U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2012-3026

March 2012

Methods for Estimating Concentrations and Loads of Selected 

Constituents in Tributaries to Lake Houston near Houston, Texas

Printed on recycled paper

Background

Houston, Texas, is the fourth largest city in the United States, with an estimated population of about 5.9 million people 

in 2009 (Texas State Data Center, 2011). Historically, groundwater has been the major source of supply for the City of 

Houston; however, development of groundwater resources has contributed to water-level declines and land-surface subsidence 

(Kasmarek and others, 2010). Lake Houston is a surface-water-supply reservoir for the City of Houston and currently (2011) 

supplies between 10 and 20 percent of the total source-water supply (City of Houston, 2011). As a result of regulations 

restricting groundwater withdrawals for the purpose of mitigating or arresting land-surface subsidence, Lake Houston is 

expected to become the primary source of water for the city in the future; the overall goal is to increase the use of surface 

water to no less than 80 percent of the total demand by 2030 (Harris-Galveston Subsidence District, 1999). Because Lake 

Houston is both a major source of potable water and a recreational resource for the Houston area, the possible effects of 

urbanization on the water quality of tributaries to Lake Houston are of interest to water managers and planners and require a 

comprehensive understanding of the factors that affect this environment.

Data Collection

Since December 2005, the U.S. Geological Survey 

(USGS), in cooperation with the City of Houston, Tex., has 

been assessing the quality of the water flowing into Lake 

Houston. Continuous in-stream water-quality monitors were 

installed at two USGS streamflow-gaging stations that represent 

watersheds contributing to Lake Houston (fig. 1) to provide 

real-time measurements of streamflow and other physical waterquality properties. USGS streamflow-gaging station 08068500 

Spring Creek near Spring, Tex., is in a primarily urban 

watershed, whereas USGS streamflow-gaging station 08070200 

East Fork San Jacinto River near New Caney, Tex., is in a 

primarily rural watershed. The watersheds of Spring Creek and 

East Fork San Jacinto River represent approximately 31 percent 

of the drainage area to Lake Houston. Additionally, discrete 

water-quality samples are periodically collected on these 

tributaries and analyzed for selected constituents of concern. 

For this analysis, 58 samples were collected at the Spring Creek 

site, and 51 samples were collected at the East Fork San Jacinto 

River site. Hydrologic conditions in the Spring Creek and East 

Fork San Jacinto River sites vary and might affect chemical 

constituent concentrations, so discrete water-quality samples 

were collected over a wide range of streamflow conditions 

(fig. 2). All samples were collected and processed as outlined in 

the USGS “National Field Manual for the Collection of WaterQuality Data” (U.S. Geological Survey, variously dated).

Although periodic discrete sampling is useful for many 

monitoring purposes, it does not provide real-time data on 

which to base operational decisions to prevent degradation or 

treat the surface-water-supply reservoir for the city. Real-time 

A continuous water-quality monitor is deployed within a “hangingwell” at U.S. Geological Survey streamflow-gaging 08070200 East 

Fork San Jacinto River near New Caney, Texas.

data are needed to provide water suppliers with timely 

information to use in adjusting water-treatment strategies. 

Data from the discrete water-quality samples collected during 

2005–9, in conjunction with the real-time streamflow data and 

data from the continuous in-stream water-quality monitors,

provided the basis for developing regression equations for the 

estimation of concentrations of water-quality constituents of 

these source watersheds to Lake Houston. 
Lake

Houston

Cypress Creek

Spring Creek

West Fork

San Jacinto River

Caney

Creek

Peach

Creek

East Fork

San Jacinto

River 

Luce

Bayou

TEXAS

Lake Houston

watershed

 Lake

Houston

Lake

 Conroe

WALLER

GRIMES

WALKER

MONTGOMERY

HARRIS

SAN JACINTO

LIBERTY

EXPLANATION

Subbasin boundary

Subwatershed boundary

U.S. Geological Survey

streamflow-gaging station

and number

08068500

Base from Houston-Galveston Area Council, 2004

Lake Houston watershed, 1:602,933

Universal Transverse Mercator projection, Zone 15

95°30'

95°00'

30°00'

30°30'

08068500

08070200

WESTERN

SUBBASIN

EASTERN

SUBBASIN

Houston

Location of study area and Houston, Texas

Study area

08068500

20 MILES

20 KILOMETERS

0

0

10

10

Figure 1. Lake Houston watershed and tributary subwatersheds and location of U.S. Geological Survey streamflow-gaging stations 08068500 

Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Tex.

Example of a high-flow event at U.S. Geological Survey 

streamflow-gaging 08070200 East Fork San Jacinto River near New 

Caney, Texas.

View of Spring Creek looking upstream from U.S. Geological 

Survey streamflow-gaging station 08068500 Spring Creek near 

Spring, Texas.
Nonexceedance probability

Streamflow, in cubic feet per second

0.1 1 2 5 10 20 30 50 70 80 90 95 98 99 99.9

10

20

30

50

70

100

200

300

500

700

1,000

2,000

3,000

5,000

7,000

10,000

Flow-duration curve of daily mean streamflow for October 1998–September 2010

Concurrent streamflow with water-quality sample for December 2005–October 2009

EXPLANATION

Nonexceedance probability

Streamflow, in cubic feet per second

0.1 1 2 5 10 20 30 50 70 80 90 95 98 99 99.9

10

20

30

50

70

100

200

300

500

700

1,000

2,000

3,000

5,000

7,000

10,000

Flow-duration curve of daily mean streamflow for October 1998–September 2010

Concurrent streamflow with water-quality sample for December 2005–October 2009

EXPLANATION

A

B

Figure 2. Flow duration curve and corresponding discrete water-quality samples, (A) Spring Creek near Spring, Texas, and 

(B) East Fork San Jacinto River near New Caney, Texas.

Nonexceedance probability

Flow-duration curve of daily mean streamflow for October 1998–September 2010

Concurrent streamflow with water-quality sample for December 2005–October 2009

EXPLANATION

Nonexceedance probability

Streamflow, in cubic feet per second

0.1 1 2 5 10 20 30 50 70 80 90 95 98 99 99.9

10

20

30

50

70

100

200

300

500

700

1,000

2,000

3,000

5,000

7,000

10,000

Flow-duration curve of daily mean streamflow for October 1998–September 2010

Concurrent streamflow with water-quality sample for December 2005–October 2009

EXPLANATION

B

Figure 2. Flow duration curve and corresponding discrete water-quality samples, (A) Spring Creek near Spring, Texas, and 

(B) East Fork San Jacinto River near New Caney, Texas.

Figure 2. Flow duration curve and corresponding discrete water-quality samples, (A) Spring Creek near Spring, Texas, and (B) East Fork San 

Jacinto River near New Caney, Tex.

Regression Analysis

Explanatory variables provide data that serve as potential 

surrogates for estimating concentrations of selected constituents 

in a site-specific statistical regression analysis. The potential 

explanatory variables included discharge (streamflow), specific 

conductance, pH, water temperature, turbidity, dissolved 

oxygen, and time (to account for seasonal variations inherent in 

some water-quality data). The response variables (the selected 

constituents) at each site were nitrite plus nitrate nitrogen, total 

phosphorus, total organic carbon, Escherichia coli, atrazine, 

and suspended sediment. Statistical regression facilitates 

corresponding estimates of uncertainty in the form of prediction 

intervals. Each regression equation potentially can be used 

to estimate concentrations of a given constituent in real time. 

The diagnostics used as indicators of general model reliability 

include the adjusted R-squared, the residual standard error, 

residual plots, and p-values. 

In conjunction with estimated concentrations, constituent 

loads can be estimated by multiplying the estimated 

concentration by the corresponding streamflow and by 

applying the appropriate unit conversion factor. 

How Are the Data Used?

Results from these regression models can be used to better 

understand fluctuations of concentration and loads during 

changing seasons and flow conditions and to assess waterquality conditions relative to total maximum daily load goals 

and water-quality standards. The information is also useful for 

evaluating loading characteristics, such as range and variability, 

and for determining effectiveness of best management 

practices (Rasmussen and others, 2008). With near real-time 

water-quality data for the tributaries (every 15 minutes), water 

managers and planners have tools to help identify potential 

effects of tributary inflows on the water quality of Lake Houston 

with sufficient alert time and can adjust operations at drinkingwater plants accordingly. In addition, over time the results 

of tributary water-quality monitoring will contribute to the 

broader understanding of watershed influences and the effects 

of those influences on Lake Houston as a drinking water and 

recreational resource.

Conclusions

• Continuously monitored streamflow and water-quality 

properties, in conjunction with regression models using data 

that serve as surrogates for other water-quality properties and 

constituents, can be used to estimate loads and frequency of 

occurrence for constituents lacking a continuous record in 

urban and rural watersheds.

• The real-time water-quality data provide information necessary for water managers and planners to prevent degradation 

or to adjust water-treatment strategies. 

• The regression equations developed by using these methods 

are specific to the location where continuous monitoring and 

discrete sampling occurs.

• The general methods that were developed and documented 

could be applied to other tributaries to Lake Houston or 

scaled and modified to fit other project needs.

A B
By Michael T. Lee

For additional information, contact 

Director, USGS Texas Water Science Center 

http://tx.usgs.gov/ 

gs-w-txpublic-info@usgs.gov

Cited References

City of Houston, 2011, Public Works and Engineering Drinking 

Water Operations: accessed March 2011, at http://www.

publicworks.houstontx.gov/utilities/drinkingwater.html.

Harris-Galveston Subsidence District, 1999 [amended 2001], 

District regulatory plan: accessed April 22, 2008, at http://www.

hgsubsidence.org/assets/pdfdocuments/HGRegPlan.pdf.

Kasmarek, M.C., Johnson, M.R., and Ramage, J.K., 2010, Waterlevel altitudes 2010 and water-level changes in the Chicot, 

Evangeline, and Jasper aquifers and compaction 1973–2009 in the 

Chicot and Evangeline aquifers, Houston-Galveston region, Texas: 

U.S. Geological Survey Scientific Investigations Map 3138, 17 p., 

16 sheets, 1 appendix.

Rasmussen, T.J., Lee, C.J., and Ziegler, A.C., 2008, Estimation 

of constituent concentrations, loads, and yields in streams of 

Johnson County, northeast Kansas, using continuous waterquality monitoring and regression models, October 2002 through 

December 2006: U.S. Geological Survey Scientific Investigations 

Report 2008–5014, 103 p. 

As part of the USGS 

mission to describe water 

resources, the USGS provides 

access to the real-time 

water-quality data from this 

study (including streamflow, 

water temperature, specific 

conductance, pH, dissolved 

oxygen, and turbidity) through 

WaterQualityWatch: http://

waterwatch.usgs.gov/wqwatch/.

Data are available for more 

than 1,300 sites—from streams 

with watersheds as small 

as a few square miles to the 

watershed of the Mississippi 

River, which is more than 

1,000,000 square miles as it 

enters the Gulf of Mexico. 

Additionally, the 

USGS includes the output 

of regression equations 

developed in this study on 

the National Real-Time Water 

Quality (NRTWQ) Web site: 

http://nrtwq.usgs.gov. The 

NRTWQ Web site conveniently 

provides estimated 

concentrations and loads for 

constituents, uncertainty values 

and probabilities for exceeding 

drinking water or recreational 

criteria, flow-duration curves, 

and historical hourly in-stream 

sensor measurements.

08068500 Spring Creek near Spring, TX

Computed diss. nitrate + nitrite

Year 2011 All

hourly

Discharge

Measured or computed water-quality constituent

90-percent prediction interval for computed value

Value obtained from discrete sampling and analysis

Load calculated using laboratory analysis and discharge

Water-quality criteria

EXPLANATION

Texas State Data Center, 2011, 2009 total population estimates 

for Texas metropolitan statistical areas: Office of the 

State Demographer, Population Estimates and Projection 

Program, accessed March 16, 2011, at http://txsdc.utsa.edu/

tpepp/2009_txpopest_msa.php.

U.S. Geological Survey, variously dated, National field manual 

for the collection of water-quality data: U.S. Geological Survey 

Techniques of Water-Resources Investigations, book 9, chap. 

A1–A9 [variously paged]. (Also available at http://pubs.water.

usgs.gov/twri9A.)

Real-Time Water-Quality Data