Dynamic water quality modeling in support of a ... - PBS&J - Atkins

Texas Water 2012 

conference program 

April 10 – 13 

Henry B. Gonzalez Convention Center 

San Antonio 

2012 Conference Schedule....4-5 

Conference Highlights..........8-9 

Competitions........................24-25 

Exhibitor 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...12-19 

WEAT.........................................54-55

TECHNICAL SESSIONS • THURSDAY MORNING, APRIL 12 

ROOM 203-B 

Moderators 

Roger Schenk 

CDM Smith 

Water Conservation 

ROOM 204-A 

Moderators 

Katie McCain 

Wachs Utility Service 

Dean Sharp 

Water Resources Management 

Utility Management 

& Workforce Issues 

ROOM 204-B 

Moderators 

Tom Ray 

Lockwood Andrews and Newnam 

Watershed Management 

ROOM 101-A/B 

Moderators 

Steve Fife 

Baytown Water Authority 

Operator Forum 

Demand Management 

Strategies for Tarrant 

Regional Water District 

Brian McDonald 

Alan Plummer Associates 

Mark Olson 

Tarrant Regional Water District 

Removing the Blinders: Using 

Dynamic Modeling to Promote 

Financial Sustainability 

Jennifer Ivey 

Red Oak Consulting 

Skipper Shook 

City of Fort Worth 

Watershed Management 

To Address Nutrient 

and Sediment Issues 

Paul Jensen 

Atkins North America 

Ka-Leung Lee 


David Buzan 


Field Testing Addresses 

Operations and 

Budget Challenges 

Kathy Fretwell 

Kennedy Jenks Consultants 

Aurora 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 Wen 

Dallas Water Utilities 

Getting It Right: A Study of 

Cost of Service Wastewater 

Treatment Allocations 

Skipper Shook 

City of Fort Worth 

Jennifer 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 Byars 

City of Austin 

Comparing Solid State 

Water Meters to Positive 

Displacement Meters 

in Residential Services 

Craig Hannah 

Johnson Controls 

10:00 - 10:30 am 

Untapped Potential: 

The Effectiveness of 

Municipally-Driven ICI 

Water Audit Programs 

Micah Reed 

City of Fort Worth Water Department 

Decision Making in the Face 

of Risk and Uncertainty 

Jeffrey Edmonds 

URS 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 


Xin He 


Ka-Leung Lee 


Cost Effective 

Automated Dead End 

Water Main Flushing 

Aaron Russell 

City of Burleson 

10:30 - 11:00 am 

When the Rain Stopped: 

Two Cities’ Pursuit of Water 

During Historic Drought 

Conditions in Central Texas 

Aaron Archer 

HDR Engineering 

Kenneth Wheeler 

City of Cedar Park 

Wayne Watts 

City of Leander 

Hiring Texas Veterans 

Bryan Daye 

Texas Veterans Commission 

Effects of the 

2011 Drought in Texas 

Karl Winters 

U.S. Geological Survey 

Gregory Stanton 

U.S. Geological Survey 

Water Supply 

Management Using 

AMI Technology 

Bernard Dunham 

Delta Engineering Sales 

11:00 - 11:30 am 

North Texas Demonstrates 

Correlation Between 

Public Education and 

Conservation Behavior 

Denise Hickey 

North Texas Municipal Water District 

Valerie Davis 

EnviroMedia Social Marketing 

Creating Your Own Workforce: 

City of Waco Partnership 

for Water Education 

Teresa Bryant 

City of Waco 

Jonathon Echols 

City of Waco 

Dynamic Water Quality Modeling 

in Support of a Watershed 

Protection Plan for Bastrop 

Bayou in Brazoria County 

Yu-Chun Su 


Paul Jensen 


Ka-Leung Lee 


Justin Bower 

Houston-Galveston Area Council 

Operators and Engineers 

Working Together Provides 

for Project Success 

Jeff Sober 

Carollo Engineers 

John Bennett 

Trinity River Authority of Texas 

11:30 am - Noon 

www.texas-water.com 15

4/16/2012 

Dynamic Water Quality Modeling in 

Support of a Watershed Protection 

Plan for Bastrop Bayou 

Atkins: Yu‐Chun Su, PhD,PE,CFM,CPSWQ,CPESC 

Paul Jensen, PhD,PE,BCEE 

Ka‐Leung Lee, PhD,PE,CFM,CPSWQ 

H‐GAC: Justin Bower 

The Bastrop 

Bayou Watershed 

• Located on Upper 

Texas 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), and tributaries: tib t i 

Brushy Creek (1105E) 

Flores Bayou (1105A) 

Austin Bayou (1105B/C 

• Pi 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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• 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 


• 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 Goals 

The goals of the EPDRIV‐1 

modeling effort were to 

evaluate: 

• 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. 

Subbasins 

and 

Monitoring 

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 

Funded by US EPA, Region IV 

Riv1H and Riv1Q by Robert Olson, NRE, Inc. 

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EPDRIV1 Setup 

Hydrodynamic Input File 

WQ Input File 

Lateral Inflows 

Withdrawals/Diversions 

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 

years 

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 Results 

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

Simulation Period: From 6/10/2009 To 9/16/2010 

Flow 

(cfs) 

TDS 

(mg/L) 

Bacteria* 

(#/dL) 

Bayou Stream 

Miles 

EPDRIV1 

XS 

Contributing 

SWQM 

Area 

Station 

(sq. miles) 

% Imp 

Avg Max Min Avg Max Min Geomean Max 

Parameter 

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 72% 7.2% 63.7 1,819 201 309 586 21.9 52.0 3301 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. 

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Proposed Condition Results 

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


Bayou Stream 

Miles 

EPDRIV1 

XS 

SWQM 

Station 


Area % Imp 

(sq. (q miles) 

Flow 

(cfs) 

TDS 

(mg/L) 

Bacteria* 

(#/dL) 


Parameter 








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 


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Proposed Condition with BMPs 

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


Bacteria (#/dL) 

% 

Bayou Stream 


EPDRIV1 SWQM 

% 

Area 

Projected Condition Load Reduction with BMPs* Geomean 

Miles XS Station Impervous 

(sq. miles) Min Geomean Max Min Geomean Max Reduced 

Parameter 

Bastrop 19.56 1‐6 18502 15.2 23.4% 7.3 18.9 2,142 7.3 18.5 1,950 ‐1.9% Enterococci 







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. coli 

Austin 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 


Austin 0.00 2‐57 18507 144.1 8.3% 1.1 30.3 1,186 1.1 28.2 1,079 ‐6.9% Enterococci 

Flores 2.26 4‐28 18508 24.3 10.8% 3.5 51.2 9,155 3.5 49.0 8,331 ‐4.2% E. coli 

Brushy 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 23,420,446 446 33,268,000 14,735,459 459 28,869,423869 101,139,699139 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 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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4/16/2012 

Conclusions and Recommendations 

• EPD‐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 

Questions? 

Yu-Chun Su 

yc.su@atkinsglobal.com 

com 

281-529-4195 

Texas Water 2012 

San Antonio, TX 

April 12, 2012 

Justin Bower 

Justin.bower@h-gac.com 

713-499-6653 

20

DYNAMIC WATER QUALITY MODELING IN SUPPORT OF A WATERSHED 

PROTECTION PLAN FOR BASTROP BAYOU IN BRAZORIA COUNTY 

ABSTRACT 

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 

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 

1

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 

2

Figure 1 – Bastrop Bayou Watershed and Delineated Subwatersheds 

3

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. 

4

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

3,000 

2,500 

EPD‐RIV1 Flow Calibration 

EPDRIV1‐XS2‐29 Flow Adjusted Chocolate Bayou Flow 

Total Volumes: 

Adjusted EPD‐RIV1: 2.55x10 9 ft 3 

Adjusted Chocolate Bayou: 2.49x10 9 ft 3 

2,000 

Flow (cfs) 

1,500 

1,000 

500 

0 

Jun 2009 Dec 2009 Jun 2010 Dec 2010 

10,000 

1,000 

EPD‐RIV1 Flow Calibration 

EPDRIV1‐XS2‐29 Flow Adjusted Chocolate Bayou Flow 

Total Volumes: 

Adjusted EPD‐RIV1: 2.55x10 9 ft 3 

Adjusted Chocolate Bayou: 2.49x10 9 ft 3 

Flow (cfs) 

100 

10 

1 

Jun 2009 Dec 2009 Jun 2010 Dec 2010 

5

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 (

Figure 3 - Suspended Solids Concentration in Runoff versus Impervious Cover 

450.0 

400.0 

Total Suspended Solids (mg/l) 

350.0 

300.0 

250.0 

200.0 

150.0 

100.0 

50.0 

0.0 

0.0 0.2 0.4 0.6 0.8 1.0 

Impervious Cover 

Volume 

Weighted 

Means 

Figure 4 - Total Nitrogen Concentration in Runoff versus Impervious Cover 

3.5 

3.0 

Total Nitrogen (mg/l) 

2.5 

2.0 

1.5 

1.0 

0.5 

Volume 

Weighted 

Means 

0.0 

0.0 0.2 0.4 0.6 0.8 1.0 


7

Figure 5 – Fecal Coliform Concentration in Runoff versus Impervious Cover 

120,000 

100,000 

Fecal Col. (col/100ml) 

80,000 

60,000 

40,000 

20,000 

Volume 

Weighted 

Means 

0 

0.0 0.2 0.4 0.6 0.8 1.0 


Figure 6 – Fecal Strep Concentration in Runoff versus Impervious Cover 

300,000 

250,000 

Fecal Strep. (col/100ml) 

200,000 

150,000 

100,000 

50,000 

Volume 

Weighted 

Means 

0 

0.0 0.2 0.4 0.6 0.8 1.0 


8

Figure 7 - Runoff Coefficient versus Impervious Cover Percentage 

1.0 

Runoff Coefficien 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

Linear Model for All Watersheds 

95% Confidence Level Lines 

All Watersheds 

Model from CRWR Report 

0.0 

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 


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 

9

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 

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


Flow 

(cfs) 

TDS 

(mg/L) 

Bacteria* 

(#/dL) 

Bayou Stream 

Miles 

EPDRIV1 

XS 

SWQM 

Station 


Area 

(sq. miles) 

% Imp 


Parameter 

Remarks 





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 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 


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. 

10

Figure 8 – Example Bastrop Bayou TDS Calibration Result 

Figure 9 – Example Bastrop Bayou Bacteria Calibration Results 

11

Figure 10 – Example Austin Bayou TDS Calibration Results 

Figure 11 – Example Austin Bayou Bacteria Calibration Results 

12

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 

13

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 

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


Flow 

(cfs) 

TDS 

(mg/L) 

Bacteria* 

(#/dL) 

Bayou Stream 

Miles 

EPDRIV1 

XS 

SWQM 

Station 


Area 

(sq. miles) 

% Imp 


Parameter 

Remarks 





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 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 


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. 

14

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

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

15

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. 

16

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. 

17

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

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


Bacteria (#/dL) 

% 

Bayou Stream 


EPDRIV1 SWQM 

% 

Area 

Projected Condition Load Reduction with BMPs* Geomean 

Miles XS Station 

Impervous 

(sq. miles) 

Min Geomean Max Min Geomean Max Reduced 

Parameter Remarks 





Bastrop 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. 




Austin 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% Enterococci 

Flores 2.26 4‐28 18508 24.3 10.8% 3.5 51.2 9,155 3.5 49.0 8,331 ‐4.2% E. coli 

Brushy 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. 

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 77,411,188 

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 28,869,423 101,139,699 

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 

Units 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 

18

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. 

19

Dynamic water quality modeling in support of a ... - PBS&J - Atkins

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