Fecal Coliform [FC] and Fecal Strep). These have a similar pattern of having the average runoff concentration be essentially independent of impervious cover percentage, but they are also very high in relation to the water quality criterion for contact recreation, which for the FC test is 200 colonies per 100 mL (col/dL). The key point is that water samples collected during and immediately after runoff events will tend to be very high in indicator bacteria.

The volume-weighted average FC EMC in City of Austin (CoA) undeveloped watersheds was 16,206 col/dL. This concentration is an average over many runoff events, and the concentration during an event will include values much higher and lower than this amount. Jensen and Lee (2005) reviewed Surface Water Quality Monitoring (SQWM) data in the Houston area and found that in waters with little wastewater influence, the ratio of E. coli (EC) to FC was close to the ratio of the criteria (126/200 or 0.63). Applying that ratio, the average runoff water in undeveloped watersheds could be expected to have an EC concentration of 10,210 MPN/dL.

It is recognized that watersheds in Austin are not a perfect match for Bastrop Bayou, as they tend to have higher slopes and different soil types. Nevertheless, high bacteria concentrations in runoff are a widely observed phenomena, and the Austin data provide a useful means to quantify the process.

What does change with impervious cover is the quantity of runoff, or the runoff coefficient, defined as the runoff volume divided by the rainfall volume. This relation is shown on Figure 7. As a watershed urbanizes and takes on more impervious cover, the amount and frequency of runoff, with associated high bacteria levels, will increase. This can be expected to be reflected in higher bacteria levels in samples and higher geometric mean bacteria results at various stations.

For this project the tributary watersheds being represented are larger than those sampled by the CoA (2006), and hydrographs will thus tend to have longer declining limbs that have lower parameter concentrations. To represent the runoff concentrations for these hydrographs, we define the indicator bacteria concentration as a function of the intensity of the runoff at each hourly time step. This function is a linear interpolation between a maximum value as represented

7

Figure 3 - Suspended Solids Concentration in Runoff versus Impervious Cover

Figure 4 - Total Nitrogen Concentration in Runoff versus Impervious Cover

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

0.0 0.2 0.4 0.6 0.8 1.0

Impervious Cover

To

tal S

usp

end

ed S

olid

s (m

g/l)

VolumeWeightedMeans

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 0.2 0.4 0.6 0.8 1.0

Impervious Cover

To

tal

Nit

rog

en (

mg

/l)

VolumeWeightedMeans

8

Figure 5 – Fecal Coliform Concentration in Runoff versus Impervious Cover

Figure 6 – Fecal Strep Concentration in Runoff versus Impervious Cover

0

20,000

40,000

60,000

80,000

100,000

120,000

0.0 0.2 0.4 0.6 0.8 1.0

Impervious Cover

Fec

al C

ol. (

col/10

0ml)

VolumeWeightedMeans

0

50,000

100,000

150,000

200,000

250,000

300,000

0.0 0.2 0.4 0.6 0.8 1.0

Impervious Cover

Fec

al S

trep

. (c

ol/

100m

l)

VolumeWeightedMeans

9

Figure 7 - Runoff Coefficient versus Impervious Cover Percentage

in the CoA runoff sampling, and a minimum value that would be associated with bank flow at the end of the runoff event. The flow values are arbitrarily selected as 100 cubic feet per second (cfs)/square miles as sufficient to reflect a pure runoff condition and 0.1 cfs/square mile to represent a relatively clean water or bank flow condition. The flows from XP-SWMM are output to a spreadsheet for each subwatershed on an hourly basis. In the spreadsheet, each hourly flow is converted to a unit flow and the function applied to yield the indicator bacteria value for that time step.

This method of setting the indicator bacteria levels for watershed runoff requires some calibration. Because the study area includes streams with both Enterococci and E. coli criteria and the model has a single parameter in its internal calculations, the Enterococci geomeans are multiplied by 0.28 (35/126) to convert to EC for comparison purpose.

The initial values used for the dynamic concentration determination function were 10 MPN/dL for the lower concentration limit and 10,000 MPN/dL for the upper or pure runoff concentration limit. The 10,000 MPN/dL is the CoA value for FC in undeveloped watersheds, converted to EC. If specific flows are above or below the selected end points, these concentrations are held constant. These values were tested and adjusted during the calibration process. Ultimately, the lower end was adjusted from 10 to 50 MPN/dL. The resulting values for the calibration period are close to the EC criteria values, that is a reasonable approximation to the current conditions.

The total dissolved solids (TDS) calibration process was similar, except that there is little variation in runoff TDS concentration during a rain event. A runoff concentration of 200 milligram per liter (mg/L) was employed for runoff, and a value of 500 mg/L employed for

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wastewater flow. A background 10-cfs flow is needed to avoid drying and a 200 mg/L TDS is assigned to the flow. The main source of TDS in the system is upstream dispersion of salt from the downstream boundary. This boundary was set at 35,000 mg/L (35 parts per thousand [ppt], sea water salinity). The main calibration process involved adjusting the dispersion coefficient in the model to try to match observed levels of TDS in the more upstream tidally influenced stations.

Table 1 presents the calibration results for the main stations plus some locations at confluence locations. The bacteria values are geometric means over the calibration period with the 0.28 adjustment to reflect the stations with Enterococci criteria. Figures 8 and 9 show example TDS and bacteria model results, respectively, at a major monitoring station along Bastrop Bayou with data available during the calibration period. Figures 10 and 11 show similar results for an Austin Bayou station. There were frequent rains during the calibration period, and model bacteria levels respond rapidly. The model appears to cover the observed values reasonably well. While agreement is not perfect, the general pattern of salt intrusion up Bastrop Bayou during low flow periods is shown.

Table 1 - Calibration Results for the Main Stations

Based on having a reasonably good agreement for runoff flows as compared with the Chocolate Bayou gage, having bacteria levels that both match the criteria indicating a marginal level of attainment, and follow the pattern of being higher in runoff events, and the TDS levels approximating the observed values, the EPDRiv1 model was considered calibrated to the Bastrop Bayou watershed.

Bastrop Bayou Watershed Protection Plan ‐ EPDRIV1 Modeling Results ‐ Existing Condition with 0.30/day DecaySimulation Period: From 6/10/2009 To

Flow

(cfs)

TDS

(mg/L)

Bacteria*

(#/dL)






Bastrop 7.65 1‐25 18507 196.5 10.3% 219.4 2,932 192 1,206 17,498 0.6 10.9 1,149 Enterococci Conf. w/ Austin B.



Austin 17.05 2‐29 18506 56.7 7.2% 63.7 1,819 201 309 586 21.9 52.0 3,301 E. coli

Austin 10.53 2‐40 none 101.6 7.8% 104.5 2,488 89 336 16,192 7.4 42.9 3,560 E. coli Conf. w/ Flores B.

Austin 5.91 2‐49 18048 128.4 8.8% 150.7 3,530 176 334 9,304 2.5 27.9 6,552 E. coli Conf. w/ Brushy B.

Austin 0.00 2‐57 18507 144.1 8.3% 190.3 3,722 188 452 18,673 0.6 17.0 888 Enterococci

Flores 2.26 4‐28 18508 24.3 10.8% 38.1 1,020 201 297 404 3.1 40.0 8,990 E. coli

Brushy 5.65 3‐2 18509 15.8 15.0% 16.4 821 202 423 697 44.9 99.0 7,664 E. coli*Enterococci numbers were obtained by multiplying EPDRIV1 output E. coli numbers by a reduction factor of 0.28.

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Figure 8 – Example Bastrop Bayou TDS Calibration Result

Figure 9 – Example Bastrop Bayou Bacteria Calibration Results

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Figure 10 – Example Austin Bayou TDS Calibration Results

Figure 11 – Example Austin Bayou Bacteria Calibration Results

13

SIMULATION OF FUTURE CONDITIONS

With the model calibrated to conditions observed during 2009–2010, it was then used to assess the likely effects of growth in the watershed, assuming that growth followed a pattern observed historically. That pattern is for new developments to be constructed on land that is currently undeveloped, typically in agricultural use. This new development would have impervious cover and more efficient drainage systems, which would produce more runoff and allow less water to be absorbed in the ground. It also involves smaller wastewater treatment facilities, which would discharge all the time, altering the natural distribution of flows. The projected growth in the study area is for population to increase by 50% within the next 30 years. Modeling results were used to evaluate how these projected future developments could be expected to affect water quality.

First of all, any new development will require new and efficient drainage systems, which will cause rainfall runoff to occur more rapidly. While the bacteria concentration of runoff will be high regardless of the type of land use, a greater volume and speed of runoff will tend to make high bacteria concentrations in the stream a more common circumstance. Ultimately, this can be expected to increase the monitored bacteria levels.

To address this effect, the calibrated model is modified to increase the impervious cover in each subwatershed by 50%. The only exception is subbasin 22 where the impervious cover is from a water surface. These were left unchanged. With the higher impervious cover percentages being the only changes, the XP-SWMM model was re-run to yield runoff flows that are increased to some extent. The magnitude of the change was limited because the soil type in the area falls into hydrologic group D, with high clay content and relatively little infiltration. The model output is used to calculate the amount of increase in bacteria levels associated with the higher level of runoff due to higher impervious cover.

Secondly, the regulation and control of domestic wastewater discharges has been a central focus of the environmental engineering profession since the beginning of environmental awareness. As a consequence of this focus, the level of treatment now provided is arguably better than it ever has been in the past. The traditional quality issues of high oxygen demand, odors, and disease risk are now largely under control. Modern wastewater plants now provide a high level of oxidation of waste and chlorinate the effluent for disinfection. When chlorine residual levels specified in the permits are achieved, which is normally the case, detections of indicator bacteria are rare.

While the level of wastewater treatment is such that effluent dominated streams now support aquatic life uses, and a diverse aquatic ecosystem is now expected, they still can produce changes that are seen in monitoring of bacteria in effluent dominated streams. The entire process is not fully understood, but one mechanism appears to be an alteration of the stream itself. For example, a small stream that would normally be intermittent can be converted to a perennial stream with wastewater discharges. While this may have a positive effect on many forms of aquatic life, it can alter the stream bed from a varied geomorphology of pools, riffles and runs to one where there is a pilot channel that flows at a near constant pace. This provides less pooled area that can allow settling of particulate matter, which includes bacteria. In addition, when a stream is dominated by effluent, the concentrations of essential nutrients are greater than for

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natural flows. The net result for effluent-dominated streams such as exist in the Houston area, is for bacteria concentrations to be relatively high even in dry weather when the only flow is disinfected wastewater.

The EPDRiv1 model does not have the capability of directly simulating the process of settling in pools and the differences that come with reductions in settling opportunity along with higher nutrient concentrations. To approximate this effect, the die-off rate of all bacteria in the water is reduced by 50%. This change in die-off rate approximates the hydraulic effect of higher base flows with less settling during dry conditions, and the effect of higher runoff that would result in higher turbulent level in the bayous and therefore less settling during and after storm events.

A tabular presentation of the projected future flow, TDS, and bacteria concentrations for the system is shown in Table 2. An example plot of Bastrop Bayou existing and projected future bacteria levels is shown in Figures 12. Figure 13 shows a similar plot for the Austin Bayou system.

Table 2 - EPDRiv1 Modeling Results for Projected Condition

The projected increases in bacteria concentration are not overwhelming large, but large enough so that if no action were taken in a system that is already close to criteria exceedance, under the present assessment procedures, listing would be likely.

Bastrop Bayou Watershed Protection Plan ‐ EPDRIV1 Modeling Results ‐ Projected Condition with 0.15/day DecaySimulation Period: From 6/10/2009 To

Flow

(cfs)

TDS

(mg/L)

Bacteria*

(#/dL)






Bastrop 7.65 1‐25 18507 196.5 10.3% 232.9 3,382 189 1,170 17,498 1.2 24.9 1,397 Enterococci Conf. w/ Austin B.



Austin 17.05 2‐29 18506 56.7 7.2% 68.1 1,827 201 307 586 35.3 78.9 4,178 E. coli

Austin 10.53 2‐40 none 101.6 7.8% 111.3 2,515 84 333 16,192 8.0 81.7 4,531 E. coli Conf. w/ Flores B.

Austin 5.91 2‐49 18048 128.4 8.8% 161.2 3,511 167 331 9,304 7.1 79.4 8,027 E. coli Conf. w/ Brushy B.

Austin 0.00 2‐57 18507 144.1 8.3% 202.8 3,784 179 449 18,673 1.1 30.3 1,186 Enterococci

Flores 2.26 4‐28 18508 24.3 10.8% 40.8 1,039 201 296 404 3.5 51.2 9,155 E. coli

Brushy 5.65 3‐2 18509 15.8 15.0% 17.6 965 201 423 697 51.7 106.7 8,496 E. coli

*Enterococci numbers were obtained by multiplying EPDRIV1 output E. coli numbers by a reduction factor of 0.28.

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Figure 12 – Example Existing and Projected Bacteria Levels in Bastrop Bayou

Figure 13 – Example Existing and Projected Bacteria Levels in Austin Bayou

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But it is also important to recognize that with the substantial degree of variation in ambient bacteria levels due to the effect of runoff, whether a listing occurs or not is to a degree a matter of chance. If a sampling trip happens to occur during or immediately after a large rain, high bacteria levels can be expected. One or more samples with high bacteria levels will substantially influence a calculated geometric mean for a number of years. On the other hand, if sample trips happen to occur during an extended dry weather, relatively low bacteria levels can be expected. This variability is an inherent feature of bacteria monitoring data, and given a sufficient period of monitoring, large variations in criteria attainment should not be common. It is not recommended that monitoring programs be changed to avoid wet weather as this would make all parameters in the monitoring data unrepresentative of overall stream conditions.

ACTIONS TO AVOID LISTING

While modeling indicates that growth projected for the watershed will likely result in the bacteria criteria for water recreation to be exceeded, this does not have to occur. Actions are available that hold a strong promise to avoid increases in bacteria levels when development takes place. These are in three broad areas:

< Minimizing the effect of increases in runoff (with high bacteria concentrations) that would occur with development and increased impervious cover through the implementation of LID techniques;

< Avoiding the effects of future wastewater discharges in causing stream geomorphology effects such as channelization, by fostering wastewater reuse; and

< Improved management of animal and human waste through a program of Best Management Practices (BMPs).

All of these measures can be implemented in the coming years at a relatively modest cost. They don't require a significant cash outlay from a governmental unit. Rather, it would require giving an alternative pathway for developers to follow along with minor modifications of existing regulatory efforts.

LID can be encouraged by providing a pathway to permitting that makes LID a viable alternative to conventional drainage approaches. This may be as simple as setting up an alternative permit system for the county and cities that requires a developer to provide an engineering evaluation and certification that runoff flows are functionally equivalent to the predevelopment condition, and in exchange offers flexibility on drainage criteria and stormwater pond mandates.

Encouraging wastewater reuse can be as simple as a policy statement from local governments that indicates they will oppose wastewater permit applications to the TCEQ, which do not contain a program for effective wastewater reuse. It would not be necessary to be overly proscriptive on exactly how much or by what method reuse is to be achieved. A certainty of opposition from local government should be sufficient incentive to developers to include reuse in the overall design. Details could be worked out during project negotiations. The basic goal can be as simple as providing a plan and implementation procedures for all wastewater to be used during dry periods for both on-site and regional irrigation projects.

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The LID and wastewater reuse measures will primarily be effective in avoiding the increases in bacteria concentrations that experience in Houston has shown to follow urbanization. The net effect of these measures will only be apparent with the passage of time.

The WPP has also identified actions to address existing sources. Improved management of waste with BMPs would include several components. One is increased efforts to ensure that On-Site Sewage Facilities (OSSF) are properly managed, both in the permitting process for new facilities, and in operation and maintenance of existing units. The latter aspect would include public education on the importance of regular maintenance, and in identifying problem installations. Management of animal waste can be improved through programs addressing fencing to keep cattle out of streams and efforts to better manage pet wastes. These are cumulatively estimated in the WPP to have a small effect (e.g., 19% reduction) as they are implemented.

To assess these potential load reductions with the model, proportionate reductions in inflow bacteria concentrations were applied. Since the BMPs could affect both the low and high flow conditions, the overall 19% reduction was allocated as follows: baseflow-5%, Waste Water Treatment Plant (WWTP) inflow-5%, runoff-9%. Table 3 shows the modeling results of these load reductions in the evaluation period. Since the baseflow and WWTP loads are relatively small in the base condition, reductions in these loads has a small effect on the overall long-term geometric mean concentrations. The model predicts these load reductions from BMPs to have a 2% to 7% reduction in the geometric mean indicator bacteria concentrations relative to the current condition.

CONCLUSION

One part of the WPP process is to calculate changes in bacteria loads that are associated with population and land use change, and relate those to ambient stream bacteria concentrations taking flows and tidal prism dilution into account. The model results describe the effect of potential load reductions with BMPs. These model results implicitly incorporate the effect of the tidal prism dilution process.

The WPP process has also employed a model called SELECT, which produces bacteria loads as a function of land use. Some of these bacteria loads are from animals (cattle, horses, hogs, pets), which are applied in the watershed and would only affect stream conditions during heavy runoff events, and some are associated with lower flows. Table 4 presents a summary of the SELECT loads for the entire Bastrop Bayou system by year and by source. Assuming these loads go to the stream and are not just deposited on land, they can be related to a stream concentration by simply dividing the load with units of #/time, by an average stream flow with units of volume/time. Using a common time basis, a concentration, with units of #/dL can be calculated for comparison with criteria.

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Table 3 - EPDRIV1 Modeling Results – Load Reduction Due to BMPs

Table 4 - Daily Bacteria Loads from SELECT model for Bastrop Bayou Watershed

Year WWTP Wildlife Urban Runoff Dogs Cattle OSSF Total

2008 10,680 841,403 18,259,373 24,270,000 14,735,459 19,294,274 77,411,1882010 11,748 838,882 19,138,940 26,172,000 14,735,459 20,880,795 81,777,8242015 12,923 837,923 19,597,292 26,980,000 14,735,459 21,758,953 83,922,5502020 14,216 835,530 20,847,989 28,932,000 14,735,459 23,591,083 88,956,2772025 15,637 830,734 23,420,446 33,268,000 14,735,459 28,869,423 101,139,6992030 17,201 825,630 26,309,130 38,044,000 14,735,459 33,391,106 113,322,5252035 18,921 819,213 29,510,488 43,386,000 14,735,459 38,717,552 127,187,6332040 20,813 814,208 32,274,813 48,102,000 14,735,459 43,543,825 139,491,118Units are million MPN/day.

The average flow for the analysis period from Table 3 at the lower end of the basin, Station 11474, is 341.8 cfs. This is 9,680 L/sec or 83.6 E 9 dL/day. The bacteria load in 2008 from Table 4 is 77,411,188 million MPN/day. The average concentration of the specified indicator bacteria is thus 926 MPN/dL in 2008. Of course this is not necessarily realistic because it is based on deposition by cattle and dogs all showing up in the stream, when that is not necessarily correct. The primary use of the SELECT model is to capture changes in loads as land use changes. From Table 4, the total bacterial load is projected to nearly double by the year 2040. By this calculation, the average indicator bacteria concentration would be near 1,668 MPN/dL. Of course this value does not necessarily reflect bacteria that enter the stream.

Another limitation of this simple calculation is that it doesn’t take into account the effect of tidal mixing in the lower part of the basin. To help address this aspect, the model was modified by changing the downstream TDS boundary condition from 35,000 mg/L (35 ppt) to 0 mg/L, but keeping the tidal hydraulics at the boundary. The only TDS inputs to the model were the upstream values. Depending on the source (background, wastewater, and runoff) TDS

Bastrop Bayou Watershed Protection Plan ‐ EPDRIV1 Modeling Results ‐ Load Reduction Due to BMPs


Bacteria (#/dL)Projected Condition Load Reduction with BMPs* Parameter

Min Geomean Max Min Geomean Max

Bastrop 19.56 1‐6 18502 15.2 23.4% 7.3 18.9 2,142 7.3 18.5 1,950 ‐1.9% EnterococciBastrop 16.67 1‐12 18503 29.8 22.5% 6.0 19.8 2,499 6.0 19.3 2,274 ‐2.3% EnterococciBastrop 14.81 1‐14 18504 34.6 20.8% 5.0 20.2 2,285 5.0 19.7 2,079 ‐2.5% EnterococciBastrop 11.26 1‐19 18505 46.0 17.4% 2.6 20.8 1,719 2.6 20.1 1,564 ‐3.5% EnterococciBastrop 7.65 1‐25 18507 196.5 10.3% 1.2 24.9 1,397 1.2 23.3 1,272 ‐6.3% Enterococci Conf. w/ Austin B.Bastrop 6.09 1‐27 11475 203.6 10.1% 1.1 24.2 1,295 1.1 22.6 1,178 ‐6.7% EnterococciBastrop 0.00 1‐39 11474 217.4 10.5% 1.1 9.4 1,104 1.1 9.0 1,005 ‐3.8% EnterococciAustin 17.05 2‐29 18506 56.7 7.2% 35.3 78.9 4,178 34.2 74.6 3,802 ‐5.5% E. coliAustin 10.53 2‐40 none 101.6 7.8% 8.0 81.7 4,531 8.0 76.3 4,123 ‐6.6% E. coli Conf. w/ Flores B.Austin 5.91 2‐49 18048 128.4 8.8% 7.1 79.4 8,027 6.9 73.7 7,305 ‐7.2% E. coli Conf. w/ Brushy B.Austin 0.00 2‐57 18507 144.1 8.3% 1.1 30.3 1,186 1.1 28.2 1,079 ‐6.9% EnterococciFlores 2.26 4‐28 18508 24.3 10.8% 3.5 51.2 9,155 3.5 49.0 8,331 ‐4.2% E. coliBrushy 5.65 3‐2 18509 15.8 15.0% 51.7 106.7 8,496 49.2 100.5 7,732 ‐5.8% E. coli

*BMP Effects: 5% reduction on baseflow bacteria, 5% reduction on WWTP discharge bacteria, and 9% reduction on runoff bacteria.

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concentrations were in the 200–500 mg/L range. The simulation average for station 11474 with no boundary condition TDS was 85 mg/L. The lower value reflects the effect of flood tide water entering with 0 TDS and exiting with TDS from upstream. Next the model was operated with the tide turned off along with the 0 TDS downstream boundary. The tidal exchange process was thus taken out of action, and flow in the bayou was downstream only. Under this circumstance, the modeled average TDS at Station 11474 was 222 mg/L. The tidal mixing effect at this location is thus to reduce the concentration by a factor of 62%.

Concentration information is needed for comparison with criteria. However, it needs to be emphasized that there is little value in calculating a concentration from a load applied to a watershed divided by an average flow, and then taking a tidal dilution process into account in the lower basin. A much more meaningful and technically correct method to calculate a concentration is with a water quality model designed for that purpose, as has been done under this project.

REFERENCES

City of Austin (CoA). 2006. Stormwater Runoff Quality and Quantity from Small Watersheds in Austin, Texas. Watershed Protection Department, Water Quality Report Series COA-ERM/WQM 2006-1.

Georgia EPD. 2002. A Dynamic One-Dimensional Model of Hydrodynamics and Water Quality, EPDRiv1, Version 1.0.

Jensen, P.A., and K.L. Lee. 2005. Comparison of Fecal Coliform and E. Coli Indicators. Proceedings, Texas Water 2005.

PBS&J. 2010. Water Quality Modeling in Support of the Bastrop Bayou Watershed Protection Plan. Report Prepared for H-GAC, 3555 Timmons Lane, No. 120. Houston TX 77027.

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