U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5171

Prepared in cooperation with the Houston-Galveston Area Council and 

the Texas Commission on Environmental Quality under the authorization 

of the Texas Clean Rivers Act and applicable Federal law

Streamflow and Water-Quality Properties in the West Fork 

San Jacinto River Basin and Regression Models to Estimate 

Real-Time Suspended-Sediment and Total Suspended-Solids 

Concentrations and Loads in the West Fork San Jacinto River 

in the Vicinity of Conroe, Texas, July 2008–August 2009
Front cover:

Top, Monitor well at station 08067650 West Fork San Jacinto River below Lake Conroe near Conroe, 

Texas, Sept. 5, 2008.

Middle, Downstream view of West Fork San Jacinto River at station 08068000 West Fork San Jacinto 

River near Conroe, Texas, Oct. 24, 2008.

Bottom, Downstream view of West Fork San Jacinto River at station 08067650 West Fork San Jacinto 

River below Lake Conroe near Conroe, Texas, Sept. 5, 2008.

Back cover:

Top, Upstream view of West Fork San Jacinto River at station 08068000, Sept. 5, 2008.

Middle, Downstream view of West Fork San Jacinto River at station 08068000, Aug. 27, 2009.

Bottom, Gage and monitor well at station 08068000, Sept. 5, 2008.

All photographs by Lee J. Bodkin, U.S. Geological Survey. 
Streamflow and Water-Quality Properties 

in the West Fork San Jacinto River Basin 

and Regression Models to Estimate RealTime Suspended-Sediment and Total 

Suspended-Solids Concentrations and 

Loads in the West Fork San Jacinto River 

in the Vicinity of Conroe, Texas, July 2008–

August 2009 

By Lee J. Bodkin and Jeannette H. Oden

Prepared in cooperation with the Houston-Galveston Area Council and 

the Texas Commission on Environmental Quality under the authorization 

of the Texas Clean Rivers Act and applicable Federal law

Scientific Investigations Report 2010–5171

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

This and other USGS information products are available at http://store.usgs.gov/

U.S. Geological Survey

Box 25286, Denver Federal Center

Denver, CO 80225

To learn about the USGS and its information products visit http://www.usgs.gov/

1-888-ASK-USGS

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Bodkin, L.J., and Oden, J.H., 2010, Streamflow and water-quality properties in the West Fork San Jacinto River Basin 

and regression models to estimate real-time suspended-sediment and total suspended-solids concentrations and 

loads in the West Fork San Jacinto River in the vicinity of Conroe, Texas, July 2008–August 2009: U.S. Geological 

Survey Scientific Investigations Report 2010–5171, 35 p. 
iii

Acknowledgments

The authors thank J.W. East, U.S. Geological Survey, for assisting with development of 

the Quality Assurance Project Plan and data management throughout this project. The authors 

also thank M.T. Lee and D.K. Coffman, U.S. Geological Survey, for their comments and 

suggestions, which improved the report.
Blank Page
v

Contents

Abstract ..........................................................................................................................................................1

Introduction ....................................................................................................................................................2

Purpose and Scope .............................................................................................................................2

Description of Study Area ..................................................................................................................3

Data-Collection and Regression Methods ...............................................................................................4

Streamflow Measurements ...............................................................................................................5

Continuous Measurements of Water-Quality Properties ..............................................................6

Discrete Water-Quality Sampling ......................................................................................................7

Analytical Methods ....................................................................................................................9

Quality Assurance .......................................................................................................................9

Regression Methods .........................................................................................................................12

Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin ...................12

Regression Models to Estimate Real-Time Suspended-Sediment and Total SuspendedSolids Concentrations and Loads ...............................................................................................17

Multiple Linear Regression Model Results ...................................................................................20

Evaluation of Regression Models ...................................................................................................21

Suspended-Sediment and Total Suspended-Solids Concentration and Load 

Computation ..........................................................................................................................23

Summary .......................................................................................................................................................27

References Cited .........................................................................................................................................28

Appendix 1. S–PLUS Output of Regression Model Development of Turbidity, Streamflow, 

Suspended-Sediment Concentration, and Total Suspended-Solids 

Concentration for Two Main Monitoring Sites (Stations 08067650 and 

08068000) in the West Fork San Jacinto River Basin, Texas, July 2008–

August 2009 ........................................................................................................................31

Figures

1–2. Maps showing: 

1. Location of monitoring sites in the West Fork San Jacinto River Basin, Texas, 

July 2008–August 2009 .......................................................................................................3

2. Land use in the San Jacinto River Basin, Texas, July 2008–August 2009 .................5

3. Graph showing total monthly rainfall (July 2008–August 2009) and mean monthly 

rainfall (1983–2009) at National Weather Service station 411956 in Conroe, Texas .........6

4. Hydrographs showing streamflow and sample collection at station (A) 08067650 

West Fork San Jacinto River below Lake Conroe near Conroe, Texas, and 

(B) 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–August 

2009 ................................................................................................................................................8

5–11. Graphs showing:

5. Comparison of historical mean monthly streamflow (1973–2009) and mean 

monthly streamflow (July 2008–August 2009) at station 08068000 West Fork 

San Jacinto River near Conroe, Texas ..........................................................................13
vi

6. Relation between suspended-sediment concentration (in linear space) 

and (A) turbidity and (B) streamflow and between suspended-sediment 

concentration (in log10 space) and (C) turbidity and (D) streamflow for 

station 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–

August 2009 ........................................................................................................................18

7. Relation between total suspended-solids concentration (in linear space) 

and (A) turbidity and (B) streamflow and between total suspended-solids 

concentration (in log10 space) and (C) turbidity and (D) streamflow for 

station 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–

August 2009 ........................................................................................................................19

8. Relation between (A) streamflow and turbidity (in linear space) and 

(B) streamflow and turbidity (in log10 space) for station 08068000 West Fork 

San Jacinto River near Conroe, Texas, July 2008–August 2009 ...............................20

9. Relation between (A) measured or estimated suspended-sediment 

concentration and turbidity and (B) measured or estimated total suspendedsolids concentration and turbidity at station 08068000 West Fork San Jacinto 

River near Conroe, Texas, July 2008–August 2009 ......................................................22

10. Estimated suspended-sediment concentrations (A) probability plot 

correlation coefficient and (B) residual plot; and estimated total suspendedsolids concentrations (C) probability plot correlation coefficient and 

(D) residual plot for multiple linear regression models developed for station 

08067650 West Fork San Jacinto River below Lake Conroe near Conroe, 

Texas, July 2008–August 2009 .........................................................................................24

11. Estimated suspended-sediment concentrations (A) probability plot 

correlation coefficient and (B) residual plot; and estimated total suspendedsolids concentrations (C) probability plot correlation coefficient and 

(D) residual plot for multiple linear regression models developed for station 

08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–

August 2009 ........................................................................................................................25

Tables

1. Description of monitoring sites in the West Fork San Jacinto River Basin, Texas, 

July 2008–August 2009 ................................................................................................................4

2. Water-quality data in samples from two main monitoring sites (stations 08067650 

and 08068000) in the West Fork San Jacinto River Basin, Texas, July 2008–August 

2009 ..............................................................................................................................................10

3. Suspended-sediment and total suspended-solids concentrations in field and 

equipment blank samples from sites in the West Fork San Jacinto River Basin, 

Texas, July 2008–August 2009 ..................................................................................................13

4. Water-quality data in samples from five additional monitoring sites (stations 

08067652, 08067653, 08067657, 08067800, and 08067900) in the West Fork San 

Jacinto River Basin, Texas, July 2008–August 2009 ............................................................15

5. Summary statistics for selected water-quality data in samples from monitoring 

sites in the West Fork San Jacinto River Basin, Texas, July 2008–August 2009 .............16

6. Summary of linear regression evaluation statistics for equations using turbidity and 

streamflow as predictive variables to estimate suspended-sediment and total suspended-solids concentrations at two main monitoring sites (stations 08067650 

and 08068000) in the West Fork San Jacinto River Basin, Texas, July 2008–August 

2009 ..............................................................................................................................................21
vii

7. Estimated monthly and total study period (14 months) suspended-sediment and 

total suspended-solids loads computed from multiple linear regression models developed for station 08067650 West Fork San Jacinto River below Lake Conroe 

near Conroe, Texas, July 2008–August 2009 .........................................................................26

8. Estimated monthly and total study period (14 months) suspended-sediment and 

total suspended-solids loads computed from multiple linear regression models 

developed for station 08068000 West Fork San Jacinto River near Conroe, Texas, 

July 2008–August 2009 ..............................................................................................................26

Conversion Factors, Datums, and Water-Quality Units

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

inch (in.) 25.4 millimeter (mm)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2) 2.590 square kilometer (km2) 

Volume

acre-foot (acre-ft) 1,233 cubic meter (m3) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

Mass

ton per day (ton/d) 0.9072 metric ton per day

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Datums

Horizontal coordinate information is referenced to North American Datum of 1983 (NAD 83).

Vertical coordinate information is referenced to National Geodetic Vertical Datum of 1929 

(NGVD 29).
viii

Water-Quality Units

Concentrations of chemical constituents in water are given in milligrams per liter (mg/L).

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 

25 °C).

Turbidity is given in Formazin nephelometric units (FNU).
Abstract

To better understand the hydrology (streamflow and water 

quality) of the West Fork San Jacinto River Basin downstream 

from Lake Conroe near Conroe, Texas, including spatial 

and temporal variation in suspended-sediment (SS) and total 

suspended-solids (TSS) concentrations and loads, the U.S. 

Geological Survey, in cooperation with the Houston-Galveston 

Area Council and the Texas Commission on Environmental 

Quality, measured streamflow and collected continuous and 

discrete water-quality data during July 2008–August 2009 in 

the West Fork San Jacinto River Basin downstream from Lake 

Conroe. 

During July 2008–August 2009, discrete samples were 

collected and streamflow measurements were made over the 

range of flow conditions at two streamflow-gaging stations on 

the West Fork San Jacinto River: West Fork San Jacinto River 

below Lake Conroe near Conroe, Texas (station 08067650) 

and West Fork San Jacinto River near Conroe, Texas (station 08068000). In addition to samples collected at these two 

main monitoring sites, discrete sediment samples were also 

collected at five additional monitoring sites to help characterize water quality in the West Fork San Jacinto River Basin. 

Discrete samples were collected semimonthly, regardless of 

flow conditions, and during periods of high flow resulting 

from storms or releases from Lake Conroe. Because the period 

of data collection was relatively short (14 months) and low 

flow was prevalent during much of the study, relatively few 

samples collected were representative of the middle and upper 

ranges of historical daily mean streamflows.

The largest streamflows tended to occur in response to 

large rainfall events and generally were associated with the 

largest SS and TSS concentrations. The maximum SS and TSS 

concentrations at station 08067650 (180 and 133 milligrams 

per liter [mg/L], respectively) were on April 19, 2009, when 

the instantaneous streamflow was the third largest associated 

with a discrete sample at the station. SS concentrations were 

25 mg/L or less in 26 of 29 environmental samples and TSS 

concentrations were 25 mg/L or less in 25 of 28 environmental samples. Median SS and TSS concentrations were 7.0 and 

7.6 mg/L, respectively. At station 08068000, the maximum SS 

concentration (1,270 mg/L) was on April 19, 2009, and the 

maximum TSS concentration (268 mg/L) was on September 

18, 2008. SS concentrations were 25 mg/L or less in 16 of 27 

of environmental samples and TSS concentrations were 25 

mg/L or less in 18 of 26 environmental samples at the station. 

Median SS and TSS concentrations were 18.0 and 14.0 mg/L, 

respectively. 

The maximum SS and TSS concentrations for all five 

additional monitoring sites were 3,110 and 390 mg/L, respectively, and the minimum SS and TSS concentrations were 5.0 

and 1.0 mg/L, respectively. Median concentrations ranged 

from 14.0 to 54.0 mg/L for SS and from 11.0 to 14.0 mg/L for 

TSS.

Continuous measurements of streamflow and selected 

water-quality properties at stations 08067650 and 08068000 

were evaluated as possible variables in regression equations 

developed to estimate SS and TSS concentrations and loads. 

Surrogate regression equations were developed to estimate 

SS and TSS loads by using real-time turbidity and streamflow 

data; turbidity and streamflow resulted in the best regression 

models for estimating near real-time SS and TSS concentrations for stations 08097650 and 08068000. 

Relatively large errors are associated with the regressioncomputed SS and TSS concentrations; the 90-percent prediction intervals for SS and TSS concentrations were ±48.9 

and ±43.2 percent, respectively, for station 08067650 and 

±47.7 and ±43.2 percent, respectively, for station 08068000. 

Regression-computed SS and TSS concentrations were 

Streamflow and Water-Quality Properties in the West 

Fork San Jacinto River Basin and Regression Models 

to Estimate Real-Time Suspended-Sediment and Total 

Suspended-Solids Concentrations and Loads in the 

West Fork San Jacinto River in the Vicinity of Conroe, 

Texas, July 2008–August 2009

By Lee J. Bodkin and Jeannette H. Oden
2 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

corrected for bias before being used to compute SS and 

TSS loads. The total estimated SS and TSS loads during 

July 2008–August 2009 were about 3,540 and 1,900 tons, 

respectively, at station 08067650 and about 156,000 and 

72,000 tons, respectively, at station 08068000. Because the 

estimated SS and TSS concentrations derived from the regression equations contained large error components, the computed load estimates are inferred to also include large errors. 

Loads were about 40 times larger at station 08068000 compared with loads at station 08067650, likely because flow at 

station 08067650 (2.5 miles downstream from Lake Conroe) 

is more representative of water-quality properties of releases 

from Lake Conroe, whereas flow at station 08068000 (11 

miles downstream from station 08067650) is more representative of water-quality properties in the West Fork San Jacinto 

River.

Introduction

Water-supply and water-quality concerns have increased 

the need to better understand the hydrology of the West Fork 

San Jacinto River Basin, Tex. (fig. 1), particularly the spatial 

and temporal variability of suspended-sediment (SS) and total 

suspended-solids (TSS) concentrations and loads in relation 

to streamflow, and other water-quality properties. Surficial 

mining of sand and gravel and the rapidly growing population 

of Montgomery County, Tex., where much of the West Fork 

San Jacinto River and its tributaries are located, have increased 

concerns among water managers about the effects of mining 

and urbanization on water supply and water quality in the 

West Fork San Jacinto River Basin. 

The Texas Commission on Environmental Quality 

(TCEQ) administers water-quality management programs 

with the goal of protecting, maintaining, and restoring water 

resources in Texas (Texas Commission on Environmental 

Quality, 2010). One such program is the Texas Clean Rivers 

Program (CRP), which was established by the 1991 Texas 

Legislature (Texas Administrative Code, 2007). Under the 

CRP, water-quality monitoring and assessments in 23 river 

and coastal basins statewide are done by partner agencies. The 

Houston-Galveston Area Council (H–GAC) is TCEQ’s partner 

agency for a 13-county service area in southeastern Texas that 

includes the Houston metropolitan area (fig. 1). Every 2 years, 

CRP partners collect water-quality data from water bodies that 

are not monitored routinely. Data from these special studies 

help determine whether additional assessment is needed to 

evaluate human health concerns, status of ecological conditions, or designated stream uses. 

 Measurements of SS and TSS concentrations, as well 

as certain water-quality properties such as turbidity, can be 

used to evaluate SS and TSS loads if sufficient measurements 

are made characterizing the range of hydrologic conditions for 

sufficient time. As explained by Oden and others (2009, p. 21), 

“Turbidity is inversely proportional to transparency depth and 

is a measure of the scattering of light (American Society for 

Testing and Materials, 2003). Thus, a decrease in the amount 

of transparency in water seems appropriate as a possible indicator for an increase in the amount of SS in the water.” To better understand the hydrology (streamflow and water quality) 

in the West Fork San Jacinto River Basin, the U.S. Geological 

Survey (USGS), in cooperation with H–GAC and TCEQ, 

measured streamflow (discharge) and collected continuous 

and discrete water-quality data during the period July 2008–

August 2009 at monitoring sites in the West Fork San Jacinto 

River Basin downstream from Lake Conroe (fig. 1). Samples 

for SS and TSS analysis were collected during the study to 

characterize SS and TSS concentrations and loads in the West 

Fork San Jacinto River Basin, as well as to document differences in loads obtained by using either SS or TSS data in the 

West Fork San Jacinto River at two USGS streamflow-gaging 

stations (table 1) downstream from Lake Conroe.

Previous USGS studies have shown that when TSS 

concentrations are used to estimate SS loads, the results are 

often biased low compared with loads estimated by using SS 

concentrations, particularly if sand constitutes more than about 

20 percent of the mass of the water-sediment mixture. The 

TSS analytical method tends to produce data that are negatively biased by 25 to 34 percent with respect to SS concentrations in samples collected at the same time (U.S. Geological 

Survey, 2000b). Differences between load estimates derived 

from SS and TSS data can also vary greatly at a given site 

depending on streamflow (Gray and others, 2000; Glysson, 

Gray, and Conge, 2000; Glysson, Gray, and Schwarz, 2001; 

U.S. Geological Survey, 2000a). However, in some basins 

where sediment loads include only small amounts of sand, 

TSS concentrations can sometimes be used effectively to 

characterize sediment loads. In such basins, the similarity of 

SS and TSS concentrations over the range of expected flows 

at a given site needs to be well documented (U.S. Geological 

Survey, 2000a). Surrogate relations and load estimates were 

derived for both SS and TSS data; differences between loads 

obtained by using either SS or TSS data are depicted, but an 

evaluation of the effectiveness of using TSS data to characterize sediment loads in the study basin was beyond the scope of 

this study.

Purpose and Scope

This report describes streamflow and water-quality 

properties for July 2008–August 2009 in the West Fork San 

Jacinto River Basin downstream from Lake Conroe and 

documents the regression models developed to estimate SS 

and TSS concentrations and loads at two USGS streamflowgaging stations on the main stem of the West Fork San Jacinto 

River. Continuous monitors measured selected water-quality 

properties (water temperature, specific conductance, pH, 

dissolved oxygen, and turbidity) at stations 08067650 West 

Fork San Jacinto River below Lake Conroe near Conroe, Tex., 

located at the State Highway 105 bridge (hereinafter station 
Introduction 3

08067650) and 08068000 West Fork San Jacinto River near 

Conroe, Tex., located at the Interstate Highway 45 bridge 

(hereinafter station 08068000). Water-quality properties 

were characterized in a preliminary manner by using summary statistics of water-quality data from stations 08067650 

and 08068000 and from five additional monitoring sites on 

tributaries to the West Fork San Jacinto River downstream 

from Lake Conroe. Periodic streamflow measurements were 

made and discrete water-quality samples were collected 

semimonthly during July 2008–August 2009 for selected 

water-quality properties and constituents (SS, TSS, water 

temperature, specific conductance, pH, dissolved oxygen, and 

turbidity) at stations 08067650 and 08068000 and the five 

additional monitoring sites. Techniques used to collect and 

analyze these samples are documented. 

Description of Study Area

The headwaters of the West Fork San Jacinto River originate in Walker County, Tex. (fig. 1). The drainage area of the 

West Fork San Jacinto River is 990 square miles (mi2). Lake 

Figure 1. Location of monitoring sites in the West Fork San Jacinto River Basin, Texas, July 2008–August 2009. 

08067650

08067652

08067653

08067657 08067800

08067900 08068000

Landrum Creek

Little Caney 

Creek

Caney Creek

WALLER

LAKE 

CONROE

Creek

West

Fork

San Jacinto

CONROE

LAKE 

HOUSTON

Lake

PATTERSON

LAKE

WALKER

MONTGOMERY

G

R

MI ES

HARRIS 30°0'

30°30' 30°30'

0 10 20 MILES

0 10 20 KILOMETERS

EXPLANATION

Subwatershed boundary

County boundary

Interstate Highway

State Highway

Farm Road

U.S. Geological Survey

streamflow-gaging station

and number (table 1)

Main monitoring site

Additional monitoring site

08067650

08067800

Base from:

U.S. Department of Agriculture, Natural Resources

Conservation Service, 2010—Basins

U.S. Geological Suvey, 2010a—Streams, lakes

U.S. Census Bureau, 2010—Counties, cities

Universal Transverse Mercator Projection, Zone 15

North American Datum of 1983

Study 

area

Houston

TEXAS

West Fork 

San Jacinto 

River Basin

-95°45'

-95°45'

-95°15'

-95°15'

30°0'

LAKE

HOUSTON

RIVER BASIN

WEST FORK 

SAN JACINTO 

RIVER BASIN

San Jacinto 

River Basin

KAREN

White Oak Creek

Alligator Creek

River
4 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

Creek is a major tributary to the West Fork San Jacinto River 

and Lake Creek tributaries include Landrum Creek, Caney 

Creek, and Little Caney Creek. White Oak and Alligator 

Creeks also are tributaries to the West Fork San Jacinto River. 

The drainage area of the West Fork San Jacinto River is 451 

mi2 at station 08067650 and 828 mi2 at station 08068000.

 The West Fork San Jacinto River provides much of the 

inflow to Lake Conroe, a reservoir impounded in 1973 to help 

meet municipal water-supply needs of Houston (Villalon and 

others, 1998). The normal operating level of Lake Conroe is 

201 feet (ft) above the National Geodetic Vertical Datum of 

1929, corresponding to a storage volume of 430,300 acre-feet 

(acre-ft) (U.S. Geological Survey, 2010b). Downstream from 

Lake Conroe, the West Fork San Jacinto River flows through 

Montgomery County and a small part of Harris County before 

becoming one of several streams impounded by Lake Houston, 

a reservoir impounded in 1954 and another municipal watersupply reservoir for Houston (Villalon and others, 1998). 

The maximum design elevation of 44.5 ft for Lake Houston 

corresponds to a capacity of 133,990 acre-ft (U.S. Geological 

Survey, 2010b). 

The northern part of the West Fork San Jacinto River 

Basin is a gently rolling area, most of which is heavily timbered. The southern part of the basin is mostly prairie (Hughes 

and Rawson, 1966). Broad, shallow stream valleys (along with 

remnants of older, abandoned stream valleys) cut through the 

study area. Pine, oak, and other trees grow along the banks 

of streams throughout the study area. Land cover in the West 

Fork San Jacinto River Basin includes municipal, commercial, agricultural, forested, and residential areas. Land cover 

downstream from Lake Conroe is predominately a woody 

wetland with some hay pastures and mixed forest (fig. 2). 

Conroe and other towns and cities in the basin consist of a 

mix of land covers from open space to high-intensity developments, such as apartments. Population density ranges from 44 

to 549 people per square mile (U.S. Census Bureau, 2000). 

Land-surface elevation in the basin ranges from about 381 ft at 

the headwaters to 43 ft at Lake Houston.

The climate of the study area is humid subtropical 

(Larkin and Bomar, 1983) and is characterized by high relative humidity, long hot summers, and short temperate winters. 

Monthly rainfall in 7 of the 14 months of the study (July 

2008–August 2009) was less than the mean monthly rainfall recorded at National Weather Service station 411956 in 

Conroe (fig. 3). Although total rainfall at Conroe during the 

14-month study period was 60.23 inches (in.) compared with 

the long-term (1983–2009) average of 56.27 in., 19.27 of the 

60.23 in. of rainfall fell in 2 months—September 2008 and 

April 2009 (fig. 3). The maximum monthly total was 10.13 in. 

for September 2008, and the minimum monthly total was 0.33 

in. for June 2009 (National Climatic Data Center, 2008 and 

2009). 

Data-Collection and Regression 

Methods 

Except as otherwise noted, all data collection followed 

surface-water quality monitoring (SWQM) procedures for 

Table 1. Description of monitoring sites in the West Fork San Jacinto River Basin, Texas, July 2008–August 2009.

[M, main site; n/a, not applicable, --, data not available; A, additional site; FM, Farm Road]

U.S. Geological Survey 

station name

U.S. Geological 

 station 

number 

(fig. 1)

Texas Commission on 

Environmental Quality 

station 

number

Latitude 

(degrees 

minutes 

seconds)

Longitude 

(degrees 

minutes 

seconds)

Date monitoring 

established Drainage 

area 

(square 

miles)

Sampling 

site 

desigDischarge nation Water 

quality

West Fork San Jacinto River 

below Lake Conroe near 

Conroe, Tex.

08067650 11251 30°20'31.09" 95°32'33.50" August 1972 July 2008 451 M

West Fork San Jacinto River near 

Conroe, Tex.

08068000 11245 30°14'43.11" 95°27'27.83" July 1939 July 2008 828 M

White Oak Creek at Memorial 

Drive, Conroe, Tex.

08067652 20731 30°19'17.48" 95°30'28.09" n/a -- A

West Fork San Jacinto River at 

FM 2854 near Conroe, Tex.

08067653 11250 30°18'52.67" 95°30'41.18" n/a -- A

Alligator Creek at Sergeant Ed 

Holcomb Road, Conroe, Tex.

08067657 20732 30°17'35.11" 95°28'52.59" n/a -- A

Lake Creek at FM 149 near 

Karen, Tex.

08067800 18191 30°16'49.26" 95°42'20.17" n/a -- A

Lake Creek near Conroe, Tex. 08067900 11367 30°15'13.59" 95°34'44.38" n/a -- A
Data-Collection and Regression Methods 5

water and sediment (Texas Commission on Environmental 

Quality, 2008; chapters 2, 3, 5, 8, and 10). These procedures 

are further described in the quality-assurance project plan 

(QAPP) developed for the study (Jean Wright, HoustonGalveston Area Council, written commun., 2008). 

Streamflow Measurements

Streamflow measurements were made periodically in 

conjunction with the collection of discrete water-quality 

samples following standard USGS methods (Rantz and others, 

1982). During July 2008–August 2009, six streamflow measurements were made at each of the two main monitoring 

sites (stations 08067650 and 08068000). Water-surface 

elevation (stage) referenced to the gage datum was also measured continuously at the stations by using a noncontact radar 

stage sensor (Blanchard, 2007) mounted at station 08068000 

and a submersible pressure transducer (Freeman and others, 

2004) at station 08067650. Stage was recorded to the nearest 0.01 ft every 15 minutes, and data were transmitted every 

Figure 2. Land use in the San Jacinto River Basin, Texas, July 2008–August 2009. 

08067650

08067652

08067653

08067657 08067800

08067900 08068000

River

LAKE 

CONROE

Creek

West

For San Jacinto

CONROE

LAKE 

HOUSTON

Lake

PATTERSON

LAKE

WALKER

MONTGOMERY

GR

MI ES

HARRIS 30°0'

30°30'

Base from:

U.S. Department of Agriculture, Natural Resources

Conservation Service, 2010—Basins

U.S. Geological Suvey, 2010a—Streams, lakes

U.S. Census Bureau, 2010—Counties, cities

Universal Transverse Mercator Projection, Zone 15

North American Datum of 1983

0 10 20 MILES

0 10 20 KILOMETERS

11 - Open water

21 - Developed, open space

22 - Developed, low intensity

23 - Developed, medium intensity

24 - Developed, high intensity

31 - Barren land, rock, sand, clay

41 - Deciduous forest

42 - Evergreen forest

43 - Mixed forest

52 - Shrub, scrub

71 - Grassland, herbaceous

81 - Pasture, hay

82 - Cultivated crops

90 - Woody wetlands

95 - Emergent herbaceous wetlands

08067650

08067800

-95°45'

-95°45'

-95°15'

-95°15'

30°0'

LAKE

HOUSTON

RIVER BASIN

KAREN

White Oak Creek

Alligator Creek

Subwatershed boundary

County boundary

Interstate Highway

State Highway

Farm Road

U.S. Geological Survey

streamflow-gaging station

and number (table 1)

Main monitoring site

Additional monitoring site

Land cover (Multi-Resolution Land 

Characteristics Consortium, 2008)

WEST FORK 

SAN JACINTO 

RIVER BASIN

EXPLANATION

30°30'
6 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

4 hours by the Geostationary Operational Environmental 

Satellite (GOES) to the USGS National Water Information 

System (NWIS) database (U.S. Geological Survey, 2010b). 

Stage-discharge relations developed on the basis of 

periodic streamflow measurements were used to compute 

a continuous record of streamflow at stations 0806750 and 

08068000 (Kennedy, 1983, 1984). The stage also was measured to determine instantaneous streamflow at the time each 

discrete water-quality sample was collected. All real-time 

streamflow (discharge) values met USGS accuracy standards 

(Rantz and others, 1982). The stage-discharge relation at station 08067650 typically is maintained for streamflow of more 

than 10 cubic feet per second (ft3

/s). For the study period, 

additional low-flow measurements (less than10 ft3/s) were 

made to extend the stage-discharge relation to include streamflow between 0 and 10 ft3

/s. 

In addition to six streamflow measurements at stations 

08067650 and 08068000, three to eight streamflow measurements were made at each of the five additional monitoring 

sites in conjunction with the collection of water-quality 

samples. When the stream was relatively shallow (water 

depths less than 3 ft), streamflow measurements were made 

while wading by using a Sontek/YSI rod-mounted acousticvelocity meter. Tethered acoustic Doppler current profilers 

were used to measure streamflow that was too deep to wade by 

using methods described in Oberg and others (2005). 

Continuous Measurements of Water-Quality 

Properties 

Continuous measurements of selected water-quality 

properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were made at stations 08067650 

and 08068000 for evaluation as possible variables in development of regression equations to estimate SS and TSS concentrations and loads. Continuous water-quality properties 

were measured every 15 minutes by using a Sontek/YSI 6920 

V2–2 multiprobe water-quality sonde, and data were transmitted every 4 hours by GOES to the NWIS database (U.S. 

Geological Survey, 2010b) The water-quality monitors were 

calibrated and the data were processed following methods in 

the SWQM procedures (Texas Commission on Environmental 

Quality, 2008; chapter 8). Field procedures, calibration of the 

continuous water-quality monitors, and record computation 

and review methods also followed Wagner and others (2006) 

and are further outlined in the QAPP (Jean Wright, HoustonGalveston Area Council, written commun., 2008). The turbidity sensor was calibrated following USGS guidelines (Wagner 

Figure 3. Total monthly rainfall (July 2008–August 2009) and mean monthly rainfall (1983–2009) at National Weather Service station 

411956 in Conroe, Texas. 

JULY AUG SEPT OCT NOV DEC JAN FEB MAR APR MAY JUNE JULY AUG

RAINFALL, IN INCHES

MONTH

Total monthly rainfall, July 2008–August 2009 Mean monthly rainfall, 1983–2009

2008 2009

0

2.0

4.0

6.0

8.0

10.0

12.0
Data-Collection and Regression Methods 7

and others, 2006; U.S. Geological Survey, variously dated) and 

further outlined in the QAPP because the SWQM procedures 

do not include a discussion on turbidity sensors. Before being 

deployed, each monitor was calibrated at the USGS Texas 

Water Science Center, Gulf Coast Program Office laboratory 

in The Woodlands, Tex. Four water-quality monitors were 

used during the study, with two deployed at any given time—

one at station 08067650 and one at station 08068000. Each 

water-quality monitor was deployed for about 14 days and 

then replaced with one that had been recalibrated; this rapid 

replacement cycle helped maintain a continuous record of 

calibrated water-quality data and eliminated potential problems associated with biofouling (Wagner and others, 2006). 

Post calibration was done after the water-quality monitor was 

retrieved from the field to determine if the instrument readings had changed compared with the initial calibration. If any 

selected water-quality property exceeded a set guideline in 

the SWQM procedures (Texas Commission on Environmental 

Quality, 2008; chapter 8), the data for that property were 

invalidated for the deployment period. All real-time waterquality properties used to compute the load estimates met the 

accuracy requirements specified in the QAPP.

The water-quality monitors were installed differently at 

stations 08067650 and 08068000. The water-quality monitor 

at station 08067650 was installed in a swinging flow-through 

well constructed of schedule 80 polyvinyl chloride (PVC) 

pipe with holes in the bottom 3 ft that allowed water to pass 

through the well. The well was hung from the State Highway 

105 bridge at station 08067650 by a heavy-duty chain to 

facilitate movement during larger streamflows, minimizing the 

likelihood of damage caused by channel debris and maximizing the likelihood of the monitor remaining near the centroid 

of flow. Similar to the installation at station 08067650, the 

water-quality monitor at station 08068000 also was installed 

in a flow-through well of PVC pipe with holes in the bottom 

3 ft. Unlike the swinging-well design at station 08067650, a 

fix-mounted design was used at station 08068000. Because of 

the height of the bridge at station 08068000, a swinging-well 

design could not safely be implemented; the well containing 

the monitor was mounted on a bridge pier in the deepest part 

of the channel where streamflow velocity was likely largest. Periodically throughout the study period, water-quality 

properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were measured across the entire 

channel, by using one of the spare monitors, to verify that the 

streamflow was well mixed and that the installed monitors 

were providing water-quality data representative of the flow 

at each gage. On average, water-quality properties measured 

across the channel by using the spare monitor varied by 5 percent or less compared with water-quality properties measured 

continuously by the monitors installed at stations 08067650 

and 08068000. 

Discrete Water-Quality Sampling

Water-quality samples were periodically collected at each 

station in accordance with guidelines in the USGS “National 

Field Manual for the Collection of Water-Quality Data” (U.S. 

Geological Survey, variously dated). During July 2008–August 

2009, 29 and 27 discrete samples were collected at stations 

08067650 and 08068000, respectively, and analyzed for SS, 

TSS, and selected water-quality properties (water temperature, 

specific conductance, pH, dissolved oxygen, and turbidity). 

At each station, some discrete samples were collected semimonthly without regard to hydrologic conditions, whereas 

other samples were collected during periods of high flow 

resulting from storms or releases from Lake Conroe (fig. 4). 

At each of the five additional monitoring sites, routine samples 

were collected five times throughout the study period as well 

as one to three samples during periods of high flow resulting 

from storms. 

Discrete water-quality samples were collected by either 

the equal-width increment (EWI) or the grab sampling 

method. When streamflow was sufficient, EWI samples 

were collected by dividing the river into five or more equalwidth sampling increments. By ensuring the volume of the 

overall sample is proportional to the amount of discharge in 

each equal-width sampling increment, an isokinetic depthintegrated sample is collected (U.S. Geological Survey, variously dated). EWI samples were collected when conditions 

permitted, either while wading the stream or from a bridge. 

When streamflow was not sufficient to use the EWI method, 

grab samples were collected from the centroid of flow by 

using methods described in the SWQM procedures (Texas 

Commission on Environmental Quality, 2008; chapter 5). 

When EWI samples were collected, the selected water-quality 

properties were measured in each EWI to determine uniformity of water-quality properties across the stream channel. 

The same water-quality properties measured in each increment 

of flow for the EWI samples were measured at the centroid of 

flow for grab samples.

Samples representing each vertical for the EWI method 

(or the sample representing the single vertical for the grab 

method) were poured into a plastic churn, mixed, and dispensed into sample bottles. Environmental and replicate 

samples were dispensed from the churn. The resulting composite water-sediment samples were horizontally and vertically averaged throughout the stream cross section and are 

assumed to represent the average streamflow-weighted SS 

concentration (Edwards and Glysson, 1999; U.S. Geological 

Survey, 2006a). Samples for SS analysis were shipped to the 

USGS Kentucky Water Science Center Sediment Laboratory 

in Louisville, Ky., for the analysis of SS concentration and 

sand/fine separation; samples for TSS analysis were shipped 

to the Eastex Environmental Laboratory in Coldspring, 

Tex. 

During the study, three different wading cross sections 

were used for collecting samples at station 08068000. Before 

November 2008, the original cross section where samples 

were collected was 10 ft downstream from the water-quality 

monitor. After the channel was scoured during a period of 

high flow in November 2008, the wading cross section was 

relocated 800 ft downstream from the water-quality monitor 
8 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

Figure 4. Hydrographs showing streamflow and sample collection at station (A) 08067650 West Fork San Jacinto River below Lake 

Conroe near Conroe, Texas, and (B) 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–August 2009. 

JULY AUG SEPT OCT NOV DEC 

2009

JAN FEB MAR APR MAY JUNE JULY AUG

0.1

0.2

0.5

1

2

5

10

20

50

100

200

500

1,000

2,000

4,000

2008

A. 08067650 West Fork San Jacinto River below Lake Conroe near Conroe, Texas

DISCHARGE, IN CUBIC FEET PER SECOND

Sample collected

1

2

5

10

20

50

100

200

500

1,000

2,000

5,000

10,000

15,000

B. 08068000 West Fork San Jacinto River near Conroe, Texas

DISCHARGE, IN CUBIC FEET PER SECOND

JULY AUG SEPT OCT NOV DEC

2009

JAN FEB MAR APR MAY JUNE JULY AUG

2008

Sample collected
Data-Collection and Regression Methods 9

because flow in the cross section near the monitor was too 

deep to wade. The original cross section was still sampled 

during high-flow conditions from the bridge by using a crane, 

four-wheel base, and appropriate sampler (U.S. Geological 

Survey, variously dated). During August–November 2008, 

eight samples were collected by wading or from the bridge 

at the original station 08068000 cross section near the waterquality monitor. During December 2008–February 2009, 

five samples were collected by wading at the cross section 

800 ft downstream from the water-quality monitor. In January 

2009, H–GAC employees accompanied USGS employees 

on a field trip to the sites to observe the collection of discrete 

water-quality samples, measurement of streamflow, and 

maintenance of the monitors. The H–GAC employees noted 

that debris deposited on the piers of a railroad bridge between 

the original sampling cross section and the new sampling 

cross section had created a pool of backwater in the stream 

and were concerned that the backwater was potentially 

creating a settling pool for SS. Because the water-quality 

monitor was upstream from this possible settling pool, 

there was concern that the continuous water-quality readings measured by the monitor might not be representative of 

water quality downstream from the pool. Between December 

2008 and February 2009, all wading samples were collected 

downstream from the pool at the new sampling cross section. 

To eliminate any possible effects of the pool on SS concentration, a third sampling cross section was selected about 

2,000 ft upstream from the water-quality monitor in a reach 

where there were no channel controls, debris piles, or inflows. 

During February–August 2009, 14 samples were collected 

at station 08068000 while wading the cross section 2,000 ft 

upstream from the water-quality monitor. Because a comparison of turbidity readings measured upstream and downstream 

from the potential settling pool did not indicate any appreciable differences, all sample results were included in the 

development of regression equations to estimate SS and TSS 

concentrations and loads.

Because only 14 months of data were collected, all 

results of this study are considered preliminary. It is preferred 

that monthly samples be collected for at least 24 to 36 months 

to obtain a dataset more representative of the typical range of 

hydrologic conditions; data typical of the range of hydrologic 

conditions facilitate accurate calibration of regression models 

used to predict water-quality properties. Although the sample 

total is relevant, the distribution of the data over the range of 

observed SS, turbidity, and streamflow values for a site is of 

paramount importance (Rasmussen and others, 2009). The 

turbidity data (table 2) were well distributed over the range 

of streamflow during the study period (figs. 4A and 4B), but 

relatively dry conditions during much of the study resulted in a 

smaller range of flow compared with that typically observed in 

the West Fork San Jacinto River, particularly during periods of 

2 or more years.

Although 14 months is not sufficient to establish an 

accurate surrogate relation to estimate SS and TSS loads 

by using continuously monitored water-quality properties 

and streamflow (Rasmussen and others, 2009), all relatively 

large streamflow events that did occur during the study 

period were sampled. The range of streamflow during the 

study was well characterized by the samples collected (figs. 

4A and 4B). 

Analytical Methods

Sediment samples were analyzed for SS concentration 

and sand/fine separation. The samples were analyzed in 

accordance with standard protocols established by the USGS 

(Guy, 1969) and American Society for Testing and Materials 

(2007). 

Lietz and Debiak (2006) describes in detail the methods 

used for the analysis of fluvial sediment concentrations and 

sand/fine separation and states that (p. 9), “Sand/fine separations were used to determine the amount of material that 

was larger or smaller than sand size. The term ‘fine’ refers to 

material that passes through a 0.0625-millimeter (0.0024-in.) 

mesh sieve, and ‘sand’ refers to particles that are retained on 

the sieve.” Sample holding time and analytical procedures followed protocols established by Shreve and Downs (2005).

Eastex Environmental Laboratories analyzed samples 

for TSS concentrations; Eastex Environmental Laboratory 

was accredited by the National Environmental Laboratory 

Accreditation Conference to analyze samples for TSS by 

method 2540D during July 2008–August 2009 (Daniel Bowen, 

Eastex Environmental Laboratory, Coldspring, Tex., written 

commun., 2009). Method 2540D is described by the American 

Public Health Association (2005). 

Quality Assurance

Quality-assurance (QA) requirements for sample collection, data management, and documentation described by the 

SWQM procedures (Texas Commission on Environmental 

Quality, 2008; chapter 10) were followed; QA requirements 

also were included in the study QAPP (Jean Wright, HoustonGalveston Area Council, written commun., 2008). Qualitycontrol samples included 14 replicate samples; about 25 

percent of all samples were replicates (table 2). 

Replicate SS and TSS samples were collected and 

analyzed to determine the precision of the results. The replicate samples were collected by splitting a water sample 

mixed in a single container (plastic churn splitter) into two 

bottles, one representing the environmental sample and one 

the replicate sample. The replicate sample is also referred to as 

a field split by TCEQ (Texas Commission on Environmental 

Quality, 2008). The samples were split immediately after 

collection from the stream using the churn splitter and then 

shipped to the laboratory as separate samples. The identities 

of the environmental and replicate samples were not revealed 

to the laboratory. The relative percent difference (RPD) was 

determined for each set of split samples as a measure of variability. Replicate samples were compared with associated 
10 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

Table 2. Water-quality data in samples from two main monitoring sites (stations 08067650 and 08068000) in the West Fork San Jacinto 

River Basin, Texas, July 2008–August 2009—Continued. 

Station name Date Time

Sample 

type 

Discharge 

(ft3/s)

Water 

temperature 

(°C)

Specific 

conductance 

(μS/cm)

pH 

(standard 

units)

Dissolved 

oxygen 

concentration 

(mg/L)

Turbidity 

(FNU)1

Suspendedsediment 

concentration

(mg/L)

Total 

suspendedsolids 

concentration

(mg/L)

Sand/

fine 

separation 

(percent 

>0.0625 

mm)

08067650 West 

Fork San Jacinto 

River below Lake 

Conroe near 

Conroe, Tex.

9/5/2008 1335 ER 0.90 26.7 469 7.9 7.7 5.1 13 9.4 79

9/16/2008 1150 ER 8.3 23.5 253 7.2 4.6 18.2 23 -- 98

9/18/2008 1305 ER 5.1 22.4 310 7.2 3.1 8.6 14 10.4 76

9/19/2008 1525 EE 2,200 26.1 247 8.3 8.4 11.5 45 26.8 47

9/23/2008 1338 EE 2,230 26.1 247 7.6 7.6 8.3 73 45.0 50

10/6/2008 1223 ER 2.0 23.2 464 7.3 5.3 2.9 3.0 4.8 93

10/29/2008 1258 ER .94 14.4 464 7.4 7.5 4.9 6.0 3.6 93

11/7/2008 1118 ER .54 17.7 494 7.4 4.9 2.8 6.0 4.2 94

11/14/2008 1430 EE 196 20.0 247 8.0 9.1 10.9 21 21.5 92

11/28/2008 1225 EE 182 17.6 250 7.9 9.3 4.0 11 8.8 52

12/19/2008 1028 ER 1.3 15.1 414 7.5 7.7 3.8 6.0 7.6 92

12/19/2008 1033 SR 1.3 15.1 414 7.5 7.7 3.8 6.0 6.5 85

12/31/2008 0956 ER 2.2 11.4 394 7.6 8.2 8.6 10 6.6 97

1/13/2009 1103 ER 3.2 9.7 436 7.6 10.6 4.4 5.0 3.5 98

1/13/2009 1108 SR 3.2 9.7 436 7.6 10.6 4.4 5.0 2.5 96

1/22/2009 1223 ER 2.7 10.7 470 7.9 13.0 2.6 4.0 3.0 97

2/9/2009 1123 ER 3.1 16.2 358 7.5 8.3 7.8 9.0 9.0 99

2/9/2009 1128 SR 3.1 16.2 358 7.5 8.3 7.8 10 7.5 99

2/23/2009 1035 ER 1.6 12.0 480 8.0 11.8 1.3 3.0 4.0 96

3/9/2009 1428 ER 1.0 23.0 446 8.0 10.3 3.2 6.0 6.0 98

3/18/2009 1253 ER 1.8 18.0 413 7.5 8.8 6.6 10 12.5 91

4/8/2009 1148 ER 1.8 16.2 353 7.6 7.7 8.6 10 7.0 98

4/19/2009 1318 EE 1,220 19.2 254 7.8 9.6 36.2 180 133 69

4/29/2009 1315 EE 643 21.0 253 7.7 8.8 7.5 20 12.0 75

5/13/2009 1400 ER 5.1 27.0 321 7.3 5.9 3.6 7.0 6.8 90

5/13/2009 1405 SR 5.1 27.0 321 7.3 5.9 3.6 31 7.2 98

5/27/2009 1130 ER 1.5 25.0 414 7.3 4.6 6.3 8.0 10.0 94

6/1/2009 1320 ER 1.2 25.1 474 7.6 7.0 3.7 7.0 9.0 87

6/1/2009 1325 SR 1.2 25.1 474 7.6 7.0 3.7 9.0 6.5 73

6/23/2009 0835 ER 3.4 27.9 528 7.6 4.3 2.4 4.0 7.5 94

7/9/2009 1310 ER .68 30.5 450 7.9 8.3 2.4 5.0 5.0 92

7/9/2009 1315 SR .68 30.5 450 7.9 8.3 2.4 5.0 2.0 91

7/20/2009 1240 ER .41 27.7 508 7.9 7.4 1.6 4.0 10.0 86

8/6/2009 1045 ER .30 28.8 459 7.7 4.8 3.6 5.0 6.4 96

8/6/2009 1050 SR .30 28.8 459 7.7 4.8 3.6 5.0 5.0 88

8/19/2009 1100 ER .48 27.1 473 7.7 4.8 2.4 2.0 4.3 94

Table 2. Water-quality data in samples from two main monitoring sites (stations 08067650 and 08068000) in the West Fork San Jacinto 

River Basin, Texas, July 2008–August 2009. 

[ft3/s, cubic feet per second; °C, degrees Celsius; μS/cm, microsiemens per centimeter at 25 degrees Celsius; mg/L, milligrams per liter; FNU, Formazin 

nephelometric units; >, greater than; mm, millimeter; ER, environmental routine; --, sample not collected; EE, environmental event; SR, split replicate]
Data-Collection and Regression Methods 11

Table 2. Water-quality data in samples from two main monitoring sites (stations 08067650 and 08068000) in the West Fork San Jacinto 

River Basin, Texas, July 2008–August 2009—Continued. 

Station name Date Time

Sample 

type 

Discharge 

(ft3/s)

Water 

temperature 

(°C)

Specific 

conductance 

(μS/cm)

pH 

(standard 

units)

Dissolved 

oxygen 

concentration 

(mg/L)

Turbidity 

(FNU)1

Suspendedsediment 

concentration

(mg/L)

Total 

suspendedsolids 

concentration

(mg/L)

Sand/

fine 

separation 

(percent 

>0.0625 

mm)

08068000 West 

Fork San Jacinto 

River near Conroe, 

Tex.

9/5/2008 1045 ER 19.0 26.7 1,000 7.8 9.3 5.2 6.0 4.2 83

9/16/2008 1040 EE 1,460 24.0 142 7.0 5.0 72.0 33 -- 66

9/18/2008 1130 EE 669 22.8 155 7.1 5.8 39.0 81 268 81

9/23/2008 1057 EE 2,800 25.9 252 7.7 7.5 22.7 147 68.7 39

10/6/2008 1048 ER 43.0 24.1 746 7.4 7.4 12.9 18 19.2 97

10/29/2008 1435 ER 36.0 17.9 521 7.4 10.1 11.5 9.0 4.6 98

11/7/2008 1335 ER 30.0 20.7 702 7.8 10.8 11.0 19 17.4 97

11/14/2008 930 EE 6,000 18.4 109 7.0 6.3 108 1,160 195 19

12/19/2008 1323 ER 70.0 18.2 447 7.7 11.5 7.3 11 14.5 92

12/19/2008 1328 SR 70.0 18.2 447 7.7 11.5 7.3 10 23.0 96

12/31/2008 1108 ER 78.0 12.9 453 7.5 10.3 12.3 14 8.8 95

1/13/2009 1246 ER 61.0 11.5 548 7.6 11.8 7.3 10 4.0 91

1/13/2009 1251 SR 61.0 11.5 548 7.6 11.8 7.3 10 5.5 99

1/22/2009 1458 ER 52.0 13.8 673 8.3 15.3 5.3 7.0 1.5 91

2/9/2009 1353 ER 63.0 18.1 639 7.5 10.2 7.1 11 10.5 85

2/9/2009 1358 SR 63.0 18.1 639 7.5 10.2 7.1 12 9.5 92

2/23/2009 1355 ER 40.0 15.9 606 7.6 11.5 4.3 8.0 6.0 96

3/9/2009 1123 ER 34.0 22.2 672 7.5 8.7 1.8 10 9.5 88

3/16/2009 1105 ER 182 13.4 322 7.4 9.5 39.9 56 42.0 90

4/8/2009 1548 ER 43.0 21.0 572 7.7 10.6 9.2 12 6.5 94

4/19/2009 1735 EE 4,200 19.8 175 7.2 7.1 110 1,270 254 19

4/21/2009 1230 EE 7,300 20.0 154 7.1 6.7 64.0 347 97.0 17

5/13/2009 1038 ER 105 25.8 396 7.4 7.6 14.2 66 18.0 99

5/13/2009 1043 SR 105 25.8 396 7.4 7.6 14.2 57 16.0 98

5/28/2009 0803 ER 43.0 23.6 603 7.3 6.8 7.2 50 7.5 99

6/1/2009 1040 ER 33.5 24.5 612 7.5 9.0 5.9 10 10.0 87

6/1/2009 1045 SR 33.5 24.5 612 7.5 9.0 5.9 84 10.5 99

6/23/2009 1455 ER 24.0 32.4 903 8.0 10.2 10.0 17 13.5 91

7/9/2009 1013 ER 172 30.0 287 7.4 6.1 68.8 134 102 96

7/9/2009 1018 SR 172 30.0 287 7.4 6.1 68.8 132 110 96

7/21/2009 1208 ER 18.0 29.4 1,119 8.0 10.0 7.5 14 11.0 91

8/6/2009 1150 ER 16.0 31.0 888 7.7 7.8 14.6 25 16.6 84

8/6/2009 1155 SR 16.0 31.0 888 7.7 7.8 14.6 23 18.2 95

8/19/2009 1200 ER 83.0 27.0 310 7.3 5.8 226 233 160 98

1

 Determined by shining an incident beam of light into a parcel of water and measuring the reflected light at an angle of 90 degrees to the incident light. 
12 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

environmental samples by computing the RPD for each constituent with the equation, 

RPD = |C1 – C2|/((C1 + C2)/2) x 100, (1)

where 

C1 is the SS or TSS concentration, in milligrams per liter, 

from the environmental sample; and 

C2 is the SS or TSS concentration, in milligrams per liter, 

from the replicate (field-split) sample.

In accordance with the QAPP (Jean Wright, HoustonGalveston Area Council, written commun., 2008), RPDs of 

20 percent or less were judged to indicate acceptable agreement between analytical results if the concentrations were 

sufficiently large compared with the laboratory reporting level 

(LRL). The RPDs for 11 of 14 replicate sample pairs (79 

percent) analyzed for SS or TSS were 20 percent or smaller. 

If the RPD computed for an environmental sample and its 

replicate exceeded 20 percent, the results were further evaluated to determine if the environmental sample result should 

be included among data used to develop regression equations to estimate SS and TSS concentrations. Streamflow and 

all water-quality properties measured when the sample was 

collected were used to determine if the environmental and 

replicate sample results were valid. If the replicate sample 

result was determined to be invalid but the associated environmental sample result was determined to be valid, the environmental sample result was still included in the analysis. The 

RPD criteria was larger for samples collected during low-flow 

conditions, which typically yielded much lower SS and TSS 

concentrations compared with concentrations during larger 

flows. The RPD in eight replicate samples collected during 

low flow exceeded 20 percent when the SS or TSS concentrations were at or near the LRL. The RPD in split pairs of 

samples collected at station 08067650 ranged from 0 to 126 

percent for SS and TSS, with a mean RPD of 27 percent. The 

maximum RPD was for the SS concentration in a split pair 

collected May 13, 2009; the SS concentration was 7.0 mg/L in 

the environmental sample and 31 mg/L in the replicate sample. 

The environmental SS concentration was determined acceptable because it was similar to the TSS concentrations; TSS 

was 6.8 mg/L in the environmental sample and 7.2 mg/L in the 

replicate sample. The replicate sample SS concentration for 

the May 13, 2009, sample was determined erroneous, likely 

because of variance between the SS sample pairs introduced 

during sample processing. The water sample was split into two 

sets of containers using a churn splitter, and nonrepresentative suspended-material samples can result from inadequate 

churning during sample processing (U.S. Geological Survey, 

variously dated; Wilde and others, 2004 with updates through 

2009). The May 13, 2009, SS replicate sample was likely the 

last sample collected, and sample volume was likely insufficient to adequately churn the volume that was removed from 

the churn for this sample. For split samples collected at station 

08068000, the RPDs for SS and TSS concentrations ranged 

from 0 to 157 percent, with a mean RPD of 23 percent. The 

maximum RPD was for the SS concentration in a split pair 

collected June 1, 2009. The SS concentration was 10 mg/L in 

the environmental sample and 84 mg/L in the replicate sample. 

As in the case of the May 13, 2009, sample, the environmental SS concentration was determined acceptable, and the 

split sample concentration was determined erroneous, likely 

because of the same problems that affected the May 13, 2009, 

replicate sample SS concentration at station 08067650. The 

May 13, 2009, and June 1, 2009, environmental concentrations 

were included in the dataset used to develop the SS concentration regression equations.

Field blanks were used to assess whether any contamination was introduced into the environmental samples during 

sampling and analysis. Field blanks were collected by passing 

deionized water through sampling equipment in the field. Field 

blanks were collected and processed at sampling sites prior to 

the collection of environmental samples. Equipment blanks 

were processed or collected at the Gulf Coast Program Office 

laboratory to determine if all applicable cleaning procedures 

for sample splitters and the equipment used for sample collection were adequate to produce samples free of equipmentrelated contamination. Field and equipment blanks were each 

collected twice during the study period and analyzed for SS 

and TSS concentrations. The field and equipment blanks collected in March 2009 had SS and TSS concentrations less than 

their respective LRL. The second set of field and equipment 

blanks collected in August 2009 had SS concentrations equal 

to the LRL of 1.0 and TSS concentrations less than the LRL 

(table 3). 

Regression Methods

S–PLUS statistical software (TIBCO Software Inc., 

2008) was used to develop multiple linear regression equation models for estimating real-time SS and TSS concentrations. The regression equation methods used in this report are 

described by Helsel and Hirsch (2002) and Rasmussen and 

others (2009). 

Streamflow and Water-Quality 

Properties in the West Fork San 

Jacinto River Basin 

Streamflow at station 08067650 historically has ranged 

from no flow on numerous days to 56,000 ft3/s recorded in 

October 1994 (U.S. Geological Survey, 2010b). Instantaneous 

streamflow for samples collected at station 08067650 ranged 

from 0.30 ft3/s on August 6, 2009, to 2,230 ft3/s on September 

23, 2008 (table 2). About 75 percent of the samples were collected during either normal or below-normal base-flow conditions. For example, of the 29 discrete water-quality samples 

collected at station 08067650, 23 samples were collected when 

streamflow ranged from 0.30 to 8.3 ft3/s, 2 samples when 
Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin 13

streamflow was 182 and 196 ft3/s, and 4 samples when streamflow ranged from 643 to 2,230 ft3

/s (table 2). 

Instantaneous streamflow for samples collected at station 

08068000 ranged from 16.0 ft3/s on August 6, 2009, to 7,300 

ft3/s on April 21, 2009 (table 2). Since installation of station 

08068000 in 1939, streamflow has ranged from an annual 

7-day minimum (lowest mean value for any 7-consecutiveday period in 1 year) of 11.0 ft3

/s on August 18, 1981, to a 

maximum peak flow of 115,000 ft3/s on October 18, 1994 

(U.S. Geological Survey, 2010b). Of the 27 discrete waterquality samples collected at station 08068000, 21 samples 

were collected when streamflow ranged from 16.0 to 182 ft3/s, 

2 samples when streamflow was 669 and 1,460 ft3/s, and 4 

samples when streamflow ranged from 2,800 to 7,300 ft3/s 

(table 2). 

At stations 08067650 and 08068000, streamflow amounts 

were relatively small most days of the study period compared with historical averages (daily mean streamflows) for 

the period of record (August 1972–August 2009 for station 

08067650; July 1939–August 2009 for station 08068000). 

To illustrate the relatively low flows during much of the 

study, historical mean monthly streamflow for 1983–2009 is 

compared with the monthly mean streamflow for the study 

period (July 2008–August 2009) at station 08068000 (fig. 5). 

Table 3. Suspended-sediment and total suspended-solids concentrations in field and equipment blank samples from sites in the West 

Fork San Jacinto River Basin, Texas, July 2008–August 2009. 

[mg/L, milligrams per liter; FB, field blank; <, less than; EB, equipment blank]

Site name U.S. Geological 

Survey site number Date Sample type

Suspendedsediment 

concentration

(mg/L)

Total 

suspendedsolids 

concentration

(mg/L)

West Fork San Jacinto River near Conroe, Tex. 08068000 3/9/2009 FB <0.5 <1.0

Gulf Coast Program Office laboratory, The Woodlands, Tex. 301056095265000 3/9/2009 EB <.5 <1.0

West Fork San Jacinto River near Conroe, Tex. 08068000 8/6/2009 FB 1.0 <1.0

Gulf Coast Program Office laboratory, The Woodlands, Tex. 301056095265000 8/6/2009 EB 1.0 <1.0

Figure 5. Comparison of historical mean monthly streamflow (1973–2009) and monthly mean streamflow (July 2008–August 2009) at 

station 08068000 West Fork San Jacinto River near Conroe, Texas. 

0

200

400

600

800

1,000

1,200

1,400

1,600

DISCHARGE, IN CUBIC FEET PER SECOND

JULY AUG SEPT OCT NOV DEC JAN FEB MAR APR MAY JUNE JULY AUG

2008 2009

MONTH

Historical mean monthly discharge, 1973–2009 Study period monthly mean discharge, July 2008–August 2009
14 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

Because of the persistent low flow during much of the study, 

relatively few samples were collected when instantaneous 

streamflow was representative of the middle and upper 

ranges of historical daily mean streamflows. Streamflow and 

sample collection during July 2008–August 2009 at stations 08067650 and 08068000 are shown in figs. 4A and 4B, 

respectively. 

All streamflow measurements and discrete waterquality data for samples collected at the two main monitoring 

sites, stations 08067650 and 08068000, are listed in table 2; 

streamflow measurements and discrete water-quality data for 

samples collected at the five additional monitoring sites 

are listed in table 4. Streamflow, SS, TSS, and water-quality 

property data for all stations are stored in the NWIS database 

(U.S. Geological Survey, 2010b) in accordance with USGS 

protocols (U.S. Geological Survey, 2006b). 

Summary statistics for selected water-quality data for 

samples collected at stations 08067650 and 08068000 are 

listed in table 5. The maximum SS and TSS concentrations 

at station 08067650 (180 and 133 mg/L, respectively) were 

in the April 19, 2009, sample (table 2). The instantaneous 

streamflow for the April 19, 2009, sample at station 08067650 

(1,220 ft3/s) was the third largest associated with a discrete 

sample. Except for the SS concentration of 180 mg/L, all other 

SS concentrations at station 08067650 were less than 75 mg/L. 

SS concentrations of 25 mg/L or less were measured in 26 

of 29 (90 percent) environmental samples collected at this 

station, and TSS concentrations of 25 mg/L or less were 

measured in 25 of 28 environmental samples. Median SS 

and TSS concentrations were 7.0 and 7.6 mg/L, respectively 

(table 5).

At station 08068000, the maximum SS concentration 

of 1,270 mg/L was in the April 19, 2009, sample; instantaneous streamflow was 4,200 ft3

/s, the third largest for a 

discrete sample collected at this station (table 2). SS concentrations were 25 mg/L or less in 16 of 27 environmental 

samples, and TSS concentrations were 25 mg/L or less in 18 

of 26 environmental samples. The maximum TSS concentration at station 08068000 was 268 mg/L in the September 18, 

2008 sample; instantaneous streamflow was 669 ft3/s, the sixth 

largest for a discrete sample at this station. Median SS and 

TSS concentrations were 18.0 and 14.0 mg/L, respectively 

(table 5).

The largest streamflows were generally associated with 

the largest SS and TSS concentrations, and these tended to 

occur in response to large rainfall events. The September 2008 

samples were collected during a month when the total rainfall 

was 10.13 in., compared with the normal (mean monthly)

rainfall (1983–2009) of 4.46 in. at National Weather Service 

station 411956 in Conroe (fig. 3), mostly because of a few 

large storm events associated with Hurricane Ike, which made 

landfall near Galveston, Tex., about 50 miles (mi) southeast of 

Houston, on September 13, 2008. 

Of the five samples collected during September 2008 at 

station 08067650 (table 2), three were collected when streamflow was low (0.90 to 8.3 ft3

/s); SS concentrations for these 

samples ranged from 13 to 23 mg/L and TSS concentrations 

for two samples were 9.4 and 10.4 mg/L (TSS sample not 

collected on September 16, 2008). Two samples were collected during September 19–23, 2008, when streamflow, 

elevated because of Hurricane Ike, ranged from 2,200 ft3/s 

(September 19, 2008) to 2,230 ft3/s (September 23, 2008). 

SS concentrations were 45 and 73 mg/L and TSS concentrations were 26.8 and 45.0 mg/L on September 19 and 23, 2008, 

respectively. 

Of the four samples collected during September 2008 at 

station 08068000 (table 4), one was collected when streamflow was low (19 ft3

/s); the SS and TSS concentrations for this 

sample were 6.0 and 4.2 mg/L, respectively. Three samples 

were collected during September 16–23, 2008, when streamflow ranged from 669 to 2,800 ft3

/s. SS concentrations ranged 

from 33 to 147 mg/L during September 16–23, 2008; TSS 

concentrations were 68.7 and 268 mg/L on September 23 and 

18, 2008, respectively.

Similar to the September 2008 samples, the April 2009 

samples were collected when rainfall greatly exceeded the 

mean monthly rainfall (1983–2009) at National Weather 

Service station 411956 in Conroe; the total rainfall in April 

2009 was 9.24 in. compared with the normal (mean monthly 

1983–2009) of 3.85 in. (fig. 3). Similar to the patterns 

observed during September 2008, the largest SS and TSS 

concentrations were measured when streamflow was relatively 

large. 

To help preliminary characterization of streamflow 

and water quality in the West Fork San Jacinto River Basin, 

water-quality data were also collected at five additional 

monitoring sites established for the study: stations 08067652 

White Oak Creek at Memorial Drive, Conroe, Tex. (White 

Oak Creek); 08067653 West Fork San Jacinto River at Farm 

Road (FM) 2854 near Conroe, Tex. (West Fork FM 2854); 

08067657 Alligator Creek at Sergeant Ed Holcomb Road, 

Conroe, Tex. (Alligator Creek); 08067800 Lake Creek at FM 

149 near Karen, Tex. (Lake Creek at FM 149); and 08067900 

Lake Creek near Conroe, Tex. (Lake Creek Conroe). Waterquality data for the five additional monitoring sites are listed 

in table 4. The median SS and TSS concentrations were 54.0 

and 14.0 mg/L at White Oak Creek; 14.0 and 13.0 mg/L at 

West Fork FM 2854; 17.0 and 13.0 mg/L at Alligator Creek; 

26.0 and 12.0 mg/L at Lake Creek FM 149; and 39.0 and 11.0 

mg/L at Lake Creek Conroe, respectively (table 5). The maximum SS and TSS concentrations for the five additional monitoring sites were 3,110 and 390 mg/L, respectively (table 5) at 

White Oak Creek on April 28, 2009 (table 4). The minimum 

SS concentration was 5.0 mg/L at Alligator Creek on April 8, 

2009, and the minimum TSS concentration was 1.0 mg/L at 

West Fork FM 2854 on July 20, 2009.
Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin 15

Table 4. Water-quality data in samples from five additional monitoring sites (stations 08067652, 08067653, 08067657, 08067800, and 08067900) in the West Fork San Jacinto River Basin, Texas, July 2008–August 2009.[ft3/s, cubic feet per second; °C, degrees Celsius; μS/cm, microsiemens per centimeter at 25 degrees Celsius; mg/L, milligrams per liter; FNU, Formazin nephelometric units; >, greater than; mm, millimeter; ER, environmental routine; EE, environmental event; --, sample not collected]

U.S. Geological 

Survey station name

U.S. Geological Survey 

station number 

(fig. 1)

Texas 

Commission on 

Environmental 

Quality station 

number

Date Time Sample type Discharge (ft3/s)

Water 

temperature 

(°C)

Specific 

conductance 

(μS/cm)

pH 

(standard 

units)

Dissolved 

oxygen 

concentration

(mg/L)

Turbidity 

(FNU)1

Suspendedsediment 

concentration

(mg/L)

Total 

suspendedsolids 

concentration

(mg/L)

Sand/fine 

separation 

(percent 

>0.0625 

mm)

White Oak Creek at 

Memorial Drive, Conroe, Tex.

08067652 20731 3/16/2009 1408 ER 7.6 18.5 184 7.4 9.2 17.9 37 14.0 51

4/8/2009 1308 ER 6.4 17.8 189 7.5 9.4 10.2 26 10.0 454/19/2009 1443 EE 21.1 21.6 135 7.0 8.1 120 385 114 394/28/2009 1353 EE 40.0 21.6 63 6.5 8.3 1,232 3,110 390 325/27/2009 1443 ER 6.5 23.5 195 7.5 8.1 19.1 54 17.0 826/22/2009 1355 ER 3.7 28.4 163 7.8 7.7 15.8 139 12.5 9.07/20/2009 1450 ER 3.8 27.6 191 7.7 7.7 8.2 14 10.0 72

West Fork San Jacinto 

River at FM 2854 near Conroe, Tex.

08067653 11250 12/19/2008 1205 ER 12.5 17.3 264 7.6 9.6 8.9 10 9.5 95

3/18/2009 1348 ER 11.7 19.9 266 7.5 9.1 16.9 18 18.0 994/28/2009 1425 EE 680 21.0 182 7.4 8.3 488 714 56.0 765/27/2009 1243 ER 9.9 24.3 261 7.5 7.3 12.8 43 13.0 996/22/2009 1455 ER 4.7 30.0 252 7.8 7.8 8.0 8.0 13.0 997/20/2009 1340 ER 5.0 28.0 252 8.2 9.3 6.7 9.0 1.0 96

Alligator Creek at Sergeant Ed Holcomb 

Road. Conroe, Tex.

08067657 20732 3/16/2009 1238 ER 1.6 16.7 296 7.9 9.6 29.3 17 15.0 98

4/8/2009 1438 ER .15 22.3 448 8.2 11.3 6.0 5.0 3.0 904/19/2009 1600 EE 4.4 25.0 262 7.7 7.7 58.7 68 27.0 1004/28/2009 1245 EE 693 22.0 84 7.7 8.3 216 450 66.0 165/28/2009 0840 ER .18 24.0 478 7.9 6.8 11.6 7.0 6.0 886/22/2009 1315 ER .014 31.1 634 8.7 8.6 14.3 15 11.5 977/21/2009 1050 ER .16 28.3 569 8.4 6.7 17.5 17 13.0 97

Lake Creek at FM 149 

near Karen, Tex.

08067800 18191 11/28/2009 1020 EE 22.5 14.9 684 7.6 8.5 21.1 26 28.0 98

3/18/2009 1020 ER 22.5 14.9 684 7.6 8.5 21.1 26 28.0 984/8/2009 1010 ER 6.7 15.1 520 7.6 8.0 13.2 16 12.0 974/19/2009 1048 EE 6,360 18.9 157 7.0 4.9 49.2 165 82.0 524/30/2009 1303 EE 1,630 23.7 164 7.1 4.3 40.7 90 -- 875/27/2009 0950 ER 2.5 25.6 452 7.4 5.9 8.9 55 12.0 976/23/2009 1008 ER .01 28.1 562 7.5 4.1 3.0 6.0 8.5 937/20/2009 1110 ER .31 27.4 314 7.5 4.3 9.7 14 12.0 98

Lake Creek near Conroe, Tex. 08067900 11367 11/28/2008 1118 EE 18.2 16.8 253 7.4 8.6 8.6 218 7.6 5.0 12/19/2008 0923 ER 14.8 14.8 276 7.5 9.1 6.1 7.0 11.0 91

3/16/2009 1603 ER 77.9 14.5 557 7.8 9.9 30.0 39 42.5 964/30/2009 1120 EE 1,320 23.0 134 7.2 4.9 34.3 203 -- 305/27/2009 1500 ER 9.1 25.8 289 7.5 7.5 10.9 46 9.5 986/23/2009 1135 ER 2.7 29.0 228 7.4 6.2 9.2 13 11.0 967/21/2009 0930 ER 3.5 27.9 221 7.5 5.7 17.7 21 13.0 94

1 Determined by shining an incident beam of light into a parcel of water and measuring the reflected light at an angle of 90 degrees to the incident light.
16 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas Table 5. Summary statistics for selected water-quality data in samples from monitoring sites in the West Fork San Jacinto River Basin, Texas, July 2008–August 2009. [n, number of samples; FM, Farm Road; ft3/s, cubic feet per second; FNU, Formazin nephelometric units; mg/L, milligrams per liter] Property or constituent Main monitoring sites Additional monitoring sites 08067650 West Fork San Jacinto River below Lake Conroe near Conroe, Tex. (n = 29) 08068000 West Fork San Jacinto River near Conroe, Tex. (n = 27) 08067652 White Oak Creek at Memorial Drive, Conroe, Tex. (n = 7) 08067653 West Fork San Jacinto River at FM 2854 near Conroe, Tex. (n = 6) Maxi- mum Mini- mum Mean Median Maxi- mum Mini- mum Mean Median Maxi- mum Mini- mum Mean Median Maxi- mum Mini- mum Mean Median Discharge (ft3/s) 2,230 0.30 232 2.0 7,300 16.0 877 61.0 40.0 3.7 12.7 6.5 680 4.7 121 10.8 Turbidity (FNU) 36.2 1.3 6.68 4.4 226 1.8 33.5 11.5 1,232 8.2 203 17.9 488 6.7 90.3 10.9 Suspended-sediment concentration (mg/L) 180 2.0 17.9 7.0 1,270 6.0 140 18.0 3,110 14.0 538 54.0 714 8.0 134 14.0 Total suspended-solids concentration (mg/L)

1133 13.0 114.2 17.6 2268 21.5 252.7 214.0 390 10.0 81.1 14.0 56.0 1.0 18.4 13.0

Property or constituent

Additional monitoring sites—Continued

08067657 Alligator Creek at 

Sergeant Ed Holcomb Road, 

Conroe, Tex. 

(n = 7)

08067800 Lake Creek at 

FM 149 near Karen, Tex. 

(n = 8)

08067900 Lake Creek 

near Conroe, Tex. 

(n = 7)

Maximum Mini- mum Mean Median Maxi- mum Mini- mum Mean Median Maxi- mum Mini- mum Mean Median

Discharge (ft3/s) 693 0.015 99.9 0.18 6,360 0.01 1,006 14.6 1,320 2.7 207 14.8Turbidity (FNU) 216 6.0 50.4 17.5 49.2 3.0 20.8 17.2 34.3 6.1 16.7 10.9Suspended-sediment 

concentration (mg/L)

450 5.0 82.7 17.0 165 6.0 49.8 26.0 218 7.0 78.1 39.0

Total suspended-solids 

concentration (mg/L)

66.0 3.0 20.2 13.0 82 8.5 26.1 12.0 42.5 7.6 15.8 11.0

1 n = 28. 2 n = 26.
Regression Models to Estimate Real-Time Suspended-Sediment and Total Suspended-Solids Concentrations and Loads 17

Regression Models to Estimate RealTime Suspended-Sediment and Total 

Suspended-Solids Concentrations and 

Loads 

As explained by Anderson and Rounds (2010), the basic 

form of regression models to estimate real-time SS and TSS 

concentrations by using data from continuous monitors and 

periodic water-quality samples is described by equation 2:

y = f(X1, X2, ..., Xn), (2)

where

y isthe dependent variable;

X1, X2, ..., Xn are explanatory variables; and

f( ) is a notation that indicates y is a function of 

the indicated explanatory variables.

The primary explanatory variables (potential predictive variables) for regression models to estimate real-time SS 

and TSS concentrations and loads evaluated for this report 

included streamflow (discharge), water temperature, specific conductance, pH, dissolved oxygen, and turbidity. The 

purpose of the predictive variables was to provide a means 

to estimate the dependent variable (Oden and others, 2009), 

which for this report were either SS or TSS concentration, the 

primary variables evaluated. Each regression equation can be 

used to estimate concentrations in “real time” on the basis of 

the predictive variables measured continuously by the waterquality monitors. Instantaneous loads can also be estimated 

by multiplying the estimated concentration by the corresponding streamflow value and applying a conversion factor. An 

adequate model-calibration dataset consists of an appropriate 

number of instantaneous, discrete samples analyzed for SS and 

TSS, as well as concurrent turbidity and streamflow measurements collected throughout the observed range of hydrologic conditions for the period of record (Glysson, 1989b; 

Rasmussen and others, 2009).

In addition to concurrent measurements of turbidity 

and streamflow, other variables such as stream stage, rates of 

streamflow rise and fall, rainfall rates and intensity, seasonality, sediment sources, and land use are useful for estimating 

SS and TSS concentrations and loads in streams (Rasmussen 

and others, 2009). All these potential predictive variables, as 

they relate to response variables (SS and TSS concentrations), 

were evaluated, but not all are included in this report. The 

evaluation included the comparison of all measured real-time 

water-quality data constituents as they relate to SS and TSS 

data. Each potential predictive variable was plotted in relation 

to a response variable in a scatter plot to help identify variables that can be used as surrogates for estimating SS and TSS 

loads. For example, when two variables such as turbidity and 

SS correlate, the presence of one might be useful for predicting the presence of the other.

Data transformations were done prior to developing 

the regression equations to make the residuals more symmetric, linear, and homoscedastic. Measured and predictive 

variables will ideally plot on a straight regression line and 

display homoscedasticity (constant variance about the regression line) (Gray and others, 2000). Normally distributed 

predictive and response variables (bell-shaped continuous 

probability distribution centered on a mean) with linear 

relations and constant variance are required for statistically 

valid multiple linear regression applications (Oden and 

others, 2009). Initial scatter plots of all possible predictive 

and response variables indicated that a transformation of 

the data would be necessary to improve linearity and compensate for non-normality. For water-quality data, logarithmic 

transformations of the predictive and response variables 

are frequently used to improve linearity and to compensate 

for non-normality and heteroscedasticity in model residuals 

(Oden and others, 2009). Because of the limited duration of 

the study (14 months) and relatively small dataset, the relations between measured and predictive variables were less 

than optimal and the data allowed only a few possible 

transformations to improve the symmetry, linearity, and 

homoscedasticity of predicted and response variable relations. Comparisons of linear (untransformed) and log transformed data are presented for station 08068000 to demonstrate how the transformations improved the computation 

of SS and TSS concentrations (figs. 6 and 7). The improved 

linearity of the relation between turbidity and streamflow for 

station 08068000 gained by log transformation is shown in 

figure 8.

The transformation that provided the most linear fit 

was accomplished by transforming the predictive and response 

variables by using a base-10 logarithmic (log10) transformation; log10 transformations were used successfully to normalize 

the residuals of estimated SS and TSS concentrations. The 

constant variance throughout the range of estimated SS and 

TSS concentrations, the normality of the residuals, and the 

vertically centered linear pattern (near the zero residual line) 

supported the selection of a log10 transformation. 

Once the optimal transformations are selected for the 

predictive and response variables, different combinations 

of predictive variables were evaluated to develop a best-fit 

multiple linear regression model of the form:

y = aX1 + bX2 + ... mXn+ Є, (3)

where

a, b, ... m are regression coefficients; 

Є	 is an error term, or intercept; and

X1, X2, ..., Xn are explanatory variable as described in 

equation 2 (Anderson and Rounds, 

2010).

Of the potential predictive variables (streamflow, water 

temperature, specific conductance, pH, dissolved oxygen concentration, and turbidity), only streamflow, water temperature, 

specific conductance, and turbidity were rigorously evaluated 
18 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

Figure 6. Relation between suspended-sediment concentration (in linear space) and (A) turbidity and (B) streamflow and between suspended-sediment concentration (in log10 space) and (C) turbidity and (D) streamflow for station 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–August 2009.

0 50 100 150 200

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

0

800400

1,200

SUSPENDED-SEDIMENT CONCENTRATION,

 IN MILLIGRAMS PER LITER

Pearson’s r correlation coefficient = .58

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

STREAMFLOW, IN CUBIC FEET PER SECOND

0

800400

1,200

SUSPENDED-SEDIMENT CONCENTRATION, 

 IN MILLIGRAMS PER LITER

Pearson’s r correlation coefficient = .76

0 0.5 1.0 1.5 2.0 2.5

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

0.51.01.52.02.53.0

, SUSPENDED-SEDIMENT CONCENTRATION

 IN MILLIGRAMS PER LITER

1.0 1.5 2.0 2.5 3.0 3.5 4.0

STREAMFLOW, IN CUBIC FEET PER SECOND

0.51.01.52.02.53.0

SUSPENDED-SEDIMENT CONCENTRATION,

R IN MILLIGRAMS PER LITE

Pearson’s r correlation coefficient = .86

Pearson’s r correlation coefficient = .82

A. B.C. D.
Regression Models to Estimate Real-Time Suspended-Sediment and Total Suspended-Solids Concentrations and Loads 19 Figure 7. Relation between total suspended-solids concentration (in linear space) and (A) turbidity and (B) streamflow and between total suspended-solids concentration (in log10 space) and (C) turbidity and (D) streamflow for station 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–August 2009. 

0 50 100 150 200

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

0

50

250200150100

TOTAL SUSPENDED-SOLIDS CONCENTRATION, 

IN MILLIGRAMS PER LITER

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

STREAMFLOW, IN CUBIC FEET PER SECOND

0

50

100150200250

, CONCENTRATION TOTAL SUSPENDED-SOLIDS

IN MIILIGRAMS PER LITER

Pearson’s r correlation coefficient = .69 Pearson’s r correlation coefficient = .55

A. B.

0 0.5 1.0 1.5 2.0 2.5

0.0

5

1.01.52.02.5

1.0 1.5 2.0 2.5 3.0 3.5 4.0

0.0

5

1.01.52.02.5

TOTAL SUSPENDED-SOLIDS CONCENTRATION, 

IN MILLIGRAMS PER LITER

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

, CONCENTRATION TOTAL SUSPENDED-SOLIDS

IN MIILIGRAMS PER LITER

STREAMFLOW, IN CUBIC FEET PER SECOND

Pearson’s r correlation coefficient = .71 Pearson’s r correlation coefficient = .57

C. D.
20 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

as potential predictive variables; previous studies have established the relation of these variables to SS concentrations and 

loads (Oden and others, 2009; Rasmussen and others, 2009; 

Anderson and Rounds, 2010). 

The coefficient of determination (R2) describes the 

proportion of the total sample variability in the response 

explained by the regression model. The coefficient will only 

increase as additional explanatory variables are added to 

the model; thus, it might not be an appropriate criterion for 

determining the usefulness of a model that has numerous 

explanatory variables (Helsel and Hirsch, 2002). The adjusted 

R2 (R2

a

) statistic compensates for this by assessing a “penalty” 

for the number of explanatory variables in the model; adding 

additional explanatory variables increases the value of R2

a

only when the predictive capability of the model increases. 

Choosing a model with the largest R2

a

 is equivalent to choosing a model with the lowest mean standard error (Helsel and 

Hirsch, 2002). Review of the transformed data scatter plots 

and R2

a

 values determined that water temperature and specific 

conductance, as predictive variables, had little relation to 

either SS or TSS concentrations and subsequently were not 

used in the regression equations.

Multiple Linear Regression Model Results

In this study, the predictive variables that provided the 

most accurate surrogate relation for estimating SS and TSS 

concentrations were turbidity and streamflow (discharge). The 

data for these two predictive variables yielded the largest R2

a

values and provided the best-fit linear regression model. R2

a

values at station 08067650 were .8815 and .695 for SS and 

TSS concentrations, respectively, and R2

a

 values at station 

08068000 were .819 and .7906 for SS and TSS concentrations, 

respectively (table 6). A multiple linear regression (MLR) 

model was developed by using turbidity and streamflow as 

these properties relate to SS and TSS concentrations. The 

following regression analysis is site specific and applies to an 

MLR model. 

Equations for the MLR models relating turbidity and 

streamflow data to estimate SS and TSS concentrations for stations 08067650 (equations 4 and 5) and 08068000 (equations 

6 and 7) for data collected during July 2008–August 2009 

are shown below. Basic model information, regression coefficients, model diagnostics, and Duan’s bias correction factor 

(Duan, 1983) are listed in table 6; a summary of the regression 

analysis for each MLR model is provided in appendix 1. 

Equations 4 and 5 were the best-fit regression equations 

for estimating SS concentration (SSC) and TSS concentration, 

respectively, at station 08067650:

log10 (SSC) = 0.7711*log10 (Turbidity) + 0.1760 

*log10 (Streamflow) + 0.2968, and (4)

log10 (TSS) = 0.5525*log10 (Turbidity) + 0.1394

*log10 (Streamflow) + 0.4621. (5) 

Equations 6 and 7 were the best-fit regression equations 

for estimating SS and TSS concentrations, respectively, at station 08068000:

log10 (SSC) = 0.7350*log10 (Turbidity) + 0.3436

*log10 (Streamflow) - 0.0677, and (6)

log10 (TSS) = 0.8140*log10 (Turbidity) + 0.2262

*log10 (Streamflow) - 0.1252. (7)

Figure 8. Relation between (A) streamflow and turbidity (in linear space) and (B) streamflow and turbidity (in log10 space) for station 

08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–August 2009. 

0 50 100 150 200

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

0

2,000

4,000

6,000

STEAMFLOW, IN CUBIC FEET PER SECOND

A.

0 0.5 1.0 1.5 2.0 2.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

STEAMFLOW, IN CUBIC FEET PER SECOND

B.
Regression Models to Estimate Real-Time Suspended-Sediment and Total Suspended-Solids Concentrations and Loads 21

Evaluation of Regression Models

A series of evaluations were done to evaluate accuracy 

and validity of the regression models. First, correlation coefficients were determined. Correlation coefficients measure 

the strength of association between two variables (Helsel 

and Hirsch, 2002). The most commonly used correlation is 

Pearson’s r. This correlation is also called the linear correlation 

coefficient because r measures the linear association between 

two variables (Helsel and Hirsch, 2002). When there is no 

correlation between two variables, r = 0. Generally, the closer 

the correlation coefficient is to 1 (perfect positive correlation), 

the stronger the association between variables (Rasmussen 

and others, 2009). For example, at station 08068000 the correlation coefficients for each equation ranged from .55 to .86 

(figs. 6 and 7). The relation between measured or estimated 

SS concentrations and turbidity by using the best-fit regression 

model for station 08068000 is shown in figure 9A; the relation between measured or estimated TSS concentrations and 

turbidity is shown in figure 9B.

Because the regression equation models developed during 

this study contained two predictive variables, an evaluation 

of multicollinearity was done to ensure that the predictive 

variables were not highly correlated with each other, which 

can result in unreliable models (Rasmussen and others, 2009). 

Helsel and Hirsch recommend computing a variance inflation factor (VIF) as a means of measuring multicollinearity. 

A VIF larger than 10 indicates multicollinearity between 

variables and that either variable would explain about the same 

amount of variability and that the two variables combined 

should not be used as predictive variables in a MLR model 

(Helsel and Hirsch, 2002). The scatter plots (fig. 8) and VIF 

values (table 6) for turbidity and streamflow data indicate that 

the multicollinearity of these variables is small and that both 

variables can be used as predictive variables in a MLR model 

to estimate SS and TSS concentrations. The VIF values for the 

MLR models developed ranged from 1.68 to 2.02. 

The root-mean-square error (RMSE) also was used to 

evaluate the regression models. The RMSE is the measure 

of the variance between observed and regression-computed 

values (Rasmussen and others, 2009). Minimizing the RMSE 

is one of the criteria for selecting predictive variables: 

“Methods whose estimates are closer to the true value have 

lower RMSEs, and are considered better” (Helsel and Hirsch, 

2002, p. 358). Another tool for evaluating regression equation models is an evaluation tool derived from the RMSE, the 

prediction error sum of squares (PRESS). The PRESS statistic 

is one of the best measures of the quality of a regression 

model (Helsel and Hirsch, 2002). An equation that produces 

the least error when making new predictions is obtained by 

minimizing the PRESS statistic (Helsel and Hirsch, 2002). A 

summary of linear regression evaluation statistics (RMSE and 

PRESS statistic) for the regression equations to estimate SS 

and TSS concentrations for stations 08067650 and 08068000 

is provided (table 6). The RMSE computed for each regression equation ranged from 0.147 to 0.282, which were higher 

than desired and indicated appreciable variance between the 

measured and regression-computed values. The PRESS statistic for each equation developed ranged from 0.562 to 1.91, 

indicating that a large amount of error was generated in the 

results derived from the equations.

The measure of variance (model residuals) between 

observed values and values predicted (estimated) by the 

regression model provides another diagnostic tool to evaluate 

the accuracy of regression models. Model residuals are defined 

as the difference between the observed values and the model 

estimated values. The residual error (ei) for the estimated 

SS and TSS concentrations should follow a normal distribution with a mean of zero and a constant variance (Helsel and 

Hirsch, 2002). A residual value of zero indicates that the 

Table 6. Summary of linear regression evaluation statistics for equations using turbidity and streamflow as predictive variables to 

estimate suspended-sediment and total suspended-solids concentrations at two main monitoring sites (stations 08067650 and 08068000) 

in the West Fork San Jacinto River Basin, Texas, July 2008–August 2009.

Evaluation statistic 

08067650 West Fork San Jacinto River 

below Lake Conroe near Conroe, Tex.

08068000 West Fork San Jacinto 

River near Conroe, Tex.

Suspended 

sediment

Total suspended 

solids

Suspended 

sediment

Total suspended 

solids

Number of measurements 29 28 27 26

Adjusted coefficient of determination (R2a) .8815 .695 .819 .7906

Variance inflation factor (VIF) 2.02 1.68 2.02 1.93

Root mean-square error (RMSE) .147 .196 .282 .282

Prediction error sum of squares (PRESS) .562 .960 1.91 1.83

90-percent prediction interval ±48.9 percent ±43.2 percent ±47.7 percent ±43.2 percent

Duan’s bias correction factor (BCF) (Duan, 1983) 1.05 1.05 1.23 1.17
22 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

model estimated SS or TSS concentrations is equal to the 

observed value. A positive value indicates that the observed 

value was larger than the estimated value, and a negative 

value indicates that the observed value was less than the 

estimated value (Rasmussen and others, 2009). The normality of the residuals was evaluated by plotting the residuals on 

a normal-probability plot and computing the probability plot 

correlation coefficient (PPCC) (figs. 10 and 11). Normally 

distributed residuals are linear and equally distributed over 

a normal-probability plot and have a larger PPCC compared 

with non-normally distributed residuals. The probability plots 

and PPCC values for the regression equations indicate that 

Figure 9. Relation between (A) measured or estimated suspended-sediment concentration and turbidity and (B) measured or 

estimated total suspended-solids concentration and turbidity at station 08068000 West Fork San Jacinto River near Conroe, Texas, July 

2008–August 2009. 

0 0.5 1.0 1.5 2.0 2.5

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

0.2

0.7

1.2

1.7

2.2

2.7

3.2

Measured suspended-sediment concentration

Estimated suspended-sediment concentration

SUSPENDED-SEDIMENT CONCENTRATION,  IN MILLIGRAMS PER LITER

0 0.5 1.0 1.5 2.0 2.5

TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

0

0.5

1.0

1.5

2.0

2.5

Measured total suspended-solids concentration

Estimated total suspended-solids concentration

TOTAL SUSPENDED-SOLIDS CONCENTRATION, 

IN MILLIGRAMS PER LITER

A.

B.
Regression Models to Estimate Real-Time Suspended-Sediment and Total Suspended-Solids Concentrations and Loads 23

the log10 transformations (equations 4–7) resulted in residuals 

that meet all these criteria. The PPCC values for the regression 

equations that were developed to estimate SS and TSS concentrations and loads ranged from 0.9892 to 0.9928, close to the 

maximum possible PPCC value (1.0). 

To assess the uncertainty of the regression-computed 

SS and TSS concentrations, prediction intervals were computed for all data generated in this study. Prediction intervals 

can be used to evaluate the uncertainty of regression-computed 

SS and TSS concentrations (Helsel and Hirsch, 2002). For a 

given predictive variable, the 90-percent prediction interval 

represents a range of values within which there is a 90-percent 

certainty that the true SS or TSS concentration occurs. The 

prediction interval for a single response (yi) is approximately:

E(yi) +/- t x s, (8)

where

E(yi) is the regression-computed value, at xi;

t is the value of the student’s t distribution having 

n-2 degrees of freedom (n is the number of 

observations) with the exceedance probability 

of α/2 (alpha value obtained from student’s 

t distribution tables in the appendix of most 

statistics textbooks (for example, Iman and 

Conover, 1983, appendix A, p. 438–439) 

for 90-percent prediction interval α = 0.10; 

and 

s is the standard error of regression or the RMSE 

(Rasmussen and others, 2009).

The larger the 90-percent prediction interval, the more 

uncertainty there is associated with the regression-computed 

SS and TSS concentrations (Rasmussen and others, 2009). 

The 90-percent prediction intervals for station 08067650 

were ±48.9 percent for SS concentrations and ±43.2 percent 

for TSS concentrations and for station 08068000 were ±47.7 

percent for SS concentrations and ±43.2 percent for TSS 

concentrations (table 6). The 90-percent prediction intervals 

could be smaller if data were collected over a longer period of 

time compared with the 14 months of data collected for these 

analyses.

Each of the linear regression models developed to estimate SS and TSS concentrations at stations 08067650 and 

08068000 has uncertainty in the estimated values. The correlation coefficients and R2

a

 values for each equation were less 

than .90; some were much less than 1.0, the value of a perfect 

correlation and perfect R2

a

 value. The VIF values obtained 

for each equation indicated no appreciable multicollinearity 

among the predictive variables selected for each equation and 

that the selected variables would provide the most accurate 

equations possible for estimating SS and TSS concentrations and loads. The RMSE values obtained for each equation 

were higher than desired and indicated appreciable variance 

between the observed and regression-computed values. 

The main indicators of the measure of error for the values 

generated from these equations were the RMSE and PRESS 

values. 

These evaluations indicate that a large amount of error 

was generated in the results derived from the equations. It 

is important to collect data over sufficient periods of time to 

cover a wide range of streamflow to accurately model SS and 

TSS concentrations and loads; data collected over shorter periods do not replicate the range of flow during longer periods, 

and the range of flow is related to concentrations and loads 

(Uhrich and Bragg, 2003).

Suspended-Sediment and Total SuspendedSolids Concentration and Load Computation

Using a log10 transformation of the response variables 

(SS and TSS concentrations) has an undesirable effect that 

needs to be considered when computing SS and TSS concentrations and loads; the regression estimates needs to be 

retransformed to the original units, a step that introduces a bias 

in computed SS and TSS concentrations (Miller, 1951; Koch 

and Smillie, 1986) unless the data are perfectly and positively 

correlated (as the R2

a

 approaches 1.0, the bias correction factor 

[BCF] also approaches 1.0). The bias arises because regression estimates are the mean of the y given x, in log units, and 

retransformation of these estimates is not equal to the mean of 

the y given x, in linear space. To correct this retransformation 

bias, Duan (1983) introduced a nonparametric BCF called the 

“smearing” estimator (Helsel and Hirsch, 2002). The equation 

to compute the smearing BCF for log10 transformations is as 

follows:

BCF = i

n ei

n

∑ =1

10

(9)

where

n is the number of samples; and

ei is the residual in log units (Rasmussen and others, 

2009).

Regression-computed SS and TSS concentrations for 

stations 08067650 and 08068000 were corrected for bias by 

multiplying the retransformed SS and TSS concentrations by 

the BCF (Rasmussen and others, 2009). The BCFs computed 

for each regression equation in this study were larger than 1.0 

(table 6). An example of the effect of the BCF on the modelestimated SS and TSS concentrations is evident in the relation 

between measured or estimated SS concentrations and turbidity at station 08068000 (fig. 9A). The closer the data points 

plot to the 1:1 relation line, the more accurate the regression 

equations. 

A time series of SS or TSS discharge (SSQ or TSSQ) is 

computed from the estimated SS or TSS concentrations and 

the corresponding streamflow for each site. Instantaneous SSQ 

(SSQi ) is computed by using SS concentrations in the following equation: 
24 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas Figure 10. Estimated suspended-sediment concentrations (A) probability plot correlation coefficient and (B) residual plot; and estimated total suspended-solids concentrations 

(C) probability plot correlation coefficient and (D) residual plot for multiple linear regression models developed for station 08067650 West Fork San Jacinto River below Lake Conroe near Conroe, Texas, July 2008–August 2009. 

Probability plot correlation coefficient = .9927

-2 -1 0 1 2

NORMAL QUANTILES

210-1 RESIDUALS (SUSPENDEDSEDIMENT CONCENTRATION)

0 20 40 60 80 100 120

ESTIMATED SUSPENDED-SEDIMENT 

CONCENTRATION, IN MILLIGRAMS PER LITER 

-70-3

0

5

7035

RESIDUALS

-2 -1 0 1 2

NORMAL QUANTILES

210-1 RESIDUALS (TOTAL SUSPENDEDSOLIDS CONCENTRATION)

Probability plot correlation coefficient = .9928

0 10 20 30 40 50 60

ESTIMATED TOTAL SUSPENDED-SOLIDS

CONCENTRATION, IN MILLIGRAMS PER LITER

-80-4

0

0

8040

RESIDUALS

A. B.

C. D.
Regression Models to Estimate Real-Time Suspended-Sediment and Total Suspended-Solids Concentrations and Loads 25 Figure 11. Estimated suspended-sediment concentrations (A) probability plot correlation coefficient and (B) residual plot; and estimated total suspended-solids concentrations (C) probability plot correlation coefficient and (D) residual plot for multiple linear regression models developed for station 08068000 West Fork San Jacinto River near Conroe, Texas, July 2008–August 2009. -2 -1 0 1 2 NORMAL QUANTILES

210-1-2-3

RESIDUALS (SUSPENDEDSEDIMENT CONCENTRATION)

Probability plot correlation coefficient = .9903

0 100 200 300 400 500 600 700

ESTIMATED SUSPENDED-SEDIMENT

CONCENTRATION, IN MILLIGRAMS PER LITER

-100-150

-5

0

0

50

100150

RESIDUALS

-2 -1 0 1 2

NORMAL QUANTILES

210-1-2

(TOTAL SUSPENDED- RESIDUALS

SOLIDS CONCENTRATION)

Probability plot correlation coefficient = .9892

0 50 100 150 200 250

ESTIMATED TOTAL SUSPENDED-SOLIDS 

CONCENTRATION, IN MILLIGRAMS PER LITER

-100-200

0

200100

S RESIDUAL

A. B.

C. D.
26 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

SSQi = SSCi x Qi x c, (10)

where

SSQi is the computed instantaneous SSQ (computed 

instantaneous TSSQ [TSSQi]) for the ith value, in 

tons per 15 minutes;

SSCi is the computed SS concentration (computed TSS 

concentration [TSSCi]), for the ith value, in 

milligrams per liter;

Qi is the streamflow for the ith value, in cubic feet per 

second; and

c is a constant, 0.000028, to convert the units to tons per 

15 minutes. 

Turbidity and streamflow were used as predictive 

variables to establish a surrogate relation to estimate SS and 

TSS loads. Using best-fit linear regressions on log10 transformed data, adjusted by applying the appropriate BCFs for 

each corresponding equation (table 6), the SS and TSS loads 

at stations 08067650 and 08068000 were estimated from 

the turbidity and streamflow values. SSQs or TSSQs were 

computed in 15-minute intervals by multiplying estimated SS 

or TSS concentrations (in milligrams per liter), respectively, 

by the corresponding streamflow (Q), in cubic feet per second 

and a correction factor (c) to convert the units to tons per 

15 minutes (equation 10). The resulting 15-minute estimates 

were summed to determine the total daily SSQ (TSSQ). The 

monthly SSQ (TSSQ) is computed by summing each daily 

SSQ (TSSQ) for that month. The study period total SSQ 

(TSSQ) is computed by summing the monthly totals for the 

study period for stations 08067650 and 08068000 (tables 7 

and 8). A few missing streamflow or turbidity values occurred 

sporadically throughout the study period and were ignored 

in the estimated SS and TSS loads. Turbidity values for 

September 17–October 6, 2008, deemed invalid because they 

did not meet the criteria specified for the post-calibration validation process, were not included in the regression equations 

to estimate SS and TSS concentrations or the ensuing SSQ and 

TSSQ computations.

 SS or TSS loads for July 2008–August 2009 were 

estimated by summing the total daily SSQ or TSSQ for the 

study period to obtain loads in tons. The SS and TSS loads 

were about 40 times larger at station 08068000 compared with 

loads at station 08067650. Total estimated loads at station 

08067650 during July 2008–August 2009 were 3,540 tons 

for SS and 1,900 tons for TSS (table 7). Minimum monthly 

total loads at station 08067650 were 0.10 ton for SS and 

0.11 ton for TSS in January 2009; maximum monthly total 

loads were 1,790 tons for SS and 1,050 tons for TSS in April 

2009. Total estimated loads at station 08068000 during July 

Table 7. Estimated monthly and total study period (14 months) 

suspended-sediment and total suspended-solids loads computed 

from multiple linear regression models developed for station 

08067650 West Fork San Jacinto River below Lake Conroe near 

Conroe, Texas, July 2008–August 2009.

Month

Suspendedsediment load 

(tons)

Total suspendedsolids load 

(tons)

July 2008 0.36 0.44

August 2008 33.5 15.4

1

September 2008 579 236

1

October 2008 13.7 7.72

November 2008 894 436

December 2008 .61 .55

January 2009 .10 .11

February 2009 1.94 1.71

March 2009 69.3 29.8

April 2009 1,793 1,047

May 2009 151 119

June 2009 .46 .50

July 2009 .69 .57

August 2009 .53 .45

Total for study period (July 

2008–August 2009)

3,538 1,895

1

 Turbidity data for September 17–October 6, 2008, not used to compute 

estimated loads; thus, estimates could be biased low.

Table 8. Estimated monthly and total study period (14 months) 

suspended-sediment and total suspended-solids loads computed 

from multiple linear regression models developed for station 

08068000 West Fork San Jacinto River near Conroe, Texas, July 

2008–August 2009. 

Month

Suspendedsediment load 

(tons)

Total suspendedsolids load 

(tons)

July 2008 56.9 41.1

August 2008 1,475 992

1

September 2008 27,091 12,461

1

October 2008 2,860 1,680

November 2008 53,850 24,188

December 2008 288 178

January 2009 132 80.4

February 2009 95.3 61

March 2009 3,106 1,809

April 2009 60,390 27,218

May 2009 6,055 2,866

June 2009 39.3 26.5

July 2009 180 125

August 2009 446 308

Total for study period (July 

2008–August 2009) 156,064 72,036

1

 Turbidity data for September 17–October 6, 2008, not used to compute 

estimated loads; thus, estimates could be biased low.
Summary 27

2008–August 2009 were 156,000 tons for SS and 72,000 tons 

for TSS (table 8). Minimum monthly total loads at station 

08068000 were 39.3 tons for SS and 26.5 tons for TSS in June 

2009; maximum monthly total loads were 60,400 tons for SS 

and 27,200 tons for TSS in April 2009. The location of the 

stations likely explains some of the differences in SS and TSS 

concentrations and loads at the two stations. Station 08067650 

is 2.5 mi downstream from Lake Conroe, a reservoir with controlled outflow. Station 08068000 is 11 mi downstream from 

station 08067650, and several tributaries confluence with the 

West Fork San Jacinto River upstream from station 08068000 

and downstream from station 08067650. The size and depth 

of Lake Conroe should allow most SS to settle before water 

is released from the reservoir. SS and TSS loads likely were 

larger at station 08068000 than at station 08067650 because 

flow at station 08068000 is more representative of waterquality properties in the West Fork San Jacinto River Basin 

compared with flow at station 08067650, which is more 

representative of the water-quality properties of releases from 

Lake Conroe.

The estimated SS and TSS concentrations derived from 

the regression equations contained large error components, 

thus inferring that the regression-computed load estimates 

also included large errors. The SS and TSS load estimates 

were derived by using models that were less than optimal in 

terms of their accuracy; the limited accuracy likely results 

from the relatively short period of data collection (14 months) 

and relatively small range in streamflow observed during the 

study period compared with the range of streamflow typically 

observed at stations 08067650 and 08068000 during longer 

periods. 

Summary

To better understand the hydrology (streamflow and 

water quality) of the West Fork San Jacinto River Basin 

downstream from Lake Conroe near Conroe, Texas, including spatial and temporal variation of suspended-sediment (SS) 

and total suspended-solids (TSS) concentrations and loads, 

the U.S. Geological Survey (USGS), in cooperation with the 

Houston-Galveston Area Council and the Texas Commission 

on Environmental Quality, measured streamflow (discharge) 

and collected continuous and discrete water-quality data during July 2008–August 2009 in the West Fork San Jacinto River 

Basin downstream from Lake Conroe. 

Discrete samples for SS and TSS analysis were collected 

at two USGS streamflow-gaging stations on the West Fork 

San Jacinto River: West Fork San Jacinto River below Lake 

Conroe near Conroe, Texas (station 08067650) and West Fork 

San Jacinto River near Conroe, Texas (station 08068000). 

Samples were collected and streamflow measurements were 

made over the range of flow conditions during the 14-month 

study. In addition to these two main monitoring sites, discrete 

samples were also collected at five additional monitoring sites. 

Water-quality properties were characterized in a preliminary 

manner by using summary statistics of water-quality data from 

the monitoring sites. Discrete samples were collected semimonthly, regardless of flow conditions, and during periods of 

high flow resulting from storms or releases from Lake Conroe. 

The largest streamflows were generally associated with 

the largest SS and TSS concentrations, and these tended to 

occur in response to large rainfall events. The September 2008 

samples were collected during a month when the total rainfall 

was 10.13 in., compared with the normal September rainfall of 

4.46 in., mostly because of a few large storm events associated with Hurricane Ike, which made landfall near Galveston, 

Texas, on September 13, 2008. The maximum SS and TSS 

concentrations at station 08067650 (180 and 133 mg/L, 

respectively) were in the April 19, 2009 sample; instantaneous streamflow (1,220 ft3

/s) was the third largest associated 

with a discrete sample at this station. SS concentrations were 

25 mg/L or less in 26 of 29 environmental samples collected 

at station 08067650, and TSS concentrations were 25 mg/L or 

less in 25 of 28 environmental samples. Median SS and TSS 

concentrations at station 08067650 were 7.0 and 7.6 mg/L, 

respectively.

At station 08068000, the maximum SS concentration 

(1,270 mg/L) was in the April 19, 2009 sample; instantaneous 

streamflow (4,200 ft3/s) was the third largest associated with 

a discrete sample at this station. The maximum TSS concentration (268 mg/L) was in the September 18, 2008 sample; 

instantaneous streamflow (669 ft3/s) was the sixth largest associated with a discrete sample at this station. SS concentrations 

were 25 mg/L or less in 16 of 27 of environmental samples, 

and TSS concentrations were 25 mg/L or less in 18 of 26 

environmental samples at this station. Median SS and TSS 

concentrations at station 08068000 were 18.0 and 14.0 mg/L, 

respectively. 

Water-quality data were also collected at five additional 

monitoring sites established for the study: stations 08067652 

White Oak Creek at Memorial Drive, Conroe, Texas (White 

Oak Creek); 08067653 West Fork San Jacinto River at Farm 

Road (FM) 2854 near Conroe, Texas (West Fork FM 2854); 

08067657 Alligator Creek at Sergeant Ed Holcomb Road, 

Conroe, Texas (Alligator Creek); 08067800 Lake Creek at 

FM 149 near Karen, Texas (Lake Creek at FM 149); and 

08067900 Lake Creek near Conroe, Texas (Lake Creek 

Conroe). The median SS and TSS concentrations were 54.0 

and 14.0 mg/L at White Oak Creek, 14.0 and 13.0 mg/L at 

West Fork FM 2854, 17.0 and 13.0 mg/L at Alligator Creek, 

26.0 and 12.0 mg/L at Lake Creek FM 149, and 39.0 and 11.0 

mg/L at Lake Creek Conroe, respectively. The maximum SS 

and TSS concentrations for the five additional monitoring sites 

were 3,110 and 390 mg/L, respectively, at White Oak Creek 

on April 28, 2009. The minimum SS concentration was 5.0 

mg/L at Alligator Creek on April 8, 2009, and the minimum 

TSS concentration was 1.0 mg/L at West Fork FM 2854 on 

July 20, 2009.

Continuous measurements of streamflow and selected 

water-quality properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) at stations 
28 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

08067650 and 08068000 were evaluated as potential variables 

in regression equations developed to estimate SS and TSS 

concentrations and loads. Surrogate regression equations were 

developed to estimate SS and TSS loads by using real-time 

turbidity and streamflow data; of all possible predictive variables, turbidity and streamflow resulted in the best regression 

models for estimating near “real-time” SS and TSS concentrations in the West Fork San Jacinto River at stations 08097650 

and 08068000. 

 A series of evaluations was done to determine the 

best variables for inclusion in the regression models and for 

evaluating accuracy and validity of the regression models. To 

improve the normal distribution of the predictive and response 

variables, base-10 logarithmic transformations were done on 

all data associated with the regression development. The data 

for the two best predictive variables, turbidity and streamflow, 

yielded the largest adjusted R-squared values and provided the 

best-fit linear regression model. The adjusted R-squared values 

were .8815 and .695 for SS and TSS, respectively, at station 

08067650 and .819 and .7906 for SS and TSS, respectively, at 

station 08068000. The variance inflation factors for the regression models developed ranged from 1.68 to 2.02, indicating 

that the predictive variables selected for each equation were 

not affected by multicollinearity. 

Each of the linear regression models developed for stations 08067650 and 08068000 to estimate SS and TSS on the 

basis of water-quality properties resulted in uncertainty in 

the estimated values. The main indicators of the measure of 

error for the values computed from the regression equations 

were the root-mean-square error and the prediction error sum 

of squares. The root-mean-square errors computed for the 

equations ranged from 0.147 to 0.282, which were higher than 

desired and indicated appreciable variance between the measured and regression-computed values. The prediction error 

sum of squares statistics for the equations ranged from 0.562 

to 1.91. These evaluations indicate that a large amount of 

error was generated in the results derived from the equations. 

The measure of variance (model residuals) between observed 

and regression-computed values provides another diagnostic 

tool to evaluate the accuracy of the regression models. The 

probability plot correlation coefficient values for the equations 

ranged from 0.9892 to 0.9928. The probability plots and probability plot correlation coefficient values for the regression 

equations indicate that the base-10 logarithmic transformed 

data provide normally distributed residuals. 

Relatively large errors are associated with the regressioncomputed SS and TSS concentrations; the 90-percent prediction intervals were ±48.9 percent for SS concentrations and 

±43.2 percent for TSS concentrations for station 08067650, 

and ±47.7 percent for SS concentrations and ±43.2 percent 

for TSS concentrations for station 08068000. Regressioncomputed SS and TSS concentrations were corrected for 

bias before being used to compute SS and TSS loads. The 

total estimated SS and TSS loads during July 2008–August 

2009 were about 3,540 and 1,900 tons, respectively, at station 

08067650 and about 156,000 and 72,000 tons, respectively, at 

station 08068000. Because the estimated SS and TSS concentrations derived from the regression equations contained large 

error components, the computed load estimates are inferred 

to also include large errors. The SS and TSS loads were about 

40 times larger at station 08068000 compared with loads at 

station 08067650, likely because station 08067650 is 2.5 mi 

downstream from a reservoir (Lake Conroe) with controlled 

releases, and station 08068000 is 11 mi downstream from 

station 08067650. Flow at station 08068000 is more representative of water-quality properties in the West Fork San Jacinto 

River Basin compared with station 08067650, which is more 

representative of water-quality properties of releases from 

Lake Conroe. 

References Cited

American Public Health Association, 2005, Standard methods 

for the examination of water and wastewater (21st ed.): 

Washington, D.C., American Public Health Association 

[variously paged]. 

American Society for Testing and Materials, 2003, D1889–00 

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Appendix 1—S–PLUS Output of Regression Model Development 

of Turbidity, Streamflow, Suspended-Sediment Concentration, 

and Total Suspended-Solids Concentration for Two Main 

Monitoring Sites (Stations 08067650 and 08068000) in the West 

Fork San Jacinto River Basin, Texas, July 2008–August 2009.
Blank Page
Appendix 1 33

*** Linear Model ***

Call: lm(formula = Log.TSS ~ Log.Turb + Log.Q, data = SJ105Final, na.action = 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

 -0.3439 -0.1057 -0.008282 0.1138 0.4791

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.4621 0.0998 4.6284 0.0001 

 Log.Turb 0.5525 0.1618 3.4142 0.0022 

 Log.Q 0.1394 0.0411 3.3922 0.0023 

Residual standard error: 0.196 on 25 degrees of freedom

Multiple R-Squared: 0.7176 Adjusted R-squared: 0.695 

F-statistic: 31.76 on 2 and 25 degrees of freedom, the p-value is 1.367e-007 

1 observations deleted due to missing values 

Analysis of Variance Table

Response: Log.TSS

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Log.Turb 1 1.997912 1.997912 52.01955 0.000000147

 Log.Q 1 0.441948 0.441948 11.50699 0.002311299

Residuals 25 0.960174 0.038407 

*** Linear Model ***

Call: lm(formula = Log.SSC ~ Log.Turb + Log.Q, data = SJ105Final, na.action 

= 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

 -0.2329 -0.08147 -0.04112 0.09103 0.2795

Coefficients:

 Value Std. Error t value Pr(>|t|)

(Intercept) 0.2968 0.0711 4.1747 0.0003 

 Log.Turb 0.7711 0.1106 6.9724 0.0000 

 Log.Q 0.1760 0.0299 5.8838 0.0000 

Residual standard error: 0.147 on 26 degrees of freedom

Multiple R-Squared: 0.89 Adjusted R-squared: 0.8815 

F-statistic: 105.1 on 2 and 26 degrees of freedom, the p-value is 3.472e-013

Analysis of Variance Table

Response: Log.SSC

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Log.Turb 1 3.794578 3.794578 175.6425 0.000000e+000

 Log.Q 1 0.747906 0.747906 34.6189 3.324109e-006

08067650 West Fork San Jacinto River below Lake Conroe near Conroe, Tex.

Appendix 1. S-PLUS output of regression model development of turbidity, streamflow, suspendedsediment concentration, and total suspended-solids concentration for two main monitoring 

sites (stations 08067650 and 08068000) in the West Fork San Jacinto River Basin, Texas, July

2008–August 2009.

Appendix 1
34 Streamflow and Water-Quality Properties in the West Fork San Jacinto River Basin in the Vicinity of Conroe, Texas

*** Linear Model ***

Call: lm(formula = Log.SSC ~ Log.Turb + Log.Q, data = SJ45Final, na.action = 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

 -0.7866 -0.1484 -0.03622 0.1229 0.5753

Coefficients:

 Value Std. Error t value Pr(>|t|)

(Intercept) -0.0677 0.1570 -0.4314 0.6700 

 Log.Turb 0.7350 0.1544 4.7602 0.0001 

 Log.Q 0.3436 0.0968 3.5509 0.0016 

Residual standard error: 0.2822 on 24 degrees of freedom

Multiple R-Squared: 0.833 Adjusted R-squared: 0.819 

F-statistic: 59.84 on 2 and 24 degrees of freedom, the p-value is 4.72e-010 

Analysis of Variance Table

Response: Log.SSC

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Log.Turb 1 8.526097 8.526097 107.0661 0.000000000

 Log.Q 1 1.004081 1.004081 12.6087 0.001624203

Residuals 24 1.911214 0.079634 

*** Linear Model ***

Call: lm(formula = Log.TSS ~ Log.Turb + Log.Q, data = SJ45Final, na.action = 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

 -0.6628 -0.1166 0.03594 0.1344 0.621

Coefficients:

 Value Std. Error t value Pr(>|t|)

(Intercept) -0.1252 0.1597 -0.7844 0.4408 

 Log.Turb 0.8140 0.1544 5.2735 0.0000 

 Log.Q 0.2262 0.0981 2.3069 0.0304 

Residual standard error: 0.2819 on 23 degrees of freedom

Multiple R-Squared: 0.8073 Adjusted R-squared: 0.7906 

F-statistic: 48.18 on 2 and 23 degrees of freedom, the p-value is 5.969e-009 

1 observations deleted due to missing values

Analysis of Variance Table

Response: Log.TSS

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Log.Turb 1 7.234734 7.234734 91.03984 0.00000000

 Log.Q 1 0.422902 0.422902 5.32168 0.03041015

Residuals 23 1.827759 0.079468 

08068000 West Fork San Jacinto River near Conroe, Tex.

Appendix 1. S-PLUS output of regression model development of turbidity, streamflow, suspendedsediment concentration, and total suspended-solids concentration for two main monitoring 

sites (stations 08067650 and 08068000) in the West Fork San Jacinto River Basin, Texas, July 

2008–August 2009—Continued.
Appendix 1 35

Appendix 1. S–PLUS output of regression model development of turbidity, streamflow, suspended-sediment concentration, 

and total suspended-solids concentration for two main monitoring sites (stations 08067650 and 08068000) in the West Fork San 

Jacinto River Basin, Texas, July 2008–August 2009—Continued.

Explanation of Abbreviations and Terms

SSC suspended-sediment concentration, in milligrams per liter

TSS total suspended-solids concentration, in milligrams per liter

Turb turbidity, in Formazin nephelometric units

Q streamflow, in cubic feet per second

Log (x) Base-10 logarithm of x

lm linear model function in S-Plus

SJ105 San Jacinto River at State Highway 105; U.S. Geological Survey streamflow-gaging station

08067650 West Fork San Jacinto River below Lake Conroe near Conroe, Tex.

na.action S-Plus function setting (argument) used to process missing values

na.exclude S-Plus function setting (argument) to exclude missing values

min minimum

1Q first quartile

3Q third quartile

max maximum

std. error standard error

t value test statistic from the student’s t distribution

Pr (F) probability of the F value

Pr(>|t|) probability of obtaining a t value at least as extreme as the one observed, if the null hypothesis 

is true

Residual standard error standard error of the residuals

Df degrees of freedom

Sum of Sq sum of squares

Mean Sq mean square

F Value value of the F statistic, used to test statistical hypotheses about the mean 

Call: S-Plus invocation of the regression model

p-value probability associated with the test statistic

Value Std. Error standard error of the value

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