East Fork

San Jacinto

River Luce Bayou

Lake

 Houston

West Fork San Jacinto River

Creek

Caney

SAN JACINTO DAM

Site A

(295826095082200)

Site B

(295554095093401)

Sheldon gage

(08072000)

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2015–5153

Simulation of the Effects of Different Inflows on Hydrologic 

Conditions in Lake Houston With a Three-Dimensional 

Hydrodynamic Model, Houston, Texas, 2009–10

Prepared in cooperation with the City of Houston
Covers, Floating platform auto-reel system for vertical profiling of real-time water-quality data at U.S. 

Geological Survey reservoir station 295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Texas (site B). 
Simulation of the Effects of Different 

Inflows on Hydrologic Conditions in 

Lake Houston With a Three-Dimensional 

Hydrodynamic Model, Houston, Texas, 

2009–10

By Samuel H. Rendon and Michael T. Lee

Prepared in cooperation with the City of Houston

Scientific Investigations Report 2015–5153

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Rendon, S.H., and Lee, M.T., 2015, Simulation of the effects of different inflows on hydrologic conditions in Lake 

Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10: U.S. Geological Survey Scientific 

Investigations Report 2015–5153, 42 p., http://dx.doi.org/10.3133/sir20155153.

ISSN 2328-0328 (online)
iii

Contents

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

Introduction ....................................................................................................................................................1

Purpose and Scope .............................................................................................................................3

Study Area .............................................................................................................................................3

Previous Studies ..................................................................................................................................3

Methods of Data Collection ........................................................................................................................4

Lake Elevation .......................................................................................................................................4

Streamflow ............................................................................................................................................4

Specific Conductance and Water Temperature .............................................................................5

Meteorological Data ............................................................................................................................5

Development of a Three-Dimensional Hydrodynamic Model ...............................................................5

Lake Elevation in 2009 and 2010 .........................................................................................................5

Inflows to Lake Houston From the Western and Eastern Watersheds in 2009 and 2010 .........7

Specific Conductance and Water Temperature in 2009 and 2010 ...............................................7

Meteorological Data in 2009 and 2010 ...........................................................................................11

Model Description and Inputs .........................................................................................................11

Model Grid ..................................................................................................................................16

Modeled Streamflow ................................................................................................................18

Specific Conductance and Water Temperature ..................................................................18

Meteorological Data ................................................................................................................22

Model Performance Testing .............................................................................................................22

Lake Elevation ............................................................................................................................23

Salinity Computed From Specific Conductance and Water Temperature ......................25

Model Limitations ...............................................................................................................................25

Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston ............32

Summary .......................................................................................................................................................40

References Cited .........................................................................................................................................40

Figures

1. Map showing Lake Houston drainage area and locations of U.S. Geological 

Survey streamflow-gaging stations and reservoir stations .................................................2

2. Graphs showing lake elevations measured at U.S. Geological Survey reservoir 

station 08072000 Lake Houston near Sheldon, Texas, in 2009 and 2010 .............................6

3. Graphs showing combined inflow from the western and eastern watersheds to 

Lake Houston, near Houston, Texas, in 2009 and 2010 ..........................................................8

4. Graphs showing specific conductance measured at U.S. Geological Survey 

streamflow-gaging stations 08068500 Spring Creek near Spring, Texas, and 

08070200 East Fork San Jacinto River near New Caney, Tex., in 2009 and 2010 ...............9

5. Graphs showing water temperature measured at U.S. Geological Survey 

streamflow-gaging stations 08068500 Spring Creek near Spring, Texas, and 

08070200 East Fork San Jacinto River near New Caney, Tex., in 2009 and 2010 .............10

6. Graphs showing specific conductance measured at a depth of 12 feet at U.S. 

Geological Survey reservoir stations 295826095082200 Lake Houston south Union 

Pacific Railroad Bridge near Houston, Texas (site A), and 295554095093401 Lake 

Houston at mouth of Jack’s Ditch near Houston, Tex. (site B), in 2009 and 2010 ............12
iv

7. Graph showing water temperature measured at 1-, 6-, 12-, and 16-foot depths 

at U.S. Geological Survey reservoir station 295554095093401 Lake Houston at 

mouth of Jack’s Ditch near Houston, Texas (site B), 2009 ..................................................13

8. Wind-rose graphs depicting hourly wind speed and direction values measured 

at the National Weather Service meteorological station at George Bush 

Intercontinental Airport near Houston, Texas, in 2009 and 2010 .......................................14

9. Graphs showing hourly precipitation measured at the National Weather Service 

meteorological station at George Bush Intercontinental Airport near Houston, 

Texas, in 2009 and 2010 .............................................................................................................15

10. Map showing conceptual layout of Lake Houston Environmental Fluid Dynamics 

Code model with grid and boundary condition cells for Lake Houston, near 

Houston, Texas ...........................................................................................................................17

11. Map showing gaged and ungaged areas of tributary watersheds providing 

inflows to Lake Houston, near Houston, Texas .....................................................................19

12. Graphs showing the relation between salinity, in parts per thousand, and 

streamflow, in cubic feet per second, during 2009–10 for U.S. Geological Survey 

(USGS) streamflow-gaging station 08068500 Spring Creek near Spring, Texas, 

and USGS streamflow-gaging station 08070200 East Fork San Jacinto River 

near New Caney, Tex. ...............................................................................................................21

13. Graph showing simulated and measured lake elevations at U.S. Geological 

Survey reservoir station 08072000 Lake Houston near Sheldon, Texas, 2009 .................23

14. Graph showing simulated and measured lake elevations at U.S. Geological 

Survey reservoir station 08072000 Lake Houston near Sheldon, Texas, 2010 .................24

15. Graphs showing simulated salinity and salinity computed from measured 

specific conductance measured in 2009 at U.S. Geological Survey reservoir 

stations on Lake Houston, near Houston, Texas ..................................................................26

16. Graphs showing simulated salinity and salinity computed from measured 

specific conductance measured in 2010 at U.S. Geological Survey reservoir 

stations on Lake Houston, near Houston, Texas ..................................................................27

17. Graphs showing simulated and measured water temperature at U.S. Geological 

Survey reservoir stations on Lake Houston, near Houston, Texas, in 2009 .....................29

18. Graphs showing simulated and measured water temperature at U.S. Geological 

Survey reservoir stations on Lake Houston, near Houston, Texas, in 2010 .....................30

19. Map showing locations of representative cross sections used to illustrate 

simulations from the three-dimensional hydrodynamic model of Lake Houston, 

near Houston, Texas ..................................................................................................................33

20. Maps and graphs showing examples of modeled changes in water residence 

time in Lake Houston, near Houston, Texas, from April 19, 2009, to April 22, 2009, 

in response to an inflow event ................................................................................................34

21. Maps and graphs showing examples of modeled changes in salinity in Lake 

Houston, near Houston, Texas, from April 19, 2009, to April 22, 2009, in response 

to an inflow event ......................................................................................................................36

22. Graph showing simulated depth-averaged residence time at each of the five 

representative cross sections and inflows from the western and eastern 

watersheds for 2009 from the Environmental Fluid Dynamics Code model of 

Lake Houston, near Houston, Texas .......................................................................................38

23. Graph showing model results for residence time over time at cross section D–Dʹ for 2010 for increased flow from West Fork San Jacinto River and 

increased flow from Luce Bayou to Lake Houston, near Houston, Texas .......................39
v

Tables

1. Data collected at U.S. Geological Survey (USGS) streamflow-gaging stations in 

the San Jacinto River Basin upstream from Lake Houston and at USGS reservoir 

stations in Lake Houston, near Houston, Texas, 2009 and 2010 ...........................................4

2. Average annual inflow for each of the tributaries to Lake Houston, near Houston, 

Texas, in 2009 and 2010 ...............................................................................................................7

3. Model input files for the Environmental Fluid Dynamics Code hydrodynamic 

model of Lake Houston, near Houston, Texas .......................................................................16

4. Gaged watershed area, ungaged watershed area, ratio of ungaged to gaged 

watershed area, and applied scalar factor for estimating the inflows from all 

tributaries to Lake Houston, near Houston, Texas, during 2009 .........................................20

5. Performance evaluation statistics for simulated lake elevation relative to 

measured lake elevation in Lake Houston, near Houston, Texas, 2009 and 2010 ...........24

6. Performance evaluation statistics for simulated salinity and salinity computed 

from measured specific conductance at U.S. Geological Survey reservoir 

stations 295826095082200 Lake Houston south Union Pacific Railroad Bridge 

near Houston, Texas, and 295554095093401 Lake Houston at mouth of Jack’s 

Ditch near Houston, Tex., 2009 and 2010 ................................................................................28

7. Performance evaluation statistics for simulated and measured water 

temperature at U.S. Geological Survey reservoir stations 295826095082200 

Lake Houston south Union Pacific Railroad Bridge near Houston, Texas, and 

295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex., 

2009 and 2010 ..............................................................................................................................31
vi

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

acre 0.004047 square kilometer (km2)

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

cubic foot (ft3) 28.32 cubic decimeter (dm3) 

cubic foot (ft3) 0.02832 cubic meter (m3) 

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

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3) 

Flow rate

foot per second (ft/s) 0.3048 meter per second (m/s)

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

Mass diffusivity and kinematic viscosity

square foot per second (ft2/s) 0.09290 square meter per second (m2/s)

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

°F = (1.8 × °C) + 32.

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

°C = (°F-32)/1.8.

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
Simulation of the Effects of Different Inflows on Hydrologic 

Conditions in Lake Houston With a Three-Dimensional 

Hydrodynamic Model, Houston, Texas, 2009–10

By Samuel H. Rendon and Michael T. Lee

Abstract

Lake Houston, an important water resource for the 

Houston, Texas, area, receives inflows from seven major 

tributaries that compose the San Jacinto River Basin upstream 

from the reservoir. The effects of different inflows from the 

watersheds drained by these tributaries on the residence time 

of water in Lake Houston and closely associated physical 

and chemical properties including lake elevation, salinity, 

and water temperature are not well known. Accordingly, the 

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

of Houston, developed a three-dimensional hydrodynamic 

model of Lake Houston as a tool for evaluating the effects 

of different inflows on residence time of water in the lake 

and associated physical and chemical properties. The 

Environmental Fluid Dynamics Code (EFDC), a gridbased, surface-water modeling package for simulating 

three-dimensional circulation, mass transport, sediments, 

and biogeochemical processes, was used to develop the 

model of Lake Houston. The Lake Houston EFDC model 

was developed and calibrated by using 2009 data and 

verified by using 2010 data. Three statistics (mean error, 

root mean square error, and the Nash-Sutcliffe model 

efficiency coefficient) were used to evaluate how well 

the Lake Houston EFDC model simulated lake elevation, 

salinity, and water temperature. The residence time of 

water in reservoirs is associated with various physical and 

chemical properties (including lake elevation, salinity, and 

water temperature). Simulated and measured lake-elevation 

values were compared at USGS reservoir station 08072000 

Lake Houston near Sheldon, Tex. The accuracy of simulated 

salinity and water temperature values was assessed by using 

the salinity (computed from measured specific conductance) 

and water temperature at two USGS monitoring stations: 

295826095082200 Lake Houston south Union Pacific 

Railroad Bridge near Houston, Tex., and 295554095093401 

Lake Houston at mouth of Jack’s Ditch near Houston, Tex. 

Specific conductance and water temperature were measured at 

as many as four different depths at each of the two monitoring 

stations during 2009 and then used for assessing the accuracy 

of simulated values of salinity and water temperature during 

2010. The performance evaluation statistics indicate that the 

model performed satisfactorily. The calibrated model was 

used to simulate two possible inflow scenarios to evaluate 

the changes in the residence time of water in Lake Houston. 

The two scenarios tested were an increased inflow of 

approximately 300 cubic feet per second for 1 month (May 

2010) from two watersheds: the West Fork San Jacinto River 

and Luce Bayou. These scenarios were chosen to mimic the 

effects of possible small releases or diversions of water from 

outside the San Jacinto River Basin into the basin (or directly 

into the lake) on the residence time of water in Lake Houston. 

During the time of increased inflow for the two scenarios 

tested, maximum residence time decreased slightly from 

approximately 106 to 97 days. 

Introduction

Lake Houston is an important water resource for the 

rapidly growing Houston, Texas, metropolitan area (Texas 

State Data Center, 2012a, 2012b). The City of Houston, 

the regional water provider for the Houston metropolitan 

area, relies primarily on surface water to meet the growing 

demands for municipal and industrial water supplies. 

According to the City of Houston Web site, in 2015, about 

71 percent of the water supply came from surface-water 

sources, and about 29 percent came from groundwater sources 

(City of Houston, 2015). The city has the goal of reducing 

groundwater usage to less than 20 percent of the total supply, 

and surface-water usage will increase to meet future water 

demands (City of Houston, 2015). The percentage of water 

supply for the City of Houston provided by Lake Houston has 

been steadily increasing; Lake Houston supplied between 10 

and 20 percent of the total source-water supply for the City 

of Houston as of 2015 (City of Houston, 2015). In addition 

to providing drinking water, Lake Houston is an important 

recreational resource for the Houston area. 

Lake Houston receives inflows from seven major 

tributaries (fig. 1). The effects of different inflows from 

the watersheds drained by these tributaries on hydrologic 

conditions of the lake, including the residence time of water in 

Lake Houston and associated physical and chemical properties 

(including lake elevation, salinity, and water temperature), 

are not well known. Accordingly, the U.S. Geological Survey 

(USGS), in cooperation with the City of Houston, developed 
2 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Lake 

Houston Houston

Lake 

 Conroe

HARRIS COUNTY

MONTGOMERY COUNTY

WALLER

 COUNTY

GRIMES

COUNTY

SAN JACINTO

COUNTY

WALKER COUNTY

LIBERTY

COUNTY

HARRIS COUNTY

MONTGOMERY COUNTY

WALLER

 COUNTY

GRIMES

COUNTY

SAN JACINTO

COUNTY

WALKER COUNTY

LIBERTY

COUNTY

Cypress Creek

Spring Creek

West Fork

San Jacinto River

East Fork

San Jacinto River

Peach

Creek

Caney

Creek

Luce

Bayou

Cypress Creek

Spring Creek

West Fork

San Jacinto River

East Fork

San Jacinto River

Peach

Creek

Caney

Creek

Luce

Bayou

08068090

08068500

08069000

08070200

08070500

08071000

08071280

08068090

08068500

08069000

08070200

08070500

08071000

08071280

West Fork San Jacinto River West Fork San Jacinto River

Caney CreekCaney Creek

East Fork San Jacinto RiverEast Fork San Jacinto River

Peach CreekPeach Creek

Cypress CreekCypress Creek

Spring

Creek

Luce Bayou Luce Bayou

Area shown on

figures 10 and 19–23

SAN JACINTO DAM

Site A

(295826095082200)

Site B

(295554095093401)

George Bush

Intercontinental Airport

a

Sheldon gage

(08072000)

Sheldon gage

(08072000)

08068090

08068500

08069000

08070200

08070500

08071000

08071280

08072000

295826095082200 

295554095093401 

West Fork San Jacinto River above Lake Houston near Porter, Tex.

Spring Creek near Spring, Tex.

Cypress Creek near Westfield, Tex.

East Fork San Jacinto River near New Caney, Tex.

Caney Creek near Splendora, Tex.

Peach Creek at Splendora, Tex.

Luce Bayou above Lake Houston near Huffman, Tex.

Lake Houston near Sheldon, Tex.

Lake Houston south Union Pacific Railroad Bridge near Houston, Tex.

Lake Houston at mouth of Jack’s Ditch near Houston, Tex.

USGS station name

USGS

station number

29°59'30"

95°48'15"

95°18'

30°29'45"

Houston

San Jacinto River Basin

 upstream from

Lake Houston

TEXAS

Study area

Western

watersheds

Eastern

watersheds

Approximate percentage of drainage area

that provides inflow to Lake Houston

63 percent

34 percent

3 percent

0 5 10 15 MILES

0 5 10 15 KILOMETERS

Base from U.S. Geological Survey 1:100,000-scale digital data

Albers Equal Area projection

North American Datum of 1983

EXPLANATION

San Jacinto River

 Basin upstream

 from Lake Houston

Watershed boundary

U.S. Geological Survey

 (USGS) streamflow-

 gaging station and 

 number

USGS reservoir station 

 and short name 

 (number)

Weather station

08068500

a

Figure 1. Lake Houston drainage area and locations of U.S. Geological Survey streamflow-gaging stations and reservoir stations. 
Introduction 3

a three-dimensional hydrodynamic model of Lake Houston 

as a tool for evaluating the effects of different inflows on the 

residence time of water in the lake and associated physical and 

chemical properties.

Purpose and Scope

The purpose of this report is to describe a threedimensional hydrodynamic model of Lake Houston, model 

performance testing, and simulation of the effects of different 

inflows on water residence time and other lake properties 

during a simulation period from 2009 through 2010. 

Hydrological and meteorological data collected during 2009 

were used to develop and calibrate the model; data collected 

during 2010 were used to verify the model. An overview 

is provided of the limitations and potential applications of 

the model, including how physical and chemical properties 

associated with residence time can change in response to 

different inflows from storm events and how the model can be 

applied to evaluate the residence time of water in the lake in 

response to two different inflow scenarios. 

Study Area

Lake Houston (fig. 1) is a eutrophic to hypertrophic 

reservoir about 25 miles (mi) northeast from the center 

of downtown Houston (Liscum and East, 2000). The San 

Jacinto Dam (referred to locally as the “Lake Houston Dam”) 

was built during 1951–53 (Dowell, 1964) and impounds 

the seven major tributaries that drain the San Jacinto River 

Basin upstream from Lake Houston. The City of Houston 

built the reservoir to provide water for municipal, industrial, 

recreational, mining, and irrigation purposes (Dowell, 1964). 

The reservoir has a capacity of about 146,700 acre-feet and a 

surface area of 12,240 acres; mean depth is about 12 feet (ft), 

and maximum depth is about 50 ft (Liscum and East, 2000; 

Texas Water Development Board, 2012). 

Lake Houston is at the outlet of the 2,785-squaremile (mi2

) San Jacinto River Basin upstream from the 

reservoir (fig. 1). Most (2,692.4 mi2, about 97 percent) of 

the San Jacinto River Basin upstream from Lake Houston 

can be divided into western and eastern watersheds; the 

remaining 92.25 mi2 (about 3 percent) of the basin consists 

of the drainage area downstream from the western and 

eastern watersheds surrounding Lake Houston. The western 

watersheds cover 1,746 mi2 (fig. 1) and represent about 

63 percent of the San Jacinto River Basin; the eastern 

watersheds cover 946.4 mi2 and represent about 34 percent of 

the basin. Hereinafter, the western and eastern watersheds are 

collectively referred to as the “San Jacinto River Basin.” 

Because of population growth, land use in the western 

watersheds has changed to include a relatively large amount 

of developed areas, whereas land use in the eastern watersheds 

has remained mostly undeveloped (fig. 1 in Beussink and 

Graham, 2011). Major tributaries in the western watersheds 

and the approximate percentage of the San Jacinto River 

Basin that they drain are West Fork San Jacinto River 

(36 percent), Spring Creek (16 percent), and Cypress Creek 

(11 percent). Major tributaries in the eastern watersheds and 

the approximate percentage of the San Jacinto River Basin 

that they drain are East Fork San Jacinto River (14 percent), 

Caney Creek (8 percent), Peach Creek (6 percent), and Luce 

Bayou (6 percent). Land use in the immediately surrounding 

area of Lake Houston is a mix of developed and undeveloped 

areas (fig. 1 in Beussink and Graham, 2011). Six wastewatertreatment facilities contribute wastewater discharge to Lake 

Houston (Miertschin and Associates, 2007). More than 

300 municipal wastewater discharges are permitted in the 

basin (Texas Commission on Environmental Quality, 2011).

The climate in the study area is classified as humid 

subtropical (Larkin and Bomar, 1983), characterized by cool, 

temperate winters and hot, humid summers. Air temperatures 

of 90 degrees Fahrenheit (°F) or warmer are common from 

April through October. During 2005–09, annual rainfall ranged 

from approximately 41 to 65 inches (in.) at the meteorological 

station operated by the National Weather Service (NWS) at 

George Bush Intercontinental Airport near Houston (National 

Oceanic and Atmospheric Administration, 2012). 

Previous Studies

Although the hydrodynamics of Lake Houston are not 

fully understood, previous investigators have gained some 

insights into water residence time and mixing in this reservoir. 

Theoretical water residence time in Lake Houston ranges from 

about 12 hours to 400 days (Liscum and East, 2000); annual 

mean water residence time during 2000–04 ranged from 24 

to 176 days (Sneck-Fahrer and others, 2005). Storm runoff 

generally passes through the reservoir within 24 hours. During 

storm events, horizontal and vertical mixing is not complete 

for at least one-half the length of the reservoir (Matty and 

others, 1987; Kilson, 1992).

Since 1983, the USGS, in cooperation with the City of 

Houston, has collected water-quality and lake-elevation data 

for Lake Houston, water-quality and streamflow data for 

the major tributaries to Lake Houston (Lee and Rast, 1997; 

Liscum and others, 1999; Sneck-Fahrer and others, 2005; 

Beussink and Burnich, 2009), and has published water-quality 

assessments pertaining to Lake Houston and its tributaries. 

Liscum and East (2000) evaluated the possible effects on Lake 

Houston water quality from an interbasin transfer of water 

from the Trinity River. Beussink and Graham (2011) focused 

on water-quality constituents that affect the aesthetic quality of 

drinking water such as geosmin, 2-Methylisoborneol (MIB), 

and manganese. Lee and others (2012) developed regression 

models for selected constituent concentrations and loads of 

tributaries to the lake. Lake Houston displays longitudinal 

gradient characteristics, with larger nutrient concentrations, 

turbidity, and algal biomass in the riverine zone (upstream part 

of the reservoir) of the reservoir and smaller concentrations in 

the lacustrine zone (downstream part of the reservoir) near the 

dam (Conrad, 1986; Sneck-Fahrer and others, 2005). 
4 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Methods of Data Collection 

A comprehensive dataset, including lake elevations, 

streamflow, specific conductance, and water temperature, 

was compiled for 10 USGS data-collection sites and 

meteorological data were compiled from the NWS 

meteorological station at George Bush Intercontinental 

Airport near Houston (National Oceanic and Atmospheric 

Administration, 2012) (fig. 1, table 1). All of these data were 

used in development of the Lake Houston three-dimensional 

hydrodynamic model. Of the 10 USGS data-collection sites, 7 

are streamflow-gaging stations, and 3 are reservoir stations. At 

five of the seven streamflow-gaging stations, only streamflow 

data were collected (table 1). At the other two streamflowgaging stations, specific conductance and water temperature 

data were collected in addition to streamflow (table 1). Lake 

elevation was measured at one of the reservoir stations, 

whereas specific conductance and water temperature were 

measured at the other two reservoir stations (table 1). The 

ArcGIS Watershed (Spatial Analyst) tool was used to create 

raster-delineated drainage basin areas (Esri, 2011).

Lake Elevation

Lake elevation was measured at USGS reservoir station 

08072000 Lake Houston near Sheldon, Tex. (hereinafter 

referred to as the “Sheldon gage”), which is near the dam. 

The Sheldon gage is satellite telemetered and transmits data 

at 15-minute intervals. Prior to October 1, 2009, the datum 

of the Sheldon gage was 0.70 ft below the National Geodetic 

Vertical Datum of 1929; after October 1, 2009, the gage 

datum, unadjusted for land-surface subsidence (2.77 ft above 

current datum), was changed to the North American Vertical 

Datum 1988 (NAVD 88), which is the datum used for the 

three-dimensional hydrodynamic model. 

Streamflow

Streamflow was continuously measured at the 

streamflow-gaging stations on the seven major tributaries to 

Lake Houston (table 1). Streamflow data were electronically 

recorded and transmitted by satellite from the seven USGS 

streamflow-gaging stations to a downlink site and then to the 

USGS Texas Water Science Center in Austin, Tex., where 

they were reviewed and finalized before being stored in the 

USGS National Water Information System (NWIS) database 

(http://waterdata.usgs.gov/tx/nwis/). Discharge measurements 

were periodically made to verify and modify a stage-discharge 

relation developed on the basis of streamflow measurements 

and the stage of the stream at the time of measurement 

(Rantz and others, 1982; Kennedy, 1984; Turnipseed and 

Sauer, 2010). These unique relations were used to compute 

a continuous record of streamflow (Kennedy, 1983) from the 

stage record at each site. Instantaneous stage and streamflow 

Table 1. Data collected at U.S. Geological Survey (USGS) streamflow-gaging stations in the San Jacinto River Basin upstream from 

Lake Houston and at USGS reservoir stations in Lake Houston, near Houston, Texas, 2009 and 2010.

[USGS, U.S. Geological Survey; --, not applicable; x, data collected]

USGS station 

number USGS station name Short name

Stage 

(lake 

elevation)

Streamflow Specific 

conductance

Water 

temperature 

Streamflow-gaging stations on tributaries to Lake Houston

08068090 West Fork San Jacinto River above 

Lake Houston near Porter, Tex.

West Fork San 

Jacinto gage

-- x -- --

08068500 Spring Creek near Spring, Tex. Spring Creek gage -- x x x

08069000 Cypress Creek near Westfield, Tex. Cypress Creek gage -- x -- --

08070200 East Fork San Jacinto River near New 

Caney, Tex.

East Fork San 

Jacinto gage

-- x x x

08070500 Caney Creek near Splendora, Tex. Caney Creek gage -- x -- --

08071000 Peach Creek at Splendora, Tex. Peach Creek gage -- x -- --

08071280 Luce Bayou above Lake Houston near 

Huffman, Tex.

Luce Bayou gage -- x -- --

Lake Houston sites

08072000 Lake Houston near Sheldon, Tex. Sheldon gage x -- -- --

295826095082200 Lake Houston south Union Pacific 

Railroad Bridge near Houston, Tex.

Site A -- -- x x

295554095093401 Lake Houston at mouth of Jack’s Ditch 

near Houston, Tex. 

Site B -- -- x x
Development of a Three-Dimensional Hydrodynamic Model 5

values are stored in the NWIS database (http://waterdata.usgs.

gov/tx/nwis/).

Specific Conductance and Water Temperature

Specific conductance and water temperature were 

continuously monitored during 2009–10 at two stream 

locations and two lake locations in the study area (table 1). 

The two stream locations were USGS streamflow-gaging 

station 08068500 Spring Creek near Spring, Tex. (hereinafter 

the Spring Creek gage), and USGS streamflow-gaging station 

08070200 East Fork San Jacinto River near New Caney, Tex. 

(hereinafter the East Fork San Jacinto gage). The two lake 

locations were USGS reservoir station 295826095082200 

Lake Houston south Union Pacific Railroad Bridge 

near Houston, Tex. (site A), and USGS reservoir station 

295554095093401 Lake Houston at mouth of Jack’s Ditch 

near Houston, Tex. (site B). 

A swinging well design was used for continuously 

monitoring specific conductance and water temperature at 

the Spring Creek and East Fork San Jacinto gages. Swinging 

wells respond to and move in the water column or “swing” in 

the direction of the force of the flowing water. The wells were 

constructed of schedule 80 polyvinyl-chloride pipes with holes 

in the bottom 3-ft section, allowing water to pass through 

freely at the position where the multiparameter monitor is 

located inside the well. Each swinging well containing a 

multiparameter monitor was positioned near the centroid of 

flow in each stream. 

At the two lake locations (sites A and B), floating 

platforms that recorded real-time water-quality data by 

using an auto-reel system for vertical profiling were used as 

described in Beussink and Burnich (2009). These platforms 

are fully automated and lower a multiparameter monitor to 

vertical depths of 1, 6, 12, and 16 ft at 15-minute intervals. 

The multiparameter monitors were calibrated as described 

in the USGS “National Field Manual for the Collection of 

Water-Quality Data” (U.S. Geological Survey, variously 

dated). The multiparameter monitors were maintained and the 

resulting data records processed as outlined in Wagner and 

others (2006).The data from each multiparameter monitor 

are electronically recorded and transmitted by satellite to a 

downlink site and then to the USGS Texas Water Science 

Center in Austin. Specific conductance and water temperature 

data collected at 15-minute intervals were stored in the USGS 

NWIS database (http://waterdata.usgs.gov/tx/nwis/). Specific 

conductance was measured in microsiemens per centimeter at 

25 degrees Celsius, and water temperature was measured in 

degrees Fahrenheit.

Meteorological Data

Meteorological data used in this report were recorded 

hourly at the NWS meteorological station at George Bush 

Intercontinental Airport near Houston (National Oceanic and 

Atmospheric Administration, 2012). Meteorological data 

recorded at this site by the NWS are supplemented by solar 

radiation data recorded for the National Solar Radiation 

Database (NSRDB). A combined dataset of meteorological 

and solar data is stored in the NSRDB, which is overseen 

by the Department of Energy’s National Renewable Energy 

Laboratory. The specific data that were obtained for use in this 

study are referred to in the NSRDB as the “solar and filled 

meteorological fields dataset” (National Renewable Energy 

Laboratory, 2012).

Development of a Three-Dimensional 

Hydrodynamic Model 

The Environmental Fluid Dynamics Code (EFDC), a 

grid-based surface-water modeling package for simulating 

three-dimensional circulation, mass transport, sediments, 

and biogeochemical processes, was used to develop a threedimensional hydrodynamic model of Lake Houston. The 

EFDC was chosen to model the hydrodynamics of Lake 

Houston because it is an open source, publicly available 

software capable of accurately modeling the residence time of 

water in a lake. The EFDC model was originally developed 

at the Virginia Institute of Marine Science for estuarine and 

coastal applications (Hamrick, 1992). The U.S. Environmental 

Protection Agency (EPA) has continued to support its 

development in cooperation with Tetra Tech, Inc. (Hamrick, 

2007), and the EFDC has been listed (along with other public 

domain surface-water models) as suitable for use in a variety 

of surface-water-related assessments (U.S. Environmental 

Protection Agency, 2015). The EFDC has been extensively 

tested, documented, and used in more than 100 surface-water 

studies (Ji, 2008). 

Data characterizing the hydrologic conditions in Lake 

Houston and its contributing areas during 2009–10 were 

compiled to develop the EFDC model of Lake Houston. These 

data included lake elevation, inflows from the western and 

eastern watersheds, specific conductance, water temperature, 

and meteorological data.

Lake Elevation in 2009 and 2010 

In 2009 and 2010, lake elevations in Lake Houston varied 

following patterns similar to the measured inflows (fig. 2). 

The maximum measured lake elevation was 44.49 ft on April 

29, 2009. The lake elevation then decreased during the early 

summer, declining to a low of 40.96 ft on July 6, 2009. The 

lake elevation generally increased during the remainder of 

2009, reaching an elevation of 43.73 ft on October 31, 2009. 

Lake elevations generally decreased throughout most of 2010, 

declining to a minimum elevation of 40.97 ft on November 1, 

2010, and reached a maximum elevation of 43.30 ft on July 3, 

2010.
6 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

Lake elevation above North American Vertical Datum of 1988, in feet Lake elevation above North American Vertical Datum of 1988, in feet

40

41

42

43

44

45

40

41

42

43

44

A

B

Figure 2. Lake elevations measured at U.S. Geological Survey reservoir station 08072000 Lake Houston near Sheldon, Texas, in A, 2009 

and B, 2010.
Development of a Three-Dimensional Hydrodynamic Model 7

Inflows to Lake Houston From the Western and 

Eastern Watersheds in 2009 and 2010

The combined annual average streamflow measured at 

the streamflow-gaging stations on the seven major tributaries 

to Lake Houston (table 1) was approximately 1,300 cubic feet 

per second (ft3/s), in 2009 and 2010 (table 2). During 2009 and 

2010, streamflow from the western watersheds (West Fork San 

Jacinto River, Spring Creek, and Cypress Creek) composed 

approximately 70 percent of the total inflows to Lake Houston, 

with the remaining coming from the eastern watersheds (East 

Fork San Jacinto River, Caney Creek, Peach Creek, and Luce 

Bayou). Whereas the combined annual average streamflow 

amounts from the seven major tributaries in 2009 and 2010 

were similar, the magnitudes of individual storm events 

were different. During 2009, there were three storm events 

(April 19–20, April 28–29, and October 30–31), for which 

the combined inflows exceeded 10,000 and 7,000 ft3/s from 

the western and eastern watersheds, respectively. In 2009, the 

maximum combined inflow rate for the western watersheds was 

38,000 ft3/s on April 28, 2009, whereas the maximum combined 

inflow rate for the eastern watersheds was 12,000 ft3/s on April 

20, 2009 (fig. 3A). During 2010, there were only two storm 

events that generated inflows of more than 10,000 ft3/s for the 

western watersheds or more than 3,500 ft3/s for the eastern 

watersheds. In 2010, the maximum combined inflow rate for the 

western watersheds was 12,000 ft3/s on July 2, 2010, whereas 

the maximum combined inflow rate for the eastern watersheds 

was 5,200 ft3/s on February 13, 2010 (fig. 3B).

Specific Conductance and Water Temperature in 

2009 and 2010

In 2009 and 2010, specific conductance and water 

temperature data were collected from one tributary to Lake 

Houston representing the western watersheds (Spring Creek) 

and from one tributary representing the eastern watersheds 

(East Fork San Jacinto River) (table 1). Specific conductance, 

which is an indicator of ion concentration (Hem, 1985), 

followed the same patterns in 2009 and 2010 observed in 

previous studies for nutrients, suspended solids, and other 

water-quality constituents. In general, larger loads of nutrients, 

suspended solids, and other water-quality constituents are 

contributed to Lake Houston from tributaries in the western 

watersheds than from tributaries in the eastern watersheds, 

reflecting the differences in land use in the watersheds 

(Liscum and others, 1999; Van Metre and Sneck-Fahrer, 

2002; Sneck-Fahrer and others, 2005). In 2009 and 2010, the 

specific conductance measured at the Spring Creek gage used 

to represent the western watersheds was generally higher 

and more variable than the specific conductance measured at 

the East Fork San Jacinto gage used to represent the eastern 

watersheds (fig. 4). 

The water temperature patterns throughout the year at the 

Spring Creek and East Fork San Jacinto gages were similar, 

with slightly warmer temperatures in Spring Creek compared 

to the East Fork San Jacinto River, a difference that was most 

pronounced in the summer months (fig. 5). The maximum 

water temperatures recorded in 2009 were 94.1 °F and 92.5 °F 

in Spring Creek and the East Fork San Jacinto River on June 

28 and on June 29, respectively. The minimum 2009 water 

temperatures recorded in Spring Creek and the East Fork San 

Jacinto River were 47.1 °F and 46.4 °F on December 28 and 

on December 29, respectively. During 2010 the maximum 

water temperature recorded at Spring Creek was 96.3 °F on 

August 5, and the minimum water temperature recorded was 

39.2 °F on January 10. For the East Fork San Jacinto River, 

the maximum water temperature recorded during 2010 was 

90.5 °F on August 15, and the minimum water temperature 

recorded was 38.8 °F on January 11. 

Table 2. Average annual inflow for each of the tributaries to Lake Houston, near Houston, Texas, in 2009 and 2010.

[USGS, U.S. Geological Survey; ft3/s, cubic foot per second]

USGS 

station 

number

USGS station name Short name

Average 

annual 

inflow, 2009

(ft3/s)

Average 

annual 

inflow, 2010

(ft3/s)

08068090 West Fork San Jacinto River above Lake Houston near Porter, Tex. West Fork San Jacinto gage 345.2 418.6

08068500 Spring Creek near Spring, Tex. Spring Creek gage 293.2 290.2

08069000 Cypress Creek near Westfield, Tex. Cypress Creek gage 248.9 251.6

08070200 East Fork San Jacinto River near New Caney, Tex. East Fork San Jacinto gage 140.5 173.6

08070500 Caney Creek near Splendora, Tex. Caney Creek gage 59.6 66.3

08071000 Peach Creek at Splendora, Tex. Peach Creek gage 71.1 73.7

08071280 Luce Bayou above Lake Houston near Huffman, Tex. Luce Bayou gage 110.8 55.4
8 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

10,000

20,000

30,000

40,000

50,000

Inflow, in cubic feet per second Inflow, in cubic feet per second

0

Combined inflow from western

 watersheds

Combined inflow from eastern 

 watersheds

EXPLANATION

Combined inflow from western

 watersheds

Combined inflow from eastern 

 watersheds

EXPLANATION

A

B

Figure 3. Combined inflow from the western and eastern watersheds to Lake Houston, near Houston, Texas, in A, 2009 and B, 2010.
Development of a Three-Dimensional Hydrodynamic Model 9

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

0

200

400

600

800

1,000

0

100

200

300

400

500

600

700

800

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

U.S. Geological Survey streamflow-gaging station

 Spring Creek near Spring, Tex.

 East Fork San Jacinto River near New Caney, Tex.

EXPLANATION

U.S. Geological Survey streamflow-gaging station

 Spring Creek near Spring, Tex.

 East Fork San Jacinto River

 near New Caney, Tex.

EXPLANATION

A

B

Figure 4. Specific conductance measured at U.S. Geological Survey streamflow-gaging stations 08068500 Spring Creek near Spring, 

Texas, and 08070200 East Fork San Jacinto River near New Caney, Tex., in A, 2009 and B, 2010.
10 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

40

50

60

70

80

90

100

30

40

50

60

70

80

90

100

Water temperature, in degrees Fahrenheit Water temperature, in degrees Fahrenheit

U.S. Geological Survey 

 streamflow-gaging station

 Spring Creek near Spring, Tex.

 East Fork San Jacinto River 

 near New Caney, Tex.

EXPLANATION

U.S. Geological Survey 

 streamflow-gaging station

 Spring Creek near Spring, Tex.

 East Fork San Jacinto River

 near New Caney, Tex.

EXPLANATION

A

B

Figure 5. Water temperature measured at U.S. Geological Survey streamflow-gaging stations 08068500 Spring Creek near Spring, 

Texas, and 08070200 East Fork San Jacinto River near New Caney, Tex., in A, 2009 and B, 2010.
Development of a Three-Dimensional Hydrodynamic Model 11

Specific conductance and water temperature data were 

also collected at sites A and B in Lake Houston (table 1). 

In 2009, specific conductance and water temperature were 

measured at sites A and B at depths of 1, 6, 12, and 16 ft. 

In 2010, specific conductance and water temperature data 

were only collected at a depth of 1 ft at site A, whereas at 

site B these water-quality data were once again collected at 

depths of 1, 6, 12, and 16 ft. Where measured, the vertical 

distribution of specific conductance values was relatively 

uniform. A slight horizontal gradient in specific conductance 

was found between sites A and B, with slightly higher values 

that tended to decrease more at site A during runoff events 

(fig. 3) compared to site B (fig. 6). The horizontal stratification 

can be attributed to the location of site A, which is closer 

to the confluences of the seven major tributaries to the lake 

compared to site B, which is closer to the dam that impounds 

the lake. The differences in their locations in the lake means 

that inflows arrive earlier at site A and have less time to 

mix with the existing lake water compared to site B. Where 

water temperature was measured at all four depths, a slight 

thermal stratification was observed during the cooler months 

of November through March; an example of this thermal 

stratification is provided by using the data collected for site B 

(fig. 7). This stratification became more pronounced during 

the warmer months of April through October. The difference 

between the 1-ft depth and the 16-ft depth can be less than 

2 °F in the winter and as much as 10 °F in the summer (fig. 7). 

There was little to no difference, however, horizontally in the 

thermal stratification in water temperature observed at sites A 

and B.

Meteorological Data in 2009 and 2010

Hourly values for selected metrological data from 

2009 and 2010 (dry bulb temperature [air temperature], 

relative humidity, air pressure, precipitation, cloud cover, 

wind speed, and wind direction) were measured at the NWS 

meteorological station at George Bush Intercontinental 

Airport during 2009–10. The prevailing wind direction varies 

considerably (fig. 8), with southerly winds prevailing during 

the spring and summer and northerly winds prevailing in 

the winter (Larkin and Bomar, 1983). Southerly winds are 

opposite to the longitudinal axis of flow in the lake. 

The total annual rainfall measured at the NWS 

meteorological station at George Bush Intercontinental Airport 

was 46.9 and 42.9 in. during 2009 and 2010, respectively 

(National Oceanic and Atmospheric Administration, 2012). 

Although the overall magnitudes of the total annual rainfall 

during 2009 and 2010 were similar, the rain fell more 

sporadically and often at a higher intensity in 2010 compared 

to 2009. The maximum hourly rainfall rate for 2009 was 

approximately 1 inch per hour (in/h), whereas the maximum 

hourly rainfall rate for 2010 was approximately double at 

approximately 2 in/h (fig. 9). The pattern of more sporadic but 

higher intensity rainfall in 2010 compared to 2009 may have 

had some effect on the temperature and specific conductance 

in the upper layer of the lake.

Model Description and Inputs 

Setup of the Lake Houston EFDC model required six 

types of boundary condition data, which may be applied to 

either individual boundary grid cells or the grid cells of the 

entire model domain and are contained in their own individual 

files (table 3). The six types of boundary condition data 

are (1) bathymetric data, (2) flow (inflows and outflows), 

(3) salinity, (4) water temperature, (5) meteorological, and 

(6) hydraulic structure (the shape and dimensions of the 

dam impounding Lake Houston). Each of these datasets was 

compiled or computed as needed, checked for erroneous or 

missing data values, and then input to the EFDC by using 

EFDC Explorer, a graphical user interface developed for the 

EFDC (Dynamic Solutions, LLC, 2015). 

The bathymetric data, used to describe the bottom surface 

for the lake, were obtained from a 1994 hydrographic survey 

of Lake Houston by the Texas Water Development Board 

(Texas Water Development Board, 2012) and are stored in the 

dxdy.inp file (table 3). The boundary condition cells that were 

used in the Lake Houston EFDC model include inflow cells 

and outflow cells (outflows are through hydraulic structure 

cells and pump cells) (fig. 10, table 3). Inflow cells require 

data characterizing the inflows to Lake Houston, as well as 

salinity and water temperature data for Lake Houston. The 

outflow cells only require the outflows from Lake Houston 

as an input since the outflow salinity and water temperature 

are computed by using equations built into the EFDC 

model. Inflow and outflow data are stored in the qser.inp

file (table 3), salinity data are stored in the sser.inp file, and 

water temperature data are stored in the tser.inp file (table 3). 

Meteorological data are applied to the entire model domain 

and are stored in the atmospheric forcing time-series file 

(aser.inp) (table 3). Hydraulic structure data, stored in the 

qctl.inp file, were based on a rating curve normalized for the 

length of the San Jacinto Dam that was developed to estimate 

outflows over the San Jacinto Dam from lake elevations 

(table 3). Additional input files (efdc.inp, cell.inp, cellt.inp, 

lxly.inp) provide a range of specific functions necessary in the 

model (table 3).
12 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

50

100

150

200

250

300

350

400

50

100

150

200

250

300

350

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Site A (295826095082200)

Site B (295554095093401)

EXPLANATION

Site A (295826095082200)

Site B (295554095093401)

EXPLANATION

A

B

Figure 6. Specific conductance measured at a depth of 12 feet at U.S. Geological Survey reservoir stations 295826095082200 Lake 

Houston south Union Pacific Railroad Bridge near Houston, Texas (site A), and 295554095093401 Lake Houston at mouth of Jack’s Ditch 

near Houston, Tex. (site B), in A, 2009 and B, 2010.
Development of a Three-Dimensional Hydrodynamic Model 13

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

40

50

60

70

80

90

100

Water temperature, in degrees Fahrenheit

EXPLANATION

Depth

 1 foot

 6 feet

 12 feet

 16 feet

Figure 7. Water temperature measured at 1-, 6-, 12-, and 16-foot depths at U.S. Geological Survey reservoir station 295554095093401 

Lake Houston at mouth of Jack’s Ditch near Houston, Texas (site B), 2009.
14 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

50 40 30 20 10 0

50 40 30 20 10 0

0°

30°

60°

90°

120°

150°

180°

210°

240°

270°

300°

330°

0°

30°

60°

90°

120°

150°

180°

210°

240°

270°

300°

330°

N

S

W E

N

S

W E

EXPLANATION

Wind speed, in feet per second, for 2009—

 Wind direction is shown in degrees (°) from

 true north. One symbol may represent more than

 one value

Wind speed, in feet per second, for 2010—

 Wind direction is shown in degrees (°) from

 true north. One symbol may represent more than

 one value

EXPLANATION

A

B

Figure 8. Wind-rose graphs depicting hourly wind speed and direction values measured at the National Weather Service 

meteorological station at George Bush Intercontinental Airport near Houston, Texas, in A, 2009 and B, 2010.
Development of a Three-Dimensional Hydrodynamic Model 15

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0

0.5

1.0

1.5

2.0

2.5

Hourly precipitation, in inches Hourly precipitation, in inches

A

B

Figure 9. Hourly precipitation measured at the National Weather Service meteorological station at George Bush Intercontinental 

Airport near Houston, Texas, in A, 2009 and B, 2010.
16 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Table 3. Model input files for the Environmental Fluid Dynamics Code (EFDC) hydrodynamic model of Lake Houston, near Houston, 

Texas.

[--, not applicable]

File name File type Type of boundary 

condition data File function

dxdy.inp Cell information file Bathymetric and roughness Defines bathymetric grid spacing, depth, roughness, and 

vegetation class.

qser.inp Time-series file Flow (inflows and outflows) Defines inflows to the lake from tributaries and outflows from the 

lake over the dam or through withdrawals (pump cells).

sser.inp Time-series file Salinity Defines salinity time series.

tser.inp Time-series file Water temperature Defines water temperature time series.

aser.inp Time-series file Meteorological Defines atmospheric forcing time series.

qctl.inp Time-series file Hydraulic structure Defines hydraulic structure.

efdc.inp Master input file -- Provides run control parameters, output control, domain, and 

external forcing functions.

cell.inp Cell identifier file -- Identifies cell characteristics.

cellt.inp Cell identifier file -- Identifies subgroups of cells.

lxly.inp Cell information file -- Defines cell center coordinates and cell orientation.

Lake-specific model parameters that govern friction, 

heat transfer, advection, and diffusion were used in the 

dynamically coupled transport equations for turbulent kinetic 

energy, turbulent length scale, salinity, and water temperature 

(Hamrick, 2007). These model parameters are used by the 

EFDC and stored in different input (.inp) files (table 3).

Friction is governed by model parameter roughness, 

which pertains to effective bottom roughness of the lake and is 

used in internal model calculations to achieve agreement with 

lake elevations and velocities; roughness is stored in the dxdy.

inp file (table 3). The roughness used in the calibrated Lake 

Houston hydrodynamic model ranges from 0.07–0.20 ft. These 

values vary longitudinally along the lake with roughness 

values of 0.07 ft near the tail waters and inflow tributaries to 

0.20 ft near the dam.

Data stored in the atmospheric forcing time-series file 

(aser.inp file) (table 3) include atmospheric pressure, air 

temperature, relative humidity, rainfall, and solar radiation, 

which are the atmospheric forcing variables that govern heat 

transfer. Heat transfer is used in internal model calculations 

for simulating water temperature in the lake. The initial bed 

temperature for the calibrated Lake Houston hydrodynamic 

model was set to 68 °F, which was nearly identical to the 

initial bed temperature used by Liscum and East (2000). 

Solar radiation was internally computed by using latitude and 

percent cloud cover. 

There are multiple advection schemes that can be used 

in the EFDC that are set in the efdc.inp file (table 3). In the 

calibrated Lake Houston hydrodynamic model, upwind 

difference momentum advection was used with a standard 

turbulent intensity scheme. This scheme was chosen because 

of the particular hydrodynamics measured in the lake, as well 

as the external wind forcing. 

In the EFDC, diffusion is modeled as vertical diffusion, 

which is computed implicitly, and horizontal diffusion, which 

is computed explicitly (Hamrick, 2007). The EFDC code that 

activates calculation of the horizontal diffusion component 

is stored in the efdc.inp file (table 3). For the calibrated Lake 

Houston hydrodynamic model the two parameters that govern 

diffusion were set to use constant horizontal momentum 

through the model calibration process: mass diffusivity (set to 

1.08 square feet per second [ft2

/s]) and kinematic viscosity (set 

to 1.08 ft2/s). 

Model Grid

Bathymetric data were obtained from a 1994 

hydrographic survey of Lake Houston (Texas Water 

Development Board, 2012). The bathymetric survey 

points were interpolated in ArcGIS (Esri, 2015) to create 

a continuous bathymetric surface for the bottom of Lake 

Houston. A bathymetric surface averaged over each of the 

modeled grid cells in the model domain was obtained from 

Dynamic Solutions, LLC, and used as the bottom elevation 

(bathymetric data) inputs to the Lake Houston EFDC model 

(Andy Stoddard, Dynamic Solutions, LLC, written commun., 

2010). Bathymetric data are stored in the dxdy.inp file 

(table 3).

To accurately describe Lake Houston and its volume, 

a curvilinear horizontal grid was developed by Dynamic 

Solutions, LLC. After testing, a total of 1,632 horizontal grids 

were used in the model domain. The Lake Houston EFDC 

model grids were then configured with eight vertical layers 

representing one-eighth of the total water depth in order to 

simulate the vertical stratification of Lake Houston.
Development of a Three-Dimensional Hydrodynamic Model 17

Discharge pump 2

Discharge pump 1

Discharge pump 3

East Fork

San Jacinto

River

Luce Bayou

Lake

 Houston

West Fork San Jacinto River

Creek

Caney

MONTGOMERY COUNTY

HARRIS COUNTY

SAN JACINTO DAM

Union Pacific Railroad

Base from U.S. Geological Survey 1:100,000-scale digital data

Albers Equal Area projection

North American Datum of 1983

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

1960A

1960

2100

59

69

EXPLANATION

Model grid

Boundary condition

 Calibration cell

 Inflow cell—Green outline

 indicates test location

 used for simulating the

 increase in flow of 300

 cubic feet per second

 Pump cell and identifier

 Hydraulic structure cell

Discharge pump 1

30°03'

29°59'30"

29°56'

95°14'30" 95°11' 95°07'30"

Site A

(295826095082200)

Site B

(295554095093401)

Sheldon gage

(08072000)

Figure 10. Conceptual layout of Lake Houston Environmental Fluid Dynamics Code (EFDC) model with grid and boundary condition cells 

for Lake Houston, near Houston, Texas.
18 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Modeled Streamflow

The EFDC uses time-series streamflow data as one of 

its input datasets. Streamflow data represented in the Lake 

Houston EFDC model consisted of inflows to Lake Houston 

from tributaries and outflows from Lake Houston in the form 

of withdrawals pumped from the lake and water going over the 

spillway of the dam. The watershed areas for Lake Houston 

and for each of the seven major tributaries (fig. 1, table 1) 

were delineated by using elevation data downloaded from 

the National Elevation Dataset (National Digital Elevation 

Program, 2004) and ArcGIS (Esri, 2015). Each of the seven 

major tributary watersheds was delineated into two areas—an 

area represented by the USGS streamflow-gaging station 

in that watershed (gaged inflows) and an area that was not 

included in the USGS flow calculations (ungaged inflows) 

(fig. 11, table 4). A simple variation on the rational method 

first proposed by Kuichling (1889) and described in Chow 

and others (1988) was used to develop corresponding lake 

inflows that capture the spatial distribution of ungaged inflows 

to Lake Houston. The gaged watershed area for each tributary 

was compared to the total watershed area of that tributary 

to calculate the ratio of ungaged to gaged areas by using 

equation 1:

uw

w

gw

A

R

A = , (1)

where 

Rw is the ratio of the ungaged area to the gaged 

tributary watershed area for a given 

watershed (unitless),

Auw is the ungaged tributary watershed area in 

acres, and

Agw is the gaged tributary watershed area in acres.

A ratio of ungaged watershed area to gaged watershed 

area around Lake Houston that did not correspond to the 

drainage area of any of the tributaries was computed by using 

equation 2:

ul

l

gl

A

R

A = , (2)

where 

Rl is the ratio of the ungaged area to the gaged 

lake watershed area for the lake (unitless),

Aul is the ungaged lake watershed area in acres, 

and

Agl is the gaged lake watershed area in acres.

The corresponding ratio of gaged to ungaged tributary 

watershed area and the additional ungaged lake watershed 

area ratio were then used to create a scalar factor from the 

streamflow rates at each of the seven gaged tributaries. This 

scalar factor (K) is computed by using equation 3: 

(1 ) K = + Rw + Rl . (3)

The gaged streamflow rate for each of the contributing 

watersheds was then multiplied by its corresponding scalar 

factor to calculate total contributing flow into Lake Houston 

from that watershed (equation 4, table 4): 

Qtw =Qgw × K , (4)

where 

Qtw is the total watershed contributing flow into 

Lake Houston, and 

Qgw is the gaged streamflow from that watershed. 

There are three time-series outflow datasets used for 

populating the pump cells in the Lake Houston EFDC model 

(for the purposes of this report, the locations where these data 

were obtained are referred to as discharge pumps 1, 2, and 

3). The data for all three pumps were provided by the City of 

Houston (Joey Eickhoff, written commun., 2012). The data for 

discharge pumps 1 and 2 were provided at a daily time step. 

The data for discharge pump 3 were provided at a monthly 

time step and adjusted to a daily time step. 

The outflow from Lake Houston at the San Jacinto 

Dam was simulated by using a hydraulic structure boundary 

condition that allows the EFDC model to simulate flow out of 

a cell on the basis of an existing rating-curve relation between 

stage and discharge. Outflow from Lake Houston over the San 

Jacinto Dam spillway is estimated from the rating curve at the 

Sheldon gage that is used to characterize the relation between 

lake elevation and outflow. This rating curve accounts for lake 

elevations ranging from 41.6 to 49.8 ft and outflows ranging 

from 0 to 370,000 ft3/s. For modeling purposes, the outflow 

from the lake computed from the rating curve was divided by 

the length of the dam, redistributed according to the length of 

each of the five applicable cells on the model grid, and then 

used to specify the outflow from the Lake Houston EFDC 

model for simulated lake water levels. 

Specific Conductance and Water Temperature

Salinity and water temperature were chosen for use 

in model calibration because they are good indicators of 

the overall hydrodynamic behavior of lakes. Salinity is a 

natural tracer that can be used for evaluating conservative 

mass transport, whereas temperature can be used to evaluate 

seasonal vertical structure, mixing potential, and energy 

balance (Jin and others, 2000).

The EFDC model also requires salinity values as an 

input dataset for all inflows to Lake Houston. Salinity values 

were computed from specific conductance measured at the 

sampling locations representing the gaged inflows from the 

Spring Creek and East Fork San Jacinto River watersheds 

(table 1). Salinity in parts per thousand was computed from 

measured specific conductance by using techniques described 

in Wagner and others (2006, p. 36) and Schemel (2001); Lewis 

(1980) provided an overview of the advantages of determining 

salinity from specific conductance compared to other methods. 
Development of a Three-Dimensional Hydrodynamic Model 19

Lake 

Houston Houston

Lake 

 Conroe

HARRIS COUNTY

MONTGOMERY COUNTY

WALLER

 COUNTY

GRIMES

COUNTY

SAN JACINTO

COUNTY

WALKER COUNTY

LIBERTY

COUNTY

HARRIS COUNTY

MONTGOMERY COUNTY

WALLER

 COUNTY

GRIMES

COUNTY

SAN JACINTO

COUNTY

WALKER COUNTY

LIBERTY

COUNTY

Cypress Creek

Spring Creek

West Fork

San Jacinto River

East Fork

San Jacinto River

Peach

Creek

Caney

Creek

Luce

Bayou

Cypress Creek

Spring Creek

West Fork

San Jacinto River

East Fork

San Jacinto River

Peach

Creek

Caney

Creek

Luce

Bayou

08068090

08068500

08069000

08070200

08070500

08071000

08071280

08068090

08068500

08069000

08070200

08070500

08071000

08071280

West Fork San Jacinto River West Fork San Jacinto River

Caney CreekCaney Creek

East Fork San Jacinto RiverEast Fork San Jacinto River

Peach CreekPeach Creek

Cypress CreekCypress Creek

Spring

Creek

Luce Bayou Luce Bayou

29°59'30"

95°48'15"

95°18'

30°29'45"

0 5 10 15 MILES

0 5 10 15 KILOMETERS

Base from U.S. Geological Survey 1:100,000-scale digital data

Albers Equal Area projection

North American Datum of 1983

EXPLANATION

08068500

Watershed

 Gaged area

 Ungaged area

Watershed boundary

U.S. Geological Survey

 streamflow-gaging 

 station and number

Figure 11. Gaged and ungaged areas of tributary watersheds providing inflows to Lake Houston, near Houston, Texas.
20 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

To estimate salinity values for the inflows from the gaged 

parts of the additional five watersheds, the computed salinity 

values were compared to instantaneous streamflow at the sites 

on Spring Creek and the East Fork San Jacinto River, and 

the following mathematical relation between streamflow and 

salinity was used:

, (5)

Table 4. Gaged watershed area, ungaged watershed area, ratio of ungaged to gaged watershed area, and applied scalar factor for 

estimating the inflows from all tributaries to Lake Houston, near Houston, Texas, during 2009.

[USGS, U.S. Geological Survey; mi2, square mile; --, not applicable]

USGS 

station 

number

USGS station name Short name 

Gaged 

watershed 

area 

(mi2

)

Ungaged 

watershed 

area 

(mi2

)

Ratio of 

ungaged 

area to 

gaged area

Scalar 

factor 

(K)

08068090 West Fork San Jacinto River above 

Lake Houston near Porter, Tex.

West Fork San 

Jacinto gage

975.5 17.21 0.02 1.05

08068500 Spring Creek near Spring, Tex. Spring Creek gage 405.7 33.51 0.08 1.12

08069000 Cypress Creek near Westfield, Tex. Cypress Creek gage 281.0 33.49 0.12 1.16

08070200 East Fork San Jacinto River near New 

Caney, Tex.

East Fork San 

Jacinto gage

387.8 13.13 0.03 1.07

08070500 Caney Creek near Splendora, Tex. Caney Creek gage 105.3 114.5 1.09 2.12

08071000 Peach Creek at Splendora, Tex. Peach Creek gage 118.3 38.53 0.33 1.36

08071280 Luce Bayou above Lake Houston near 

Huffman, Tex.

Luce Bayou gage 153.2 15.75 0.10 1.14

-- Lake Houston Lake Houston 2,519 92.25 0.04 --

 b S a Q− = ×

where 

S is salinity, in parts per thousand,

a and b are curve-fitting coefficients, and 

Q is the streamflow for each gaged tributary, in 

cubic feet per second, at any given time. 

The relation between salinity and streamflow developed 

for the gaged part of the Spring Creek watershed resulted 

in values of 0.88 and 0.35 for a and b, respectively, and a 

coefficient of determination (R2) value (Helsel and Hirsch, 

2002) of 0.88; this relation was used to develop a continuous 

time-series dataset of salinity values for the inflows from 

the gaged parts of the two other western watersheds (West 

Fork San Jacinto River and Cypress Creek). For the gaged 

part of the East Fork San Jacinto River watershed, the values 

for the a and b coefficients were 0.20 and 0.26, respectively 

(R2 = 0.40); this mathematical relation between streamflow 

and salinity was used to develop the continuous time-series 

dataset for salinity values for inflows from the gaged parts 

of the remaining eastern watersheds (Caney Creek, Peach 

Creek, and Luce Bayou). By using the scalar factor (K) 

method described in the “Modeled Streamflow” section of 

this report, salinity values for the ungaged parts of the seven 

watersheds contributing inflows to Lake Houston (fig. 11) 

were estimated from the salinity values for the gaged parts 

of these watersheds. The relations between salinity and 

streamflow for the East Fork San Jacinto gage in the eastern 

watersheds and for the Spring Creek gage in the western 

watersheds are similar, but there is more scatter in the salinity 

values computed from measured specific conductance for the 

East Fork San Jacinto gage compared to the Spring Creek gage 

(fig. 12).

In addition to specific conductance, water temperature 

data also were collected at the streamflow-gaging stations 

representing the gaged inflows from the East Fork San Jacinto 

River and Spring Creek watersheds (table 1). The continually 

measured water temperature data at these sites were used 

to represent the temperature values of inflows from the 

remaining gaged and ungaged parts of the western and eastern 

watersheds, with the data from the continuously monitored 

Spring Creek site used to represent water temperature for 

inflows from the two other western watersheds and the data 

from the continuously monitored East Fork San Jacinto site 

used to represent water temperature for inflows from the 

three other eastern watersheds. The continually measured 

water temperature data were applied directly to the gaged and 

ungaged parts of the western and eastern watersheds, with 

Spring Creek water temperature data applied to the two other 

western watersheds and East Fork San Jacinto River water 

temperature data applied to the three other eastern watersheds. 
Development of a Three-Dimensional Hydrodynamic Model 21

Streamflow, in cubic feet per second

0 2,000 4,000 6,000 8,000 10,000 12,000

Streamflow, in cubic feet per second

0 1,000 2,000 3,000 4,000

0

0.06

0.12

0.18

0.24

0.30

0.36

0.42

0

0.03

0.06

0.09

0.12

0.15

0.18

Salinity, in parts per thousand Salinity, in parts per thousand 

Salinity—Eastern watersheds

 Best-fit curve estimated from mathematical relation

 S = a × Q-b

where

 S is salinity, in parts per thousand, a (0.20) and b (0.26) are

 curve-fitting coefficients, and Q is the streamflow for each

 gaged tributary, in cubic feet per second, at any given time

EXPLANATION

 Computed* from measured specific conductance at

 USGS streamflow-gaging station 08070200 East

 Fork San Jacinto River near New Caney, Tex. 

Salinity—Western watersheds

 Best-fit curve estimated from mathematical relation

 S = a × Q-b

where

 S is salinity, in parts per thousand, a (0.88) and b (0.35) are

 curve-fitting coefficients, and Q is the streamflow for each

 gaged tributary, in cubic feet per second, at any given time

EXPLANATION

 Computed* from measured specific conductance at

 U.S. Geological Survey (USGS) streamflow-gaging 

 station 08068500 Spring Creek near Spring, Tex.

A

B

*Salinity was computed by using techniques described in Wagner and others (2006, p. 36). 

Figure 12. The relation between salinity, in parts per thousand, and streamflow, in cubic feet per second, during 2009–10 for A, U.S. 

Geological Survey (USGS) streamflow-gaging station 08068500 Spring Creek near Spring, Texas, and B, USGS streamflow-gaging station 

08070200 East Fork San Jacinto River near New Caney, Tex.
22 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Meteorological Data

The meteorological data described in the “Meteorological 

Data in 2009 and 2010” section of this report were used as 

inputs for the Lake Houston EFDC model. The aerodynamic 

method of calculating evaporation from an open body of water 

(Chow and others, 1988, p. 84–86) is implemented internally 

in the EFDC model by using selected meteorological data 

collected for Lake Houston (dry bulb temperature, relative 

humidity, air pressure, wind speed, and wind direction). 

Additional information on the calculation of evaporation in the 

EFDC model is provided by Hamrick (1992, 2007).

Model Performance Testing 

The Lake Houston EFDC model was developed and 

calibrated by using input boundary condition data from 

2009. The Lake Houston EFDC model was then verified by 

using 2010 input boundary condition data as a secondary 

performance test. 

Model results at three locations in the model grid of 

Lake Houston (at various depths) were compared to measured 

data collected from the three data collection sites on the 

lake (fig. 10). The three types of data used to verify model 

performance were lake elevations, salinity (computed from 

measured specific conductance), and water temperature. Lake 

elevations were compared at the Sheldon gage. Salinity and 

water temperature were compared at the two lake locations, 

sites A and B.

Three statistics were used to evaluate performance of the 

Lake Houston EFDC model: mean error, root mean square 

error, and the Nash-Sutcliffe model efficiency coefficient 

(Nash coefficient) (Nash and Sutcliffe, 1970; Wilks, 1995). 

Mean error is computed as

( ) 1

1 i i

n

M i E S M

n = = ∑ − , (6)

where

ME is mean error,

n is the number of observations,

i is the given time step,

S is the simulated value at the given time step, 

and

M is the measured value at the given time step.

The mean error is the average difference between the 

simulated and measured values and can range from negative 

infinity to infinity. The mean error is used as a goodnessof-fit statistic to help identify possible bias in the simulated 

values. A positive mean error value indicates that the model 

is simulating values that are, on average, higher than the 

measured values; that is, biased high. A negative mean error 

indicates that the simulation values are biased low (Wilks, 

1995).

Root mean square error is a measure of the overall 

goodness-of-fit of the simulated values to the measured values 

and is computed as the root of the mean of the squares of the 

difference between the simulated and measured values as

( )

2

1

1 i i

n

i RMSE M S

n = = ∑ − , (7)

where

RMSE is root mean square error,

n is the number of observations,

i is the given time step, 

M is the measured value at the given time step, 

and

S is the simulated value at the given time step.

Since the root mean square error has the same units of 

measure as the quantity being simulated, it represents the 

error associated with individual simulated values (Hyndman 

and Koehler, 2006). Root mean square error values can range 

from 0 to infinity. Stonewall and Bragg (2012) explained that, 

as root mean square error values approach zero, the model 

approaches a perfect match between measured and simulated 

values, with root mean square error values of zero indicating 

residual values of zero (no difference between the measured 

and simulated value). 

Because mean error and root mean square error retain 

the units of measure of the original data, normalizing these 

metrics facilitates comparisons of how well the simulated 

and measured values for lake elevation, salinity, and 

water temperature each match; direct comparisons among 

normalized mean errors and normalized root mean square 

errors are possible for lake elevation, salinity, and water 

temperature once they are made proportional. The mean 

errors for lake elevation, salinity, and water temperature were 

normalized by dividing them by the range of the measured 

observations. The normalized mean error equation is

max min

ME NME

M M = − , (8)

where

NME is normalized mean error,

ME is mean error,

Mmax is the maximum measured value, and

Mmin is the minimum measured value.
Development of a Three-Dimensional Hydrodynamic Model 23

In the same way that mean error values were normalized, the 

root mean square error values for lake elevation, salinity, and 

water temperature also were normalized by dividing them 

by the range of the measured observations (maximum value 

minus the minimum value):

max min

RMSE NRMSE

M M = − , (9)

where

NRMSE is normalized root mean square error,

RMSE is root mean square error,

Mmax is the maximum measured value, and

Mmin is the minimum measured value.

The following equation is used to compute the Nash 

coefficient:

( ) ( ) 2

1

2

1 Nash coefficient 1 / n n

mean i i i = M S M M i = i = − −  −  ∑ ∑  , (10)

where

n is the number of observations,

i is the given time step,

Mi is the measured value at the given time step,

Si is the simulated value at the given time step, 

and

Mmean is the mean of the measured values.

Just as the normalized mean error and normalized root 

mean square error are dimensionless, the Nash coefficient 

also is dimensionless, which makes comparisons of how 

well the simulated and measured values match for lake 

elevation, salinity, and water temperature straightforward. 

The Nash coefficient can range from negative infinity to 1. 

A Nash coefficient of 1 would indicate that the simulated 

data are a perfect match to the measured data, a Nash 

coefficient of 0 would indicate that the simulated data are as 

good of a predictor as the mean of the measured data, and 

a Nash coefficient of less than 0 indicates that the mean of 

the measured data is a better predictor than the mean of the 

simulated data (Nash and Sutcliffe, 1970). Moriasi (2007) 

considered Nash coefficients of 0.50 or larger indicative 

of a satisfactory match between the simulated and measured 

data. 

Lake Elevation

A comparison of simulated to measured lake elevations 

provides insights into the Lake Houston EFDC model 

performance in terms of the overall mass balance of water 

in Lake Houston. There was a generally good fit between 

the simulated and measured lake elevations during 2009 and 

2010, with little overall bias (figs. 13 and 14). The largest 

deviation of the simulated lake elevations of approximately 

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

40

41

42

43

44

45

Lake elevation above North American Vertical Datum of 1988, in feet

Simulated 

Measured

EXPLANATION

Figure 13. Simulated and measured lake elevations at U.S. Geological Survey reservoir station 08072000 Lake Houston near Sheldon, 

Texas, 2009.
24 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

Lake elevation above North American Vertical Datum of 1988, in feet

40.5

41.0

41.5

42.0

42.5

43.0

43.5

Simulated 

Measured

EXPLANATION

Figure 14. Simulated and measured lake elevations at U.S. Geological Survey reservoir station 08072000 Lake Houston near Sheldon, 

Texas, 2010.

1 ft from measured values was observed during the summer 

months, with the simulated and measured values converging 

once again during the remainder of the year. For model runs 

using 2010 input boundary conditions, the simulated and 

measured lake-elevation values are similar. During 2009–10, 

mean error for lake elevation ranged from -0.102 to -0.080 ft, 

whereas the normalized (dimensionless) mean error for lake 

elevation ranged from -0.04 to -0.02, indicating a small 

negative bias in the model simulated values. Root mean 

square error values were within a small range from 0.20 

to 0.31 ft; the normalized root mean square errors for lake 

elevation equaled 0.09. The dimensionless Nash coefficient 

ranged from 0.54 to 0.75 (table 5). Although the Nash 

coefficient values did not closely approximate 1, they were 

consistently larger than 0.50, the lower limit of what Moriasi 

and others (2007) defined as indicative of satisfactory model 

performance. The normalized mean error and normalized root 

mean square error closely approximated zero. On an overall 

basis the performance evaluation statistics corroborate that 

the model performed satisfactorily in terms of simulating lake 

elevations. 

Table 5. Performance evaluation statistics for simulated lake elevation relative to measured lake elevation in Lake Houston, near 

Houston, Texas, 2009 and 2010.

[Mean errors, root mean square errors, and Nash-Sutcliffe model efficiency coefficients were determined by using the Environmental Fluid Dynamics Computer 

Code (EFDC) surface-water modeling package (Hamrick, 1992, 2007). ft, foot]

Year Mean error 

(ft)

Normalized 

mean error 

(dimensionless)

Root mean 

square error 

(ft)

Normalized root 

mean square error 

(dimensionless)

Nash-Sutcliffe model 

efficiency coefficient 

(dimensionless)

2009 -0.080 -0.02 0.31 0.09 0.54

2010 -0.102 -0.04 0.20 0.09 0.75
Development of a Three-Dimensional Hydrodynamic Model 25

Salinity Computed From Specific Conductance 

and Water Temperature

Comparisons between simulated salinity and salinity 

computed from measured specific conductance can provide 

insights into a model’s performance in terms of overall 

hydrodynamics. Comparisons were made between simulated 

salinity values and salinity values computed from measured 

specific conductance for two locations in Lake Houston, site 

A and site B (figs. 15 and 16). During 2009 and 2010, specific 

conductance and water temperature were measured at four 

incremental depths, 1, 6, 12, and 16 ft, at site B. At site A, 

specific conductance and water temperature were measured 

at 1, 6, 12, and 16 ft in 2009 but only measured at the 1-ft 

depth in 2010. Mean error ranged from -0.009 to -0.0039 

part per thousand for salinity (normalized mean error ranged 

from 0.10 to -0.09). Root mean square error ranged from 

0.011 to 0.013 part per thousand for salinity (normalized root 

mean square error ranged from 0.12 to 0.14). The normalized 

mean error and normalized root mean square error closely 

approximated zero, and the Nash coefficient ranged from 0.66 

to 0.86, indicating that the model performed satisfactorily in 

terms of simulating salinity (Moriasi and others, 2007). The 

model performance evaluation statistics for salinity in 2009 

and 2010 are summarized in table 6. 

During the summer months, simulated water 

temperatures generally are biased slightly high compared 

to measured water temperatures (figs. 17 and 18); however, 

this bias is not sufficient to hinder the Lake Houston EFDC 

model’s ability to accurately represent energy balance and 

mixing potential during the simulated time periods. On an 

overall basis, the performance evaluation statistics indicate 

that the model performed satisfactorily in terms of matching 

simulated to measured temperatures with relatively little bias 

and small residual values. During 2009–10, the mean error 

for temperature ranged from -0.20 to 2.84 °F (normalized 

mean error ranged from 0 to 0.06), and the root mean square 

error ranged from 1.49 to 3.83 °F (normalized root mean 

square error ranged from 0.04 to 0.08). The normalized 

mean error and normalized root mean square error closely 

approximated zero, and the Nash coefficient ranged from 

0.92 to 0.98, indicating the model performed satisfactorily in 

terms of simulating water temperature. All of the performance 

evaluation statistics for water temperature in 2009 and 2010 

are listed in table 7.

Model Limitations

Hydrodynamic models are simplified representations 

of complex systems that allow the hydrodynamics of a lake 

or other body of water to be simulated and visualized in a 

manner that otherwise might not be possible. The actual 

natural processes cannot be described completely through 

mathematical formulas, so models must inherently be 

conceptual (Liscum and East, 2000). 

The Lake Houston EFDC model was built to characterize 

the hydrodynamics of Lake Houston. Because models must be 

calibrated and tested by using input data collected in the field, 

uncertainty may be introduced by inaccuracies or limitations 

of the input datasets. 

Bathymetric data collected more than 20 years ago were 

used to calculate the storage capacity of Lake Houston in the 

EFDC model. Inaccuracies in the bathymetric data can arise 

from changes in the geometry of Lake Houston caused by the 

addition or removal of bed sediments since the last survey 

was completed in 1994, the relatively coarse resolution at 

which the bathymetric data were collected, and the need to 

average bathymetric data over large grid areas for the model. 

To minimize uncertainty in the geometric data, up-to-date 

bathymetric data would need to be collected; however, the 

collection of new bathymetric data at the same resolution at 

which the existing data were collected would not mitigate 

errors associated with the averaging of the data over large 

grid cells. 

Inaccuracies associated with inflows to Lake Houston 

from ungaged areas are another source of uncertainty. For this 

model, inflows to Lake Houston from ungaged areas of the 

watersheds were extrapolated from streamflow measured for 

gaged areas as explained in the “Modeled Streamflow” section 

of this report. Although relating drainage area to streamflow is 

a common practice in this type of modeling application, it may 

not accurately represent the inflows to Lake Houston from 

ungaged areas because of differences in land use. To fully 

characterize all of the inflows to Lake Houston, streamflowgaging stations would need to be installed on each of the 

tributaries near their confluences with the lake, and streamflow 

data would need to be collected for a sufficient period of time 

for modeling purposes (likely several years).
26 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

1-foot depth

6-foot depth

12-foot depth

16-foot depth

1-foot depth

6-foot depth

12-foot depth

16-foot depth

A. Site A (295826095082200) B. Site B (295554095093401)

2009

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

2009

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

0

0.05

0.10

0.20

0.15

0

0.05

0.10

0.15

0

0.05

0.10

0.15

0

0.05

0.10

0.15

Salinity, in parts per thousand

0

0.05

0.10

0.20

0.15

0

0.05

0.10

0.15

0

0.05

0.10

0.15

0

0.05

0.10

0.15

Salinity, in parts per thousand

Simulated

Measured

EXPLANATION

Figure 15. Simulated salinity and salinity computed from measured specific conductance measured in 2009 at U.S. Geological Survey 

reservoir stations on Lake Houston, near Houston, Texas. A, 295826095082200 Lake Houston south Union Pacific Railroad Bridge near 

Houston, Tex. B, 295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex.
Development of a Three-Dimensional Hydrodynamic Model 27

1-foot depth 1-foot depth

6-foot depth

12-foot depth

16-foot depth

A. Site A (295826095082200) B. Site B (295554095093401)

2010

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

2010

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

0

0.05

0.10

0.20

0.15

Salinity, in parts per thousand

0

0.05

0.10

0.20

0.15

0

0.05

0.10

0.15

0

0.05

0.10

0.15

0

0.05

0.10

0.15

Salinity, in parts per thousand

Simulated

Measured

EXPLANATION

Figure 16. Simulated salinity and salinity computed from measured specific conductance measured in 2010 at U.S. Geological Survey 

reservoir stations on Lake Houston, near Houston, Texas. A, 295826095082200 Lake Houston south Union Pacific Railroad Bridge near 

Houston, Tex. B, 295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. 
Table 6. Performance evaluation statistics for simulated salinity and salinity computed from measured specific conductance at U.S. Geological Surv295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Texas, and 295554095093401 Lake Houston at mouth of Jack’s Ditch and 2010. [USGS, U.S. Geological Survey; ft, foot; ppt, part per thousand] USGS station number USGS station name Short name Depth (ft) Mean error (ppt) Normalized mean error (dimensionless) Root mean square error (ppt) Norm

root m

square 

(dimens

2009 295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. Site A 1 -0.0053 -0.06 0.011 0.1

295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. Site A 6 -0.0061 -0.07 0.011 0.1295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. Site A 12 -0.0079 -0.09 0.012 0.1295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. Site A 16 -0.0090 0.10 0.013 0.1295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. Site B 1 -0.0054 -0.06 0.011 0.1295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. Site B 6 -0.0055 -0.06 0.011 0.1295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. Site B 12 -0.0056 -0.06 0.011 0.1295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. Site B 16 -0.0060 -0.07 0.012 0.1

2010 295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. Site A 1 -0.0039 -0.04 0.012 0.1

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 1 -0.0056 -0.06 0.013 0.1

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 6 -0.0058 -0.06 0.013 0.1

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 12 -0.0060 -0.06 0.012 0.1

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 16 -0.0063 -0.06 0.013 0.1
Development of a Three-Dimensional Hydrodynamic Model 29

1-foot depth

6-foot depth

12-foot depth

16-foot depth

1-foot depth

6-foot depth

12-foot depth

16-foot depth

A. Site A (295826095082200) B. Site B (295554095093401)

2009

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

2009

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

Water temperature, in degrees Fahrenheit

Water temperature, in degrees Fahrenheit

40

60

80

100

40

60

80

100

40

60

80

100

40

60

80

100

40

60

80

100

40

60

80

100

40

60

80

100

40

60

80

100

110 110

Simulated

Measured

EXPLANATION

Figure 17. Simulated and measured water temperature at U.S. Geological Survey reservoir stations on Lake Houston, near Houston, 

Texas, in 2009. A, 295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. B, 295554095093401 Lake 

Houston at mouth of Jack’s Ditch near Houston, Tex.
30 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

1-foot depth 1-foot depth

6-foot depth

12-foot depth

16-foot depth

A. Site A (295826095082200) B. Site B (295554095093401)

2010

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

2010

Jan. 

Feb. 

Mar. 

Apr. 

May 

June 

July 

Aug. 

Sept. 

Oct. 

Nov. 

Dec. 

Water temperature,

in degrees Fahrenheit

40

60

80

100

110

40

60

80

100

110

40

60

80

100

40

60

80

100

40

60

80

100

Water temperature, in degrees Fahrenheit

Simulated

Measured

EXPLANATION

Figure 18. Simulated and measured water temperature at U.S. Geological Survey reservoir stations on Lake Houston, near Houston, 

Texas, in 2010. A, 295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex. B, 295554095093401 Lake 

Houston at mouth of Jack’s Ditch near Houston, Tex.
Table 7. Performance evaluation statistics for simulated and measured water temperature at U.S. Geological Survey reservoir stations 295826095082Union Pacific Railroad Bridge near Houston, Texas, and 295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex., 2009 and 2010.[USGS, U.S. Geological Survey; ft, foot; °F, degree Fahrenheit]

USGS station 

number USGS station name Short name Depth (ft) Mean error (°F) Normalized mean error (dimensionless) Root mean square error (°F) Norma

root mea

err

(dimens

2009

295826095082200 Lake Houston south Union Pacific Railroad Bridge 

near Houston, Tex.

Site A 1 1.10 0.02 2.39 0.0

295826095082200 Lake Houston south Union Pacific Railroad Bridge 

near Houston, Tex.

Site A 6 0.81 0.02 1.80 0.0

295826095082200 Lake Houston south Union Pacific Railroad Bridge 

near Houston, Tex.

Site A 12 0.05 0.00 1.49 0.0

295826095082200 Lake Houston south Union Pacific Railroad Bridge 

near Houston, Tex.

Site A 16 -0.20 0.00 1.55 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 1 1.20 0.03 3.13 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 6 1.26 0.03 2.72 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 12 1.28 0.03 2.39 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 16 1.21 0.03 2.16 0.0

2010

295826095082200 Lake Houston south Union Pacific Railroad Bridge 

near Houston, Tex.

Site A 1 1.93 0.04 3.01 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 1 2.20 0.04 3.83 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 6 1.93 0.04 3.46 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 12 2.84 0.06 3.64 0.0

295554095093401 Lake Houston at mouth of Jack’s Ditch near 

Houston, Tex.

Site B 16 2.29 0.05 2.93 0.0
32 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Specific conductance and water temperature were only 

collected at two streamflow-gaging stations representing the 

gaged areas of two of the seven major tributaries to Lake 

Houston, so the extrapolation of these data to represent the 

gaged and ungaged areas of all seven watersheds is a potential 

source of error. Salinity rather than specific conductance 

is needed as input in the EFDC model, so any errors 

introduced when salinity is computed from measured specific 

conductance are another source of model uncertainty. There is 

also uncertainty associated with the relations between salinity 

and streamflow derived for the two inflow tributaries where 

specific conductance and water temperature are collected and 

with the salinity to streamflow relations from the gaged areas 

of the two watersheds that were extrapolated to the remaining 

tributaries in the watershed. The methodology used to estimate 

salinity may not yield salinity values that fully characterize 

each of the individual tributaries. Furthermore, because the 

relation between salinity and streamflow for the East Fork 

San Jacinto gage in the eastern watersheds is not as good 

compared to the relation between salinity and streamflow for 

the Spring Creek gage in the western watersheds (evidenced 

by the larger amount of scatter in the salinity values computed 

from measured specific conductance at the East Fork San 

Jacinto gage compared to those computed at the Spring 

Creek gage), there was relatively more uncertainty associated 

with the estimated salinity values computed for the eastern 

watersheds compared to the amount of uncertainty associated 

with estimated salinity values in the western watersheds. 

There also is uncertainty associated with the extrapolation of 

water temperature measured from one tributary in each of the 

western and eastern watersheds to the remaining tributaries 

in the western and eastern watersheds. To more accurately 

characterize the salinity and water temperature for each of 

the tributaries, data would have to be collected at each of the 

individual tributaries. 

The hydraulic structure boundary condition cells used in 

the Lake Houston EFDC model require a rating curve relating 

the hydraulic head of the outflow over the dam to flow rate. 

Although a rating curve for flow over the San Jacinto Dam 

was available, the length of the dam and the fact that it is not 

perfectly level make it difficult to quantify flow rates at the 

low end of the rating curve. To improve the accuracy of this 

rating curve, additional measurements are needed at low flow 

rates.

Simulation of the Effects of Different 

Inflows on Hydrologic Conditions in 

Lake Houston

After the Lake Houston EFDC model was developed 

as explained in the “Development of a Three-Dimensional 

Hydrodynamic Model” section of this report, the model was 

verified by using 2010 input boundary condition data as a 

performance test. Inspection of the spatial distributions of 

the residence time of water, salinity, and water temperature 

obtained from outputs of model simulations using the 2009 

and 2010 data yields insight into the overall hydrodynamics 

of Lake Houston. Spatially distributed plan and profile views, 

as well as five representative cross sections, were chosen to 

illustrate the changes in residence time and salinity over time 

and location in the lake (fig. 19). 

The inflow events on April 19–20, April 28–29, and 

October 30–31, 2009 (fig. 3), in relation to the residence 

time and salinity were assessed to gain an understanding 

of the hydrodynamics of the lake. On the basis of it shape, 

Lake Houston can be conceptually separated into two parts, a 

northern part upstream from the Farm to Market (FM) 1960 

Bridge over Lake Houston and a southern part downstream 

from the FM 1960 Bridge (fig. 19). The inflows to Lake 

Houston from the western and eastern watersheds are less 

well mixed in the northern part of the lake, with the degree 

of mixing of the inflows from the western and eastern 

watersheds increasing in the downstream direction from the 

FM 1960 Bridge (figs. 20 and 21). Plan view and profile view 

plots of the residence time and salinity can lead to a better 

understanding of vertical and horizontal gradients in Lake 

Houston during inflow events. An example of plan and profile 

views is provided of a modeled change in residence time at 

representative cross sections in Lake Houston that resulted 

from an inflow event during April 19–20, 2009 (fig. 20). This 

pattern of the water flowing into the lake from each of the two 

contributing watersheds staying closer to their respective side 

of the lake and not fully mixing until reaching the southern 

part of the lake is evident in all of the events that were studied 

(figs. 20 and 21). 

Larger inflow events can appreciably change the overall 

residence time of water in the lake, whereas smaller inflow 

events do not have much effect on residence time (fig. 22). 

The smaller events preceding the April 19–20, 2009, inflow 

event have greater influence on the upper part of the lake than 

on the lower part of the lake. The April 19–20, 2009, inflow 

event did affect the entire lake, accelerating the movement 

of water through the lake and reducing the residence time at 

all of the five representative cross sections. The April 28–29, 

2009, inflow event then continued to accelerate the movement 

of water through the lake. By considering differences in 

residence time among the five cross sections (fig. 22), patterns 

in the traveltime of the water in Lake Houston become 

evident. For example, it is evident that the traveltime of 

water through the lake is not constant. Smaller events have 

larger traveltimes compared to larger events, and effects on 

residence times may not be propagated all of the way to the 

downstream end of the lake, whereas larger events accelerate 

the movement of water through the lake and, when sufficiently 

large, can completely flush the lake. 
Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston 33

East Fork

San Jacinto

River

Luce Bayou

Lake

 Houston

West Fork San Jacinto River

Creek

Caney

MONTGOMERY COUNTY

HARRIS COUNTY

SAN JACINTO DAM

Union Pacific Railroad

Base from U.S. Geological Survey 1:100,000-scale digital data

Albers Equal Area projection

North American Datum of 1983

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

1960A

1960

2100

59

69

EXPLANATION

Model grid

C C' Line of section

30°03'

29°59'30"

29°56'

95°14'30" 95°11' 95°07'30"

A

A'

B

B'

C

C'

D

D'

E

E'

Figure 19. Locations of representative cross sections used to illustrate simulations from the three-dimensional hydrodynamic model of 

Lake Houston, near Houston, Texas.
34 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

225 1,000 2,000 3,000 4,000 5,000 6,000 7,218

Distance from western shore, in feet

262 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 8,661

Distance from western shore, in feet

360 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,038

Distance from western shore, in feet

50

45

40

35

30

25

20

15

10

50

45

40

35

30

25

20

15

Lake elevation above North American 

Vertical Datum of 1988, in feet

Lake elevation above North American 

Vertical Datum of 1988, in feet

Lake elevation above North American 

Vertical Datum of 1988, in feet

50

45

40

35

30

25

20

15

B. April 18, 2009, 8 p.m.

A. April 18, 2009, 5 p.m.

C. April 19, 2009, 5 p.m.

0 2 4 MILES

0 2 4 KILOMETERS

0 2 4 MILES

0 2 4 KILOMETERS

0 2 4 MILES

0 2 4 KILOMETERS

N

N

N

EXPLANATION EXPLANATION

Water residence time, in days, 

 depth averaged per column

 0

 10

Line of section

Water residence time, in days, 

 per cell

 0

 10

A A' Estimated stage

B B'

A

A'

C

C'

A A'

B B'

C C'

WEST EAST

WEST EAST

WEST EAST

Figure 20. Examples of modeled changes in water residence time in Lake Houston, near Houston, Texas, from April 19, 2009, to April 22, 

2009, in response to an inflow event. Changes in water residence time are depicted with plan views and cross section profile views for 

different dates and times. A, April 18 at 5 p.m. B, April 18 at 8 p.m. C, April 19 at 5 p.m. D, April 21 at 12 a.m. E, April 22 at 12 p.m.
Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston 35

1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,272

Distance from western shore, in feet

607 2,000 3,000 4,000 5,000 6,000 7,044

Distance from western shore, in feet

50

45

40

35

30

25

20

15

10

5

50

45

40

35

30

25

20

15

10

5

Lake elevation above North American 

Vertical Datum of 1988, in feet

Lake elevation above North American 

Vertical Datum of 1988, in feet

E. April 22, 2009, 12 p.m.

D. April 21, 2009, 12 a.m.

0 2 4 MILES

0 2 4 KILOMETERS

0 2 4 MILES

0 2 4 KILOMETERS N

N

EXPLANATION EXPLANATION

Water residence time, in days, 

 depth averaged per column

 0

 10

Line of section

Water residence time, in days, 

 per cell

 0

 10

D D' Estimated stage

D D'

E E'

WEST EAST

WEST EAST

D D'

E

E'

Figure 20. Examples of modeled changes in water residence time in Lake Houston, near Houston, Texas, from April 19, 2009, to April 

22, 2009, in response to an inflow event. Changes in water residence time are depicted with plan views and cross section profile views 

for different dates and times. A, April 18 at 5 p.m. B, April 18 at 8 p.m. C, April 19 at 5 p.m. D, April 21 at 12 a.m. E, April 22 at 12 p.m.—

Continued
36 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

225 1,000 2,000 3,000 4,000 5,000 6,000 7,218

Distance from western shore, in feet

262 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 8,661

Distance from western shore, in feet

360 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,038

Distance from western shore, in feet

50

45

40

35

30

25

20

15

10

50

45

40

35

30

25

20

15

Lake elevation above North American 

Vertical Datum of 1988, in feet

Lake elevation above North American 

Vertical Datum of 1988, in feet

Lake elevation above North American 

Vertical Datum of 1988, in feet

50

45

40

35

30

25

20

15

B. April 18, 2009, 8 p.m.

A. April 18, 2009, 5 p.m.

C. April 19, 2009, 5 p.m.

0 2 4 MILES

0 2 4 KILOMETERS

0 2 4 MILES

0 2 4 KILOMETERS

0 2 4 MILES

0 2 4 KILOMETERS

N

N

N

EXPLANATION EXPLANATION

Salinity, in part per thousand, 

 depth averaged per column

 0.05

 0.11

Line of section

Salinity, in part per thousand, 

 per cell

 0.05

 0.11

A A' Estimated stage

B B'

A

A'

C

C'

A A'

B B'

C C'

WEST EAST

WEST EAST

WEST EAST

Figure 21. Examples of modeled changes in salinity in Lake Houston, near Houston, Texas, from April 19, 2009, to April 22, 2009, in 

response to an inflow event. Changes in salinity are depicted with plan views and cross section profile views for different dates and 

times. A, April 18 at 5 p.m. B, April 18 at 8 p.m. C, April 19 at 5 p.m. D, April 21 at 12 a.m. E, April 22 at 12 p.m.
Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston 37

1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,272

Distance from western shore, in feet

607 2,000 3,000 4,000 5,000 6,000 7,044

Distance from western shore, in feet

50

45

40

35

30

25

20

15

10

5

50

45

40

35

30

25

20

15

10

5

Lake elevation above North American 

Vertical Datum of 1988, in feet

Lake elevation above North American 

Vertical Datum of 1988, in feet

E. April 22, 2009, 12 p.m.

D. April 21, 2009, 12 a.m.

0 2 4 MILES

0 2 4 KILOMETERS

0 2 4 MILES

0 2 4 KILOMETERS N

N

EXPLANATION EXPLANATION

D D'

Salinity, in part per thousand, 

 depth averaged per column

 0.05

 0.11

Line of section

Salinity, in part per thousand, 

 per cell

 0.05

 0.11

Estimated stage

D D'

E E'

WEST EAST

WEST EAST

D D'

E

E'

Figure 21. Examples of modeled changes in salinity in Lake Houston, near Houston, Texas, from April 19, 2009, to April 22, 2009, in 

response to an inflow event. Changes in salinity are depicted with plan views and cross section profile views for different dates and 

times. A, April 18 at 5 p.m. B, April 18 at 8 p.m. C, April 19 at 5 p.m. D, April 21 at 12 a.m. E, April 22 at 12 p.m.—Continued
38 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

2009

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

0

50

100

150

200 0

10,000

20,000

30,000

40,000

50,000

60,000

Depth-averaged residence time, in days

Inflow, in cubic feet per second

Simulated residence time at cross section A–A’

Simulated residence time at cross section B–B’

Simulated residence time at cross section C–C’

Simulated residence time at cross section D–D’

Simulated residence time at cross section E–E’

Inflow from eastern watersheds

Inflow from western watersheds

EXPLANATION

A A'

B B'

C

C'

D D'

E

E'

Lake Houston Environmental Fluid Dynamics Code

model grid showing lines of section

Figure 22. Simulated depth-averaged residence time at each of the five representative cross sections and inflows from the western 

and eastern watersheds for 2009 from the Environmental Fluid Dynamics Code (EFDC) model of Lake Houston, near Houston, Texas.

Multiple smaller events in October 2009 seem to have 

had a cumulative effect on the residence time (fig. 22). There 

were insufficient inflows from the early October events to 

move water completely through the lake, but the combined 

inflows from all of the events during October 1–29, 2009, 

followed by the October 30–31, 2009, event affected the entire 

lake (fig. 22). 

Two scenarios of increased inflow were chosen 

to demonstrate the predictive capabilities of the EFDC 

model. The two scenarios tested used an increased inflow 

of approximately 300 ft3

/s for 1 month (May 2010) from 

two watersheds: the West Fork San Jacinto River and Luce 

Bayou. These two inflow scenarios were chosen to mimic 

the effects of possible small releases or diversions of water 

from outside the San Jacinto River Basin into the basin (or 

directly into the lake) on the residence time of water in Lake 

Houston. These small releases and diversions would likely 

be from upstream lakes and rivers to help meet growing 

water demands in the Houston area. The inflow time series 

for each of the two tested locations (inflow cells shown in 
Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston 39

fig. 10) were modified from the original 2010 data by adding 

the increased inflow evenly distributed over the vertical 

layers at each location. New salinity time series were then 

computed by using equation 5. The effects of the two inflow 

scenarios that were tested are illustrated for cross section D–Dʹ 

(fig. 23). Flow was artificially increased by approximately 

300 ft3/s in May 2010 from the two watersheds, and after this 

1-month period, inflows were decreased to the inflows used to 

calculate the simulated residence time at cross section D–Dʹ 

without the increased inflows. With the increased flows in 

May 2010, the simulated residence time in the lake at cross 

section D–Dʹ decreased, but by early July 2010, the simulated 

residence time in the lake at cross section D–Dʹ returned to 

the simulation residence time in the lake without increased 

inflows. This pattern is as expected with small inflow events 

that have little effect on the overall residence time in the 

lake. As a result of increased inflow in May 2010, maximum 

residence time (as shown by the simulated residence times at 

cross section D–Dʹ with and without increased flow in fig. 23) 

decreased slightly from approximately 106 to 97 days during 

late June 2010. 

Simulated residence time for 2010 at cross section D–D’

 without increased flow

Simulated residence time for 2010 at cross section D–D’

 with increased flow from Luce Bayou

Simulated residence time for 2010 at cross section D–D’

 with increased flow from West Fork San Jacinto River

EXPLANATION

0

20

40

60

80

100

120

140

160

180

Residence time, in days

2010

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

D D'

Luce

Bayou

West Fork San Jacinto River

Lake Houston Environmental Fluid Dynamics Code

model grid showing line of section

Figure 23. Model results for residence time over time at cross section D–Dʹ for 2010 for increased flow from West Fork San Jacinto 

River and increased flow from Luce Bayou to Lake Houston, near Houston, Texas.
40 Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston

Summary

The U.S. Geological Survey, in cooperation with the City 

of Houston, developed a three-dimensional hydrodynamic 

model of Lake Houston, near Houston, Texas, as a tool for 

evaluating the effects of different inflows on residence time 

of water in the lake. Lake Houston is about 25 miles northeast 

from the center of downtown Houston and is an important 

source-water-supply reservoir and recreational resource for 

the Houston metropolitan area. The dam impounding Lake 

Houston was constructed on the San Jacinto River in 1951–53 

by the City of Houston to provide water for public supply and 

irrigation. The reservoir has a capacity of about 146,700 acrefeet and a surface area of 12,240 acres; mean depth is about 

12 feet, and maximum depth is about 50 feet.

The Environmental Fluid Dynamics Code (EFDC), a 

grid-based, surface-water modeling package for simulating 

three-dimensional circulation, mass transport, sediments, 

and biogeochemical processes in surface waters was used for 

modeling Lake Houston. Initial setup of the Lake Houston 

EFDC model required six types of boundary condition data, 

which were applied to either individual grid cells or the entire 

model domain. 

The six types of boundary condition data are 

(1) bathymetric data, (2) flow (inflow and outflow), (3) salinity 

(computed from measured specific conductance), (4) water 

temperature, (5) meteorological, and (6) hydraulic structure 

(the shape and dimensions of the dam impounding Lake 

Houston). The bathymetric data were based on a curvilinear 

horizontal grid configured to have eight vertical layers and 

were obtained from a 1994 hydrographic survey of Lake 

Houston by the Texas Water Development Board. Inflow, 

outflow, salinity, and water temperature boundary conditions 

were formulated from a comprehensive dataset collected at 

10 sites and used in development of the Lake Houston EFDC 

model. Meteorological and solar data were collected at the 

National Weather Service meteorological station at George 

Bush Intercontinental Airport near Houston. Hydraulic 

structure data were based on a rating curve normalized for the 

length of the San Jacinto Dam that was developed to estimate 

outflows over the San Jacinto Dam from lake elevations. 

The Lake Houston EFDC model was initially calibrated by 

using 2009 data and was then verified by using 2010 data. 

To evaluate the Lake Houston EFDC model performance, 

three statistics were used: mean error, root mean square error, 

and the Nash-Sutcliffe model efficiency coefficient (Nash 

coefficient). Normalized mean error and normalized root 

mean square error values also were computed to facilitate 

comparisons of mean error and root mean square error 

results. Simulated and measured lake-elevation values were 

compared at USGS reservoir station 08072000 Lake Houston 

near Sheldon, Tex. The accuracy of simulated salinity and 

water temperature values was assessed by using the salinity 

(computed from measured specific conductance) and water 

temperature that were measured at two USGS reservoir 

stations: USGS reservoir station 295554095093401 Lake 

Houston at mouth of Jack’s Ditch near Houston, Tex., and 

USGS reservoir station 295826095082200 Lake Houston 

south Union Pacific Railroad Bridge near Houston, Tex. 

Specific conductance and water temperature were measured at 

as many as four different depths at each of the two reservoir 

stations during 2009 and then used for assessing the accuracy 

of simulated values of salinity and water temperature during 

2010. The performance evaluation statistics indicate that the 

model performed satisfactorily. The normalized mean error 

and normalized root mean square error for the differences 

between measured and simulated values for lake elevation, 

salinity, and temperature all closely approximated zero, and 

the Nash-Sutcliffe model efficiency coefficients were all larger 

than 0.50, indicative of a satisfactory match between the 

simulated and measured data. 

To understand the limitations of the Lake Houston EFDC 

model it is important to understand the sources of model 

uncertainty. Inaccuracies may be attributed to the changes 

in Lake Houston bathymetric surface, adjustments made 

to inflows in the model from streamflow rates measured at 

upstream streamflow-gaging stations determined by using 

drainage area to flow relations, specific conductance and water 

temperature data being collected for only two of the seven 

major tributaries, errors in salinity values for inflows estimated 

from the specific conductance data, errors in salinity values 

estimated for the lake, and errors associated with the rating 

curve for outflow over the dam. Overall, these uncertainties 

did not diminish the utility of this model as shown by the 

performance evaluation statistics. 

Two scenarios of small increases in inflows were chosen 

to demonstrate the predictive capabilities of the EFDC 

model. The two scenarios tested were an increased inflow of 

approximately 300 cubic feet per second for 1 month (May 

2010) from two watersheds: the West Fork San Jacinto River 

and Luce Bayou. These scenarios were chosen to mimic the 

effects of possible small releases or diversions of water from 

outside the San Jacinto River Basin into the basin (or directly 

into the lake) on the residence time of water in Lake Houston. 

During the time of increased inflow for the two scenarios 

tested, maximum residence time decreased slightly from 

approximately 106 to 97 days. 

References Cited

Beussink, A.M., and Burnich, M.R., 2009, Continuous and 

discrete water-quality data collected at five sites on Lake 

Houston near Houston, Texas, 2006–08: U.S. Geological 

Survey Data Series 485, 18 p.

Beussink, A.M., and Graham, J.L., 2011, Relations between 

hydrology, water quality, and taste-and-odor causing 

organisms and compounds in Lake Houston, Texas, April 

2006–September 2008: U.S. Geological Survey Scientific 

Investigations Report 2011–5121, 27 p.
References Cited 41

Chow, V.T., Maidment, D.R., and Mays, L.W., 1988, Applied 

hydrology: New York, McGraw-Hill Book Co., 572 p.

City of Houston, 2015, Public utilities division—Drinking 

water operations: City of Houston, accessed June 30, 

2015, at http://www.publicworks.houstontx.gov/pud/

drinkingwater.html.

Conrad, D.R., 1986, The physicochemical limnology of Lake 

Houston reservoir, Houston, Texas: Nacogdoches, Tex., 

Stephen F. Austin State University, master’s thesis, 207 p.

Dowell, C.L., 1964, Dams and reservoirs in Texas—Historical 

and descriptive information: Texas Water Commission 

Bulletin 6408, accessed March 18, 2015, at http://www.

twdb.texas.gov/publications/reports/bulletins/doc/B6408.

pdf.

Dynamic Solutions, LLC, 2015, Hydraulics & hydrodynamics: 

Accessed July 29, 2015, at http://www.dsllc.com/services/

hydraulics-hydrodynamics/.

Esri, 2011, ArcGIS Resource Center—Watershed 

(Spatial Analyst): Accessed August 6, 2015, at http://

help.arcgis.com/en/arcgisdesktop/10.0/help/index.

html#//009z00000059000000.htm.

Esri, 2015, ArcGIS Desktop—Release 10: Esri, Redlands, 

Calif. [Also available at http://www.esri.com.]

Hamrick, J.M., 1992, A three-dimensional Environmental 

Fluid Dynamics Computer Code—Theoretical and 

computational aspects: The College of William and Mary, 

Virginia Institute of Marine Science, Special Report 317, 

63 p.

Hamrick, J.M., 2007, The Environmental Fluid Dynamics 

Code user manual, US EPA version 1.0: Fairfax, Va., Tetra 

Tech, Inc., 231 p., accessed September 15, 2015, at http://

www2.epa.gov/exposure-assessment-models/environmentfluid-dynamics-code-efdc-download-page.

Helsel, D.R., and Hirsch, R.M., 2002, Statistical methods in 

water resources: U.S. Geological Survey Techniques of 

Water-Resources Investigations, book 4, chap. A3, 510 p. 

Hem, J.D., 1985, Study and interpretation of the chemical 

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Rendon and Lee—Simulation of the Effects of Different Inflows on Hydrologic Conditions in Lake Houston—SIR 2015–5153

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155153