Section 7.0 Soil Data

7-1

7.0 Soil Data

The 3MRA modeling system is designed to use a regional site-based approach to estimate

risks from waste management units on a regional or national basis. To create a representative

national data set for model testing and demonstration, EPA used a site-based data collection

strategy centered around 201 sites randomly selected from the 1985 Industrial D Screening

Survey. Site-specific soil data were collected for all 201 sites, largely using automated methods.

Geographic information system (GIS) programs were used to identify and extract, for each

Industrial D site modeled, soil map units and map unit areas by watershed subbasin and by waste

management unit (WMU). Database programs were used to extract soil data from the underlying

databases by these map units and process them to generate soil properties by watershed for

surface soil and by the WMU for the entire soil column (vadose zone or subsoil). Surface soil is

defined as the top 20 cm within the 3MRA modeling system. Vadose zone soil extends from the

ground surface to the water table. Depending on the property, average (area- and depthweighted) or predominant soil properties were derived for the soil depth zone of interest across

each watershed subbasin or WMU. 

Section 5 describes the GIS and database processing used to delineate the watershed

subbasins and local watersheds (which include the WMU) for which soil data were collected.

This section describes the collection and processing of soil data for these entities, including the

parameters collected (Section 7.1), the data sources (Section 7.2), the overall data collection

methodology (Section 7.3), and the data collection results by parameter (Section 7.4).

Section 7.5 describes quality assurance and quality control (QA/QC) measures. Significant

uncertainties are discussed by parameter in Section 7.4 and summarized in Section 7.5.

7.1 Parameters Collected

Table 7-1 lists the soil and universal soil loss equation (USLE) soil erosion input

parameters required by the various modules. Soil data are primarily used by the source,

Watershed, and Vadose Zone Modules, but fraction organic carbon is used by the Farm Food

Chain, Terrestrial Food Web, Human Exposure, and Ecological Risk Modules. Additionally, the

Farm Food Chain Module uses soil pH. This section also includes the land use-based water

erosion inputs, the USLE cover factor (C), and the USLE erosion control factor (P). Collection

of the USLE rainfall erosivity factor (R) and the USLE length/slope factor (LS) are described in

Section 4 and Section 5, respectively. 

In terms of collection scale, all soil parameters can be considered to be site-based in that

they are either site-specific or derived from site-specific data using national relationships. The

latter category includes soil hydrologic properties derived from site-specific soil texture or
Section 7.0 Soil Data

7-2

hydrologic class and other properties derived from a combination of soil texture or class and sitespecific land use.

Table 7-1. Soil Inputs by Module 

Model Input

Source Modules 

Media

Modules Food Chain & Exp. Modulesa

LF LAU SI WP WS

Vadose

Zone

Farm

Food

Chain TFW

Human

 Exp.

Eco

Exp.

depth of root zone ! !!

dry bulk density (subsoil) !

field capacity ! !!

fraction organic carbon (cover soil) !

fraction organic carbon (surface soil) ! !! ! ! ! !

fraction organic carbon (subsoil) !

hydrologic soil group !1 !1 !1

percentage organic matter (subsoil) !

pH (subsoil) ! !

residual water content (subsoil) !

saturated hydraulic conductivity (cover

soil) !

saturated hydraulic conductivity (surf.

soil) ! !!

saturated hydraulic conductivity (subsoil) !! !

saturated water content (cover soil) !

saturated water content (surface soil) ! !!

saturated water content (subsoil) ! !

SCS curve number ! !!

silt content (surface soil at LAU) !

soil column temperature (annual average) ! !

soil moisture coefficient b (cover soil) !

soil moisture coefficient b (surface soil) ! !!

soil moisture coefficient b (subsoil) !

soil parameter, alpha (subsoil) ! !

soil parameter, beta (subsoil) ! !

soil type/texture (subsoil) !1

USLE cover factor (C) ! !!

USLE erodibility factor (K) ! !!

USLE erosion control factor (P) ! !!

wilting point ! !!

a

 Parameter used to determine correct cross-correlation for soil properties.

LF = landfill; LAU = land application unit; SI = surface impoundment; WP = waste pile; WS = watershed; TFW = terrestrial

food web; Hum Exp = human exposure; Eco Exp = ecological exposure
Section 7.0 Soil Data

7-3

7.2 Data Sources

Table 7-2 shows the data sources used for soil properties and erosion factors. The

primary sources for site-specific soil properties were the State Soil Geographic (STATSGO)

database maintained by the U.S. Department of Agriculture (USDA, 1994a) and two GIS-based

compilations of STATSGO data, USSOILS (Schwarz and Alexander, 1995) and the

Conterminous United States Multi-Layer Soil Characteristics (CONUS) data set (Miller and

White, 1998). USSOILS, maintained by the U.S. Geological Survey (USGS), averages

STATSGO data by map unit, with depth-weighted averages to the water table. CONUS, created

by Pennsylvania State University, averages by STATSGO map unit and converts STATSGO soil

layers into a set of 11 standardized soil layers extending to a depth of 2.5 m (60 in).

Various lookup tables based on STATSGO soil texture and hydrologic soil group were

used for parameters not readily available using STATSGO-based data sources alone. Only one

soil parameter, soil column temperature, was not derived using STATSGO-based sources. The

land use-based USLE factors were obtained using lookup tables by Anderson land use codes

obtained for each site from the Geographic Retrieval and Analysis System (GIRAS) land use

database.

7.2.1 STATSGO Data

STATSGO (USDA, 1994a) is a GIS database designed primarily for regional, multistate,

river basin, State, and multicounty planning, managing, and monitoring resources. Soil maps for

STATSGO are compiled from more detailed county soil survey maps. When county soil survey

maps are not available, data on geology, topography, vegetation, and climate are assembled,

together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas are

studied, and the probable classification and extent of the soils are determined.

Using the USGS 1:250,000 scale, 1- by 2-degree quadrangle series as a map base, the soil

data are digitized by vector method to comply with national guidelines and standards. Data for

the STATSGO database are collected in 1- by 2-degree topographic quadrangle units and

merged and distributed as statewide coverages. Features are edge-matched between states. The

STATSGO data provide national coverage at a scale of 1:250,000, except for Alaska, which is at

a scale of 1:200,000.

The approximate minimum area delineated is 625 ha (1,544 acres), which is represented

on a 1:250,000-scale map by an area approximately 1 cm by 1 cm (0.4 in by 0.4 in). Linear

delineations are no less than 0.5 cm (0.2 in) in width. The number of delineations per 1:250,000

quadrangle typically is 100 to 200 but may range up to 400. Delineations depict the dominant

soils making up the landscape. Other dissimilar soils, too small to be delineated, are present

within a delineation.

Attribute accuracy is tested by manual comparison of the source with hardcopy plots

and/or symbolized display of the map data on an interactive computer graphic system. Selected 
Section 7.0 Soil Data

7-4

Table 7-2. Soil Property Data Sources

Parameter Code Source Description

depth (root zone) DRZ Dunne and Leopold (1978) lookup table by soil texture and

land use

dry bulk density (subsoil) RHOB U.S. EPA (1996) calculated from saturated water

content

field capacity SMFC Carsel et al. (1988) lookup table by hydrologic soil

group

fraction organic carbon (cover soil) focC U.S. EPA (1996) calculated from USSOILS POM

fraction organic carbon (surface soil) focS U.S. EPA (1996) calculated from STATSGO POM

fraction organic carbon (subsoil) focS_lf U.S. EPA (1996) calculated from USSOILS POM

hydrologic soil group HydroGroup USDA (1994a) derived from STATSGO data

percentage organic matter (subsoil) POM Schwarz and 

Alexander (1995)

USSOILS

pH (subsoil) FarmPh, VadPh,

WSPh

USDA (1994b) derived from STATSGO data

residual water content (subsoil) VadWCR Carsel and Parrish (1988) lookup table by soil texture

saturated hydraulic conductivity

(cover soil)

KsatC Carsel and Parrish (1988) lookup table by soil texture

saturated hydraulic conductivity (surf.

soil)

Ksat Carsel and Parrish (1988) lookup table by soil texture

saturated hydraulic conductivity

(subsoil)

VadSATK Carsel and Parrish (1988) lookup table by soil texture

saturated water content (cover soil) WCS_C Carsel and Parrish (1988) lookup table by soil texture

saturated water content (surface soil) WCS Carsel and Parrish (1988) lookup table by soil texture

saturated water content (subsoil) VadWCS Carsel and Parrish (1988) lookup table by soil texture

SCS curve number CN USDA (1986) lookup table by hydrologic soil

group and land use

silt content (soil at lau) Ss Miller and White (1998) derived from CONUS data

soil column temperature (annual

average)

VadTemp Collins (1925) same as ground water temp.

soil moisture coefficient b (cover soil) SMbC Clapp and Hornberger (1978) lookup table by soil texture

soil moisture coefficient b (surface

soil)

SMb Clapp and Hornberger (1978) lookup table by soil texture

soil moisture coefficient b (subsoil) SMbS Clapp and Hornberger (1978) lookup table by soil texture

soil parameter, alpha (subsoil) VadALPHA Carsel and Parrish (1988) lookup table by soil texture

soil parameter, beta (subsoil) VadBETA Carsel and Parrish (1988) lookup table by soil texture

soil type/texture (subsoil) VadSoilType,

SoilType

Miller and White (1998) derived from CONUS

USLE cover factor C Wanielista and Yousef (1993) lookup table by land use

USLE erodibility factor K Schwarze and Alexander

(1995)

USSOILS

USLE erosion control factor P Wanielista and Yousef (1993) lookup table by land use

wilting point SMWP Carsel and Parrish (1988) lookup table by hydrologic soil

group
Section 7.0 Soil Data

7-5

attributes that cannot be visually verified on plots or on screen are interactively queried and

verified on screen. In addition, the attributes are tested against a master set of valid attributes. 

All attribute data conform to the attribute codes in the signed classification and correlation

document and amendments and are current as of the date of digitalizing.

A map unit is a collection of areas defined and named the same in terms of their soil

and/or nonsoil areas. Each map unit differs in some respect from all others in a survey area and

is uniquely identified. Each individual area is a delineation. Each map unit consists of 1 to

21 components.

In those few areas where detailed maps do not exist, reconnaissance soil surveys were

combined with data on geology, topography, vegetation, climate, and remote sensing images to

delineate map units and estimate the percentages of the components. The STATSGO map unit

components are soil series phases, and their percentage composition represents the estimated

area proportion of each within the STATSGO map unit. The composition for a map unit is

generalized to represent the statewide extent of that map unit and not the extent of any single

map unit delineation. These specifications provide a nationally consistent representation of

STATSGO attribute data. 

Refer to metadata available on the Internet at http://www.ftw.nrcs.usda.gov/stat2.html

(USDA, 1994a) for more information on STATSGO. The STATSGO User’s Guide (USDA,

1994b) provides details on STATSGO database structures and definitions.

USSOILS (Schwarz and Alexander, 1995) and CONUS (Miller and White, 1998) are

simplified compilations of STATSGO data. They were used in this HWIR risk assessment to

reduce data processing requirements for certain soil parameters. The USSOILS coverage was

originally compiled to support a national model of water quality. USSOILS aggregates the

STATSGO layer and component information up to the level of a map unit by depth-averaging,

over the entire soil column, median properties within a component and then area-averaging

component values across a map unit. Metadata on USSOILS can be found at:

http://water.usgs.gov/lookup/getspatial?ussoils.

The CONUS soil data set was compiled by the Earth System Science Center in the

College of Earth and Mineral Sciences at Pennsylvania State University for application to a wide

range of climate, hydrology, and other environmental models (Miller and White, 1998). CONUS

contains STATSGO soil properties averaged to 11 standard layer depths from the STATSGO

layers to a depth of 2.5 m. Within each STATSGO map unit and CONUS standard layer, soil

properties either represent the predominant property (as with soil texture) or are area-weighted

averages of STATSGO component values. Additional information on CONUS can be found at:

http://www.essc.psu.edu/soil_info/index.cgi?soil_data&conus.

7.2.2 GIRAS Data

GIRAS (U.S. Environmental Protection Agency [EPA], 1994) provides comprehensive

land use data, in digital GIS format, for the conterminous United States. This spatial data set

represents digital data originally collected by the USGS (USGS, 1990) and then converted into
Section 7.0 Soil Data

7-6

the ARC/INFO GIS format by EPA. These digital coverages are available from EPA by 1-

degree quadrangles (1:250,000 scale). This information was extracted from metadata available

on GIRAS. The full metadata record may be found at

http://www.epa.gov/ngispgm3/nsdi/projects/giras.htm (U.S. EPA, 1994). 

GIRAS land use/land cover (LU/LC) data are useful for environmental assessment of

land use patterns based on water quality analysis, growth management, and other types of

environmental impact assessment. Data are meant to be used by quadrangle, or among adjacent

quadrangles where they are temporally contiguous. The data can be used in any geographic

application where intermediate scale land use data are appropriate and the dates are

representative of the need. Each quadrangle of land use data has a different representative date.

Date ranges from mid-1970s to early 1980s are common. Because the data are about 15 to 25

years old, their use may be limited. 

When joined together, quadrangles will not likely match along edges due to differences

in interpretation and time coverage. Edges of each map file were manually digitized and may not

join neighboring maps. For some quadrangles, edges have been mathematically recalculated

(using a GIRASNEAT Arc Macro Language [AML] program) to join without overlap or gaps in

coverage with adjacent maps. 

The GIRAS series includes several themes of spatial data. The LU/LC data were used in

HWIR. LU/LC in GIRAS was mapped and coded using the Anderson classification system

(Anderson et al.,1976), which is a hierarchical system of general (one-digit, level 1) to more

specific (two-digit, level 2) characterizations (see Table 7-3).

7.2.3 National Data Sources

Nationwide relationships from various sources were used as lookup tables in conjunction

with STATSGO and GIRAS site-specific data. Carsel and Parrish (1988) provide transformed

distributions by soil texture for saturated hydraulic conductivity, residual water content, and 

water retention parameters alpha and beta. Clapp and Hornberger (1978) provide values for the

soil moisture coefficient b by soil texture. Carsel et al. (1988) have values for field capacity and

wilting point by hydrologic soil group and layer. A lookup table for root zone depth by

Anderson land use code and soil texture was developed using a Dunne and Leopold (1978) table

of rooting depth by vegetative cover and soil texture. Similarly, a lookup table for Soil

Conservation Service (SCS) curve number by Anderson land use code and hydrologic soil group

was developed using tables of curve number by ground cover and hydrologic soil group from

Urban Hydrology for Small Watersheds (USDA, 1986). As with the soil properties, lookup

tables for USLE factors C and P by land use were developed using Wanielista and Yousef's

(1993) tables of C and P factors by land use.

7.3 Methodology

A largely automated methodology was employed to collect soil data for the 3MRA

modeling system representative national data set. Figure 7-1 shows an overview of this

methodology, which employs both GIS and conventional database processing to collect soil

property data by map unit 
Section 7.0 Soil Data

7-7

Table 7-3. Anderson Land Use Codes

1 Urban or builtup land 5 Water

11 Residential 51 Streams and canals

12 Commercial and services 52 Lakes

13 Industrial 53 Reservoirs

14 Transportation, communication, utilities 54 Bays and estuaries

15 Industrial and commercial complexes 6 Wetland

16 Mixed urban or builtup land 61 Forested wetland

17 Other urban or builtup land 62 Nonforested wetland

2 Agricultural land 7 Barren land

21 Cropland and pasture 71 Dry salt flats

22 Orchards, groves, vineyards, nurseries,

and ornamental horticultural land

72 Beaches

73 Sandy areas not beaches

23 Confined feeding operations 74 Bare exposed rock

24 Other agricultural land 75 Strip mines, quarries, gravel pits

3 Rangeland 76 Transitional areas

31 Herbaceous rangeland 8 Tundra

32 Shrub and brush rangeland 81 Shrub and brush tundra

33 Mixed rangeland 82 Herbaceous tundra

4 Forest land 83 Bare ground

41 Deciduous forest land 84 Wet tundra

42 Evergreen forest land 85 Mixed tundra

43 Mixed forest land 9 Perennial snow or ice

91 Perennial snowfields

92 Glaciers

Source: Anderson et al. (1976)
7-8

 Watershed Data Processing Soil Data · STATSGO · CONUS · USSOILS · literature values Watershed Soil P

by texture: sat. hydraulic consat. water content soil moisture coefroot zone depth by hydro. group: field capacity wilting point SCS curve numbedepth weighted: pHfraction organic caUSLE erodibility f

Vadose Zone Data Processing Vadose Zone Soi

by texture: alpha beta sat. hydraulic conresidual water consat. water content soil moisture coefbulk density depth weighted: percent organic mfraction organic casilt content pH GIS Processing STATSGO Mapunits Mapunit for WMU Mapunit by Watershed Database Processing Data to M

Figure 7-1. 3MRA modeling system soil data collection overview.
Section 7.0 Soil Data

7-9

for watershed subbasin or WMU. Because GIS spatial soil data coverages were available for the

entire United States, site-specific data were collected for all 201 sites. For a particular spatial

area of concern (e.g., watershed subbasin or WMU), STATSGO map units were obtained using

GIS. Map unit data were then passed to a Microsoft Access database containing soil data by map

unit from STATSGO, USSOILS, and CONUS, along with lookup tables for the nationwide

relationships. Similarly, GIRAS coverages for the areas surrounding each site were used to

obtain site-specific land use data, which were passed to the data processor to generate land usebased inputs.

Database query programs processed the soil data by watershed or WMU to generate the

3MRA modeling system inputs shown in Figure 7-1. Parameters collected by the watershed map

units included soil properties for the Watershed Module as well as those collected for the local

watersheds in the LAU and Waste Pile Modules. Although the LAU and waste pile soil

parameters were indexed by local watershed and subarea, they are the values for the watershed

subbasin in which the local watershed resides. Soil parameters collected by the WMU map units

were used to provide subsoil data for the vadose zone model and subsoil and cover soil data for

the Landfill Module. 

7.3.1 STATSGO GIS Processing

Soil map unit data contained in STATSGO were downloaded as a single national

ARC/INFO coverage from a University of Pennsylvania Web site. A soil polygon coverage was

overlaid with the regional watershed subbasin coverage to identify map units within the

subbasins; it was also overlaid with the WMU to identify map units within the WMU

(Figure 7-2). For each watershed subbasin and WMU, GIS was used to calculate the area of the

subbasin or WMU that was covered by the map unit. Map unit assignments were exported for

data processing in two GIS tables (w_all and o_all in Figure 7-3) The o_all table includes map

units by watershed subbasin and site, and the w_all table includes map units by WMU type and

site.

7.3.2 GIRAS GIS Processing

The GIRAS coverages were downloaded from an EPA Web site as ARC/INFO

coverages. GIS was used to overlay the regional watershed coverage with a nationwide

coverage of all the GIRAS quadrangle names. Each quadrangle that touched a watershed

subbasin was then combined to create one coverage (Figure 7-2). The GIRAS coverage was

then clipped to the regional watershed boundaries. The Anderson land use classification code in

GIRAS is contained in the two-digit lucode attribute in the polygon attribute table. The first

digit is the Anderson level 1 value, and the second digit is the level 2 value (U.S. EPA, 1994). 

GIS identified all land use types (lucode) within the watershed subbasin and calculated their

areas. A GIS land use data table (u_all in Figure 7-3) contains land use data by watershed and

site.

7.3.3 Database Processing

GIS soil map unit and land use tables were used along with various compilations of

STATSGO data and static lookup tables to provide the soil and land use data required by the
Section 7.0 Soil Data

7-10

Figure 7-2. Sample soil and land use coverages.

GIRAS Landuse Symbology
Section 7.0 Soil Data

7-11

site_num (IE)

wshd_num (IE)

muid (IE)

area

o_all

site_num (IE)

wshd_num (IE)

lucode (IE)

area

u_all

site_num (IE)

wmu_type (IE)

muid (IE)

area

w_all

GIS SOIL and Land Use Tables

* muid - map unit identifier

Figure 7-3. GIS soil and land use data tables.

modules. The flow chart in Figure 7-4 provides an overview of the soil database processing. 

Using the GIS map unit assignments, site-specific data for soil parameters were queried from a

soil database composed of STATSGO, USSOILS, and CONUS data tables. Other inputs (e.g.,

soil hydrologic properties) were derived from lookup data tables using the site-specific

STATSGO USDA soil texture class or hydrologic soil group. A couple of the soil parameters,

depth of root zone and SCS curve number, along with the USLE cover factor and erosion control

factor, required site-specific land use information by watershed, obtained using the GIS GIRAS

data. As with soil hydrologic properties, this land use data was related to the parameters of

interest using lookup tables.

7.4 Results by Parameter

The following sections describe the data collection process and results by each

soil-related input parameter. Significant assumptions and uncertainties are also provided by

parameter.

7.4.1 Soil Texture

USDA soil texture was derived from CONUS data by layer and map unit for the top

20 cm of soil (soil) and for the entire soil column (subsoil). Even though only the soil texture for

the entire soil column was passed explicitly in the data, many of the other parameters came from

lookup tables based on the predominant texture in either surface soil or the entire soil column.

7.4.1.1 Database Compilation for Soil Texture. CONUS texture data are given by

11 standard layers with bottom depths of 5, 10, 20, 30, 40, 60, 80, 100, 150, 200, and 250 cm.

The CONUS data were used to create tables of soil texture by map unit for the entire soil column

and for the top 20 cm of soil. Possible values in CONUS for the texture are shown in Table 7-4. 
Section 7.0 Soil Data

7-12

STATSGO

map unit layer

predominant soil

texture 3

USLE erodibility

factor

pH

silt content

percent organic

matter

fraction

organic

carbon

sat. water content

moisture retention

parameters

(alpha/beta)

hydrologic

soil group

SCS curve

number

GIRAS

landuse

STATSGO SCS curve

number 1

moisture

retention

parameters

(alpha/beta) 1

sat. water

content 3

pH 1

silt content 2

soil texture 1

fraction organic

carbon 2

USLE erodibility

factor 1

soil moisture

coefficient b

soil moisture

coefficient b 3

percent organic

matter 1

STATSGO

CONUS

STATSGO

USSOILS

CONUS

Carsel & Parrish (1988)

Clapp & Hornberger

(1978)

depth-weighted

average

percent organic

matter

USSOILS

fraction organic

carbon

fraction organic

carbon 1

calculated

calculated

res. water content

sat. hydraulic

conductivity

residual water

content 1

sat. hydraulic

conductivity 3

predominant by

depth

USDA (1986)

root zone depth

Dunne & Leopold (1978)

bulk

density

calculated

soil bulk density 1

root zone depth 2

wilting point

field capacity

Carsel et al (1988)

hydrologic soil

group 1

wilting point 1

field capacity 1

componentweighted

average by

predominant

soil texture

* shaded areas represent data passed by GIS

1 - entire soil column

2 - top 20 cm of soil

3 - both entire soil column and top 20 cm

Figure 7-4. 3MRA modeling system soil data processing.
Section 7.0 Soil Data

7-13

Table 7-4. Soil Textures in CONUS

CONUS Abbreviation Soil Texture

ND no data

S sand

LS loamy sand

SL sandy loam

SIL silt loam

SI silt

L loam

SCL sandy clay loam

SICL silty clay loam

CL clay loam

SC sandy clay

SIC silty clay

C clay

OM organic materials

W water

BR bedrock

O other

# Soil texture for the entire soil column was defined for each map unit as the

predominant soil texture weighted by layer depth for all 11 CONUS layers,

excluding layers with textures of no data, bedrock, and other. When two soil

textures had equal predominance (a tie), the texture that occurs most commonly

across the United States was used.

# Soil texture for the top 20 cm of soil was defined for each map unit as the

predominant soil texture weighted by layer depth for the top three CONUS layers,

excluding layers with textures of no data, bedrock, and other. When there was a

tie between textures, the texture that occurs most commonly across the United

States was used.
Section 7.0 Soil Data

7-14

7.4.1.2 Database Processing for Soil Texture. GIS programs were used to obtain the

map units and portions of each map unit in the area concerned (e.g., watershed or WMU) for

each site or site/WMU combination. The map unit with the largest area (that did not have a

texture of water) in the area of concern was then used to assign a soil texture from the preexisting tables prepared as described earlier.

7.4.1.3 Assumptions and Uncertainty for Soil Texture. To simplify data processing,

CONUS, which contains a limited number of basic soil textures by standard layers, was chosen

over the STATSGO raw data, which contain a greater number of textures by component-specific

layer thickness (i.e., STATSGO lay_thick varies by component). Further processing was then

done to simplify and compile CONUS data to a level of resolution appropriate for the 3MRA

modeling system modules (i.e., the Watershed Module requires homogeneous soil properties

with depth and across a watershed subbasin; the Vadose Zone Module requires a homogeneous

soil column). Because of this processing, soil textures are assumed to be homogeneous when

they actually may vary by layer and component.

7.4.2 Hydrologic Soil Group

STATSGO, which contains hydrologic soil groups by component, was used as the data

source for this parameter. To ensure that the hydrologic soil group reflected the predominant

soil texture used to derive other soil properties for a particular map unit, only the components

with the same predominant texture as determined from CONUS data were used to determine the

hydrologic soil group. The hydrologic soil group was passed explicitly to provide the values on

which the cross-correlation for field capacity and wilting point are based and also was used to

derive other parameters (i.e., curve number, wilting point, and field capacity) from lookup tables.

7.4.2.1 Database Compilation for Hydrologic Soil Group. Although STATSGO

contains hydrologic soil groups by components, independent of layer, soil texture is given by

layer and not limited to the 17 textures possible in CONUS. The first step in the risk assessment

process was to derive textures by component from STATSGO layer data. The process used to do

this closely matches the CONUS methodology for deriving texture for the standardized CONUS

layers. Descriptors were first stripped from textures in STATSGO. The descriptors are

adjectives such as cherty, gravelly, peaty, and stony, that are commonly followed by a dash

before the actual texture. Because STATSGO may contain more than one texture per layer, the

first texture after the descriptors was chosen to be the representative texture for that layer. This

usage is consistent with CONUS documentation. The final stripped texture was then matched to

the CONUS textures using a crosswalk table (Table 7-5). The predominant texture for the entire

STATSGO component was determined by summing the layer depths by soil texture, excluding

bedrock and other textures, and using the soil texture with the greatest depth. To be consistent

with CONUS, if the lowest layer did not extend to bedrock, then the layer was extended to

bedrock or to 2.5 m (60 in, the bottom of the lowest CONUS layer), whichever was encountered

first.

The second part of the process required obtaining an “average” hydrologic soil group for

all of the components within each map unit that had the same predominant texture as the

predominant CONUS texture for the entire soil column. After using the texture to obtain only

the hydrologic soil groups for the components with the same texture as the predominant texture 
Section 7.0 Soil Data

7-15

Table 7-5. Crosswalk between STATSGO and CONUS Soil Textures

STATSGO CONUS Description STATSGO CONUS Description

IND BR bedrock FRAG O other

MARL BR bedrock GYP O other

UWB BR bedrock ICE O other

VAR WB BR bedrock VAR O other

WB BR bedrock CIND GRX-S S sand

C C clay CIND S S sand

CL CL clay loam COS S sand

G G1 gravel FS S sand

L L loam S S sand

LCOS LS loamy sand SG S sand

LFS LS loamy sand VFS S sand

LS LS loamy sand SC SC sandy clay

LVFS LS oamy sand SCL SCL

sandy clay

loam

CE OM organic materials DE SI SIL SI silt

FB OM organic materials SI SI silt

HM OM organic materials SIC SIC silty clay

MPT OM organic materials CEM SICL SICL

silty clay

loam

MUCK OM organic materials SICL SICL

silty clay

loam

PEAT OM organic materials SIL SIL silt loam

SP OM organic materials COSL SL sandy loam

CEM O other FSL SL sandy loam

CIND O other SL SL sandy loam

DE O other VFSL SL sandy loam

1 G (gravel) is not one of the CONUS textures but was retained because it does not match well to a

CONUS texture.
Section 7.0 Soil Data

7-16

for the entire map unit, the hydrologic soil groups were averaged. Using a conversion similar to

the one in USSOILS, hydrologic soil groups were assigned numbers to allow numeric averaging

(Table 7-6).

The new numeric-equivalent hydrologic soil groups were then averaged, weighted by the

percent area of the map unit occupied by that component. The average numeric hydrologic soil

groups were then converted back to letters using the following criteria:

Average Letter

# 1.5 A

> 1.5 and # 2.5 B

> 2.5 and # 3.5 C

> 3.5 D

The final result was a table of hydrologic soil groups by map unit based on soil texture. 

Even though every attempt was made to mimic CONUS processing, occasionally no component

within a map unit had the same predominant texture as that map unit (this occurred in 370 out of

the 10,388 map units that had texture data derived from CONUS). These map units were filled

in with the hydrologic soil type most common nationally (B) and marked as infilled in the

database.

7.4.2.2 Database Processing for Hydrologic Soil Group. As with soil texture, the map

unit with the largest area (that did not have a texture of water) in the area concerned was used to

derive the hydrologic soil group from the pre-existing table described earlier.

7.4.2.3 Assumptions and Uncertainty for Hydrologic Soil Group. The process by

which the predominant texture was derived for each STATSGO component resulted in 370 out

of 10,388 map units that did not have any components of the same texture as the predominant

CONUS texture. This indicates that, although a process similar to that of CONUS was

employed, there may be some differences in the results due to the processing to standard layers

in CONUS versus processing to STATSGO component. The use of the average soil hydrologic

group, however, should minimize any potential bias for these few map units. 

7.4.3 Field Capacity

Field capacity was obtained using a lookup table from Carsel et al. (1988) of field

capacity by hydrologic soil group and layer. Median values were used because field capacity is

correlated with saturated water content (i.e., total porosity), yet Carsel et al. (1988) do not

provide correlation parameters.
Section 7.0 Soil Data

7-17

Table 7-6. Hydrologic Soil Groups

Hydrologic Group Number Description

A 1 well- to excessively drained sands and gravels (high infiltration rates)

B 2

moderately well- to well-drained soils with moderate textures (moderate

infiltration rates)

C 3

moderately to poorly drained soils with moderately fine to fine textures (slow

infiltration rates)

D, A/D, B/D, C/D 4 poorly drained soils with very fine texture (very slow infiltration rates)

A/C, B/C, 2, VAR ----

7.4.3.1 Database Compilation for Field Capacity. A table for field capacity from

Carsel et al. (1988) was entered into the database as a lookup table, including hydrologic soil

group, layer, distribution type (constant), and median value (Table 7-7).

7.4.3.2 Database Processing for Field Capacity. The map unit with the largest area in

the area concerned was used to obtain the hydrologic soil group that was then used with the

lookup table to obtain the value for the field capacity by layer. Field capacities derived from the

infilled hydrologic soil groups were also marked as infilled in the database.

7.4.3.3 Assumptions and Uncertainty for Field Capacity. Field capacity is specified

as a national median value based on site-specific hydrologic soil groups. Any uncertainty

associated with the hydrologic soil group would be translated into the associated field capacity.

7.4.4 Wilting Point

Wilting point was obtained similarly to field capacity using a Carsel et al. (1988) lookup

table of wilting point by hydrologic soil group and layer. Median values were used because

wilting point is correlated with saturated water content, yet Carsel et al. (1988) do not provide

correlation parameters.

7.4.4.1 Database Compilation for Wilting Point. A table for wilting point from Carsel

et al. (1988) was entered into the database as a lookup table, including hydrologic soil group,

layer, distribution type (constant), and median value (Table 7-8).

7.4.4.2 Database Processing for Wilting Point. The map unit with the largest area

(that did not have a texture of water) in the area concerned was used to obtain the hydrologic soil

group that was then used with the lookup table to obtain the value for the wilting point by layer. 

Wilting points derived from the infilled hydrologic soil groups were also marked as infilled in

the database.
Section 7.0 Soil Data

7-18

Table 7-7. Field Capacity Values

Hydrologic Group Layer Median

A 1 9.4

A 2 8.1

A 3 5.9

A 4 5.8

B 1 19.1

B 2 18.8

B 3 18.7

B 4 17.5

C 1 22.5

C 2 23.2

C 3 22.9

C 4 21.3

D 1 24.2

D 2 26.3

D 3 25.6

D 4 24.4

Source: Carsel et al. (1988).

Table 7-8. Wilting Point Values

Hydrologic Group Layer Median

A 1 3.1

A 2 2.3

A 3 2.1

A 4 1.9

B 1 8.7

B 2 9.3

B 3 8.9

B 4 8.4

C 1 10.4

C 2 12.1

C 3 11.9

C 4 11.5

D 1 13.8

D 2 17.0

D 3 16.3

D 4 15.1

Source: Carsel et al. (1988).
Section 7.0 Soil Data

7-19

7.4.4.3 Assumptions and Uncertainty for Wilting Point. Wilting point is specified as

a national median value based on site-specific hydrologic soil groups. Any uncertainty

associated with the hydrologic soil group would be translated into the associated wilting point.

7.4.5 SCS Curve Number

The SCS curve number was obtained using a lookup table based on a USDA (1986) table

of curve numbers by cover type and hydrologic soil group. The curve number was passed to the

LAU and Waste Pile Modules by local watershed and subarea. The curve number was also

passed for the Watershed Module by watershed subbasin.

7.4.5.1 Database Compilation for SCS Curve Number. Using the cover type

descriptions from the USDA (1986) table for curve numbers, a comparison was made to the

Anderson land use descriptions (Table 7-3) to match a cover type to each Anderson land use

code. The resulting table (Table 7-9) consists of SCS curve numbers by Anderson land use code

and hydrologic soil group.

7.4.5.2 Database Processing for SCS Curve Number. Using the soil map unit with the

largest area in the watershed subbasin, the hydrologic soil group was obtained as described in

Section 7.3.2. A GIS table of Anderson land use codes by watershed subbasin was used along

with the hydrologic soil group for the map unit to obtain the corresponding SCS curve number 

from Table 7-9. Because there could be multiple land use codes per watershed subbasin, a land

use area-weighted average of the curve number was calculated, resulting in one SCS curve

number for each watershed subbasin. A GIS table matching the site and WMU type to the

regional watershed in which the local watershed resides was used to apply the regional

watershed SCS curve number to the local watershed. The result was indexed by local watershed

and subarea, but because soil and land use data were not available on the subarea scale, the same

SCS curve number was passed for all of the subareas in a given local watershed.

7.4.5.3 Assumptions and Uncertainty for SCS Curve Number. As mentioned

previously, the map unit and land use data were only available on the watershed subbasin scale,

resulting in the same curve number being used for the local watershed as the watershed subbasin

in which it lies. The same curve number was repeated for each of the subareas in each local

watershed. Also, any uncertainties associated with the hydrologic soil group would be translated

into the associated curve number.

7.4.6 Root Zone Depth

The depth to the root zone was obtained using a Dunne and Leopold (1978) table of

rooting depth by vegetation type and soil texture. Like SCS curve numbers this parameter was

indexed by regional watershed subbasin for the watershed model and by local watershed and

subarea for the LAU and Waste Pile Modules. 

7.4.6.1 Database Compilation for Root Zone Depth. Anderson land use descriptions

were used to match a vegetation type from Dunne and Leopold (1978) to an Anderson land use

code. Also, because Dunne and Leopold included only five soil textures and there are 13 basic
Section 7.0 Soil Data

7-20

Table 7-9. SCS Curve Number Values

Anderson Code (GIRAS Land Use) Assumed Cover Type (USDA, 1986)

SCS Curve

Numbera

ABCD

11- residential residential (averaged over different lot sizes) 58 73 82 86

12 - commercial and services commercial and business 89 92 94 95

13, 15 - industrial/commercial services industrial 81 88 91 93

14 -transportation, communication, utilities paved roads, open ditches (with right of

way)

83 89 92 93

16 -mixed urban or builtup land commercial and business, industrial,

residential – one-fourth acre or less

(average)

80 87 91 93

17 - other urban or builtup land urban open space (fair) 49 69 79 84

21 - cropland and pasture mean cropland and pasture – fair (average) 57 72 80 85

22 - orchards, groves, vineyards, nurseries,

and ornamental horticultural land

woods – grass combination (fair) 43 65 76 82

23, 24 - confined feeding operations/

other agricultural land

farmsteads 59 74 82 86

31 - herbaceous rangeland herbaceous and pasture/grassland/range

(average)

49 70 80 87

32 - shrub and brush rangeland oak-aspen, desert shrub, sagebrush, brush –

fair (average)

45 57 68 74

33 - mixed rangeland 31, 32 (average) 47 64 74 81

41, 42, 43 - deciduous/evergreen/

mixed forestland

woods (fair) 36 60 73 79

71, 72, 73, 76 - barren land bare ground/newly graded areas 77 86 91 94

74 - bare exposed rock paved parking lots/bare rock 98 98 98 98

75 - strip mines, quarries, gravel pits gravel roads 76 85 89 91

a

 Extracted or calculated from USDA (1986) using assumed cover type. A, B, C, D are hydrologic soil groups.

CONUS textures, the five textures were mapped across the CONUS textures. Table 7-10 shows

the crosswalk between Dunne and Leopold vegetation types and soil textures and the Anderson

land use codes and CONUS soil textures collected for the representative national data set.

7.4.6.2 Database Processing for Root Zone Depth. The data processing program for

root zone depth was similar to that used for SCS curve numbers except that soil texture was used

in place of the hydrologic soil group. Using the map unit with the maximum area in each
Section 7.0 Soil Data

7-21

Table 7-10. Depth to Root Zone Values

Anderson Code (GIRAS)

Vegetation

(Dunne & Leopold, 1978)

Depth to Root Zone a

Fine

Sand

(S)

Fine

Sandy

Loam

(LS, SL)

Silt Loam

(L, OM, SI,

SIL)

Clay Loam

(CL, SCL,

SICL)

Clay

(C, SC,

SIC)

11, 12, 13, 14, 15, 16, 17, 22 orchards 1.5 1.67 1.5 1 0.67

21, 24 moderately deep-rooted crops 0.75 1 1 0.8 0.5

23 shallow-rooted crops 0.5 0.5 0.62 0.4 0.25

31, 32, 33, 81, 82, 84, 85 deep-rooted crops 1 1 1.25 1 0.67

41, 42, 43, 61 mature forest 2.5 2 2 1.6 1.17

71, 72, 73, 74, 75, 76 none - no vegetation 0 0 0 0 0

a

 Extracted from Dunne and Leopold (1978); assignment to HWIR soil textures shown in parentheses.

watershed subbasin, the soil texture for the top 20 cm was used with a GIS table of Anderson

land use codes by watershed to obtain the corresponding rooting depth from the lookup table

shown in Table 7-10. Because there are multiple land uses per watershed, an area-weighted

average root zone depth was calculated across all land uses for each watershed. The root zone

depth by local watershed was obtained by taking the same value for the local watershed as the

regional watershed in which it resides. This value was repeated for all subareas in each local

watershed.

7.4.6.3 Assumptions and Uncertainty for Root Zone Depth. Similar to the SCS curve

number, the map unit and land use data were only available on the watershed subbasin scale,

resulting in the same rooting depth being used for the local watershed as the regional watershed

in which it lies. Also, this same value was repeated for each of the subareas in each local

watershed. Any uncertainties associated with the soil texture will be somewhat inherent in the

root zone depth.

7.4.7 Alpha, Beta, Residual Water Content, and Saturated Hydraulic Conductivity

Distributions for the unsaturated zone hydrological parameters alpha, beta, residual water

content (WCR), and saturated hydraulic conductivity (SatK) were taken from national

distributions developed by Carsel and Parrish (1988) for different USDA soil textures. 

Distributions for all four variables were collected for the vadose zone model based on the

predominant soil texture for the entire soil column under the WMU (see Section 7.4.1). A crosscorrelation table provided by Carsel and Parrish (1988, Table 9) for these four variables also was

included in the database. SatK also was provided for the LAU, Waste Pile and Watershed

Modules, but it was based on the predominant soil texture for the top 20 cm of soil.
Section 7.0 Soil Data

7-22

SatK was provided for the landfill cover soil based on the predominant soil texture for

the entire soil column under the WMU. This value is based on the assumption that the soil used

for covering the landfill is obtained from soil at or very nearby the facility and, in many cases,

could be soil excavated to construct the landfill itself.

7.4.7.1 Database Compilation for Alpha, Beta, WCR, and SatK. The table from

Carsel and Parrish (1988) was hand-entered into a database table, including soil texture, lower

and upper limits, transformation type, transformed mean, and transformed standard deviation

(Table 7-11). The lower limit for SatK was raised from 0 to 1x10-8 m/yr to accommodate the

models that cannot handle a 0 value for this variable.

7.4.7.2 Database Processing for Alpha, Beta, WCR, and SatK. The map unit with the

maximum area in the area of concern (WMU or watershed subbasin) was used to determine the

predominant soil texture for either the top 20 cm of soil or the entire soil column. This texture

was then used to query the distribution statistics (type, mean, standard deviation, minimum, and

maximum) for each variable needed from the lookup table in the database. For a small number

of map units, the predominant texture was organic materials. Because Carsel and Parrish (1988)

do not provide distributions for organic materials, in these cases distribution data corresponding

to the most commonly occurring texture in the United States, loam, were entered into the

database as the default, with the infill flag set to true.

7.4.7.3 Assumptions and Uncertainty for Alpha, Beta, WCR, and SatK. Along with

any uncertainty associated with the soil texture (see Section 7.4.1) and its effect on these

properties, four sites had organic materials as the predominant texture for a few watersheds.

Although a nationally typical soil type (loam) was selected in these cases, it is uncertain whether

this selection presents a bias because typical hydrologic properties for organic soils were not

available for this analysis. Because the number of these cases is sufficiently small, there was

probably little impact on the overall analysis.

7.4.8 Saturated Water Content and Dry Bulk Density

The values for saturated water content (WCS; total porosity) were taken from a Carsel

and Parrish (1988) table of mean WCS by soil texture. WCS values for the vadose zone model

were taken from the soil texture for the entire soil column under the WMU. Values for the LAU,

Waste Pile, and Watershed Modules were taken from the soil texture for the top 20 cm of soil,

and similar to the saturated hydraulic conductivity (see Section 7.4.7), the landfill cover soil

WCS was based on the soil texture for the entire soil column under the WMU. 

Dry soil bulk density (RHOB) was calculated from the WCS using an equation from

U.S. EPA (1996), and it was provided for the Vadose Zone Module based on the soil texture for

the entire soil column.

7.4.8.1 Database Compilation for WCS and RHOB. WCS values from Carsel and

Parrish (1988) were hand-entered into a database table, including soil texture and mean WCS

(Table 7-12). RHOB was then calculated from WCS using the following equation (U.S. EPA,

1996):
Section 7.0 Soil Data

7-23

Table 7-11. Unsaturated Zone Hydrologic Parameters:

Moisture Retention Parameters (Alpha, Beta), Saturated Hydraulic Conductivity (SatK),

and Residual Water Content (WCR)

Variable

Soil

Texture Min Max Transformation

Transformed

Mean

Transformed

Std.

Deviation

Alpha C 0 0.15 TrnJohnsonSB -4.145 1.293

Alpha CL 0 0.15 TrnLogNormal -4.22 0.72

Alpha L 0 0.15 TrnJohnsonSB -1.27 0.786

Alpha LS 0 0.25 Normal 0.124 0.043

Alpha S 0 0.25 TrnJohnsonSB 0.378 0.439

Alpha SC 0 0.15 TrnLogNormal -3.77 0.563

Alpha SCL 0 0.25 TrnJohnsonSB -1.38 0.823

Alpha SI 0 0.1 Normal 0.017 0.006

Alpha SIC 0 0.15 TrnLogNormal -5.66 0.584

Alpha SICL 0 0.15 TrnJohnsonSB -2.75 0.605

Alpha SIL 0 0.15 TrnLogNormal -4.1 0.555

Alpha SL 0 0.25 TrnJohnsonSB -0.937 0.764

Beta C 0.9 1.4 TrnLogNormal 0.0002 0.118

Beta CL 1 1.6 TrnJohnsonSB 0.132 0.725

Beta L 1 2 TrnJohnsonSU 0.532 0.099

Beta LS 1.35 5 TrnJohnsonSB -1.11 0.307

Beta S 1.5 4 TrnLogNormal 0.978 0.1

Beta SC 1 1.5 TrnLogNormal 0.202 0.078

Beta SCL 1 2 TrnLogNormal 0.388 0.086

Beta SI 1.2 1.6 Normal 1.38 0.037

Beta SIC 1 1.4 TrnJohnsonSB -1.28 0.821

Beta SICL 1 1.5 Normal 1.23 0.061

Beta SIL 1 2 TrnJohnsonSB -0.37 0.526

Beta SL 1.35 3 TrnLogNormal 0.634 0.082

SatK C 1E-08 5 TrnJohnsonSB -5.75 2.33

(continued)
Table 7-11. (continued)

Section 7.0 Soil Data

Variable

Soil

Texture Min Max Transformation

Transformed

Mean

Transformed

Std.

Deviation

7-24

SatK CL 1E-08 7.5 TrnJohnsonSB -5.87 2.92

SatK L 1E-08 15 TrnJohnsonSB -3.71 1.78

SatK LS 1E-08 51 TrnJohnsonSB -1.27 1.4

SatK S 1E-08 70 TrnJohnsonSB -0.394 1.15

SatK SC 1E-08 1.5 TrnLogNormal -4.04 2.02

SatK SCL 1E-08 20 TrnJohnsonSB -4.04 1.85

SatK SI 1E-08 2 TrnLogNormal -2.2 0.7

SatK SIC 1E-08 1 TrnLogNormal -5.69 1.31

SatK SICL 1E-08 3.5 TrnJohnsonSB -5.31 1.62

SatK SIL 1E-08 15 TrnLogNormal -2.19 1.49

SatK SL 1E-08 30 TrnJohnsonSB -2.49 1.53

WCR C 0 0.15 TrnJohnsonSU 0.445 0.282

WCR CL 0 0.13 TrnJohnsonSU 0.679 0.06

WCR L 0 0.12 TrnJohnsonSB 0.639 0.487

WCR LS 0 0.11 TrnJohnsonSB 0.075 0.567

WCR S 0 0.1 TrnLogNormal -3.12 0.224

WCR SC 0 0.12 TrnJohnsonSB 1.72 0.7

WCR SCL 0 0.12 TrnJohnsonSB 1.65 0.439

WCR SI 0 0.09 Normal 0.042 0.015

WCR SIC 0 0.14 Normal 0.07 0.023

WCR SICL 0 0.115 Normal 0.088 0.009

WCR SIL 0 0.11 TrnJohnsonSB 0.478 0.582

WCR SL 0 0.11 TrnJohnsonSB 0.384 0.7

Source: Carsel and Parrish (1988).
Section 7.0 Soil Data

7-25

(7-1)

Table 7-12. Saturated Water Content and Dry Bulk Density

Soil

Texture

Saturated Water

Content

(WCS)

Dry Bulk Density

(RHOB)

C 0.38 1.643

CL 0.41 1.5635

L 0.43 1.5105

LS 0.41 1.5635

SI 0.46 1.431

SIL 0.45 1.4575

SIC 0.36 1.696

SICL 0.43 1.5105

S 0.43 1.5105

SC 0.38 1.643

SCL 0.39 1.6165

SL 0.41 1.5635

Sources: Carsel and Parrish (1988), U.S. EPA (1996).

where

2.65 = soil particle density in g/cm3

.

RHOB values were then added to the table of mean WCS by soil texture.

7.4.8.2 Database Processing for WCS and RHOB. The map unit with the maximum

area in the area of concern (WMU or watershed subbasin) was used to determine the

predominant soil texture for either the top 20 cm of soil or the entire soil column. This texture

was then used to query the corresponding values for WCS and bulk density from the lookup

tables in the database. Because a small number of map units have organic materials as the

predominant texture and Carsel and Parrish (1988) does not provide a value for this texture, infill

values corresponding to the most commonly occurring texture in the United States, loam, were

entered as the default, with the infill flag set to true. 
Section 7.0 Soil Data

7-26

7.4.8.3 Assumptions and Uncertainty for WCS and RHOB. Like any uncertainties

associated with the soil texture, a small number of map units with organic materials as the

predominant texture affected some watershed values at four of the sites. Although a nationally

typical soil type (loam) was selected in these cases, it is uncertain whether this selection presents

a bias because typical hydrologic properties for organic soils were not available for this analysis. 

Because the number of these cases is sufficiently small, there was probably little impact on the

overall analysis.

7.4.9 Soil Moisture Coefficient b

Mean values for soil moisture coefficient b were obtained from a Clapp and Hornberger

(1978) table of b values by USDA soil texture class (Table 7-13). Soil moisture coefficient b

values were collected for the landfill model, cover soil and subsoil, using the soil texture for the

entire soil column under the WMU. The b values for the LAU, Waste Pile, and Watershed

Modules were collected using the soil texture for the top 20 cm of soil.

7.4.9.1 Database Compilation for Soil Moisture Coefficient b. The Clapp and

Hornberger (1978) table for soil moisture coefficient b was hand-entered into a table in the

database to create a lookup table for b values based on soil texture.

7.4.9.2 Database Processing for Soil Moisture Coefficient b. The map unit with the

maximum area in the area of concern (WMU or watershed subbasin) was used to determine the

predominant soil texture that was, in turn, used to query the corresponding b value from the

lookup table in the database. Note that in Table 7-13 there is no b value for silt or organic

materials, so an infill value corresponding to the predominant soil texture in the United States,

loam, was used and designated as infilled in the data. Silt was never a predominant soil texture

for a watershed or WMU, however, so this infill was not exercised for the representative national

data set data collection effort.

7.4.9.3 Assumptions and Uncertainty for Soil Moisture Coefficient b. Any

uncertainty for soil texture (see Section 7.4.1) would also be translated into the soil moisture

coefficient.

7.4.10 Fraction Organic Carbon and Percent Organic Matter

The percent organic matter (POM), available in STATSGO and USSOILS, was

converted to fraction organic carbon (FOC) using an equation from U.S. EPA (1996). Like other

parameters, fraction organic carbon was collected for both the top 20 cm of soil and the entire

soil column. Unlike the other parameters, two different sources were used based on the depth

required. Because USSOILS had already compiled the percent organic matter for the entire soil

column, USSOILS was used as the source for the fraction organic carbon for the entire soil

column as well as the percent organic matter required by the vadose zone model. Because a

value was needed for the top 20 cm and the organic matter content is extremely sensitive to

depth in the soil column, the percent organic matter data from STATSGO, by component and

layer, were used to calculate values for the top 20 cm of soil.
Section 7.0 Soil Data

7-27

(7-2)

Table 7-13. Soil Moisture Coefficient b Values

Soil Texture Soil Moisture Coefficient b Values

sand 4.05

loamy sand 4.38

sandy loam 4.90

silt loam 5.30

loam 5.39

sandy clay loam 7.12

silty clay loam 7.75

clay loam 8.52

sandy clay 10.4

silty clay 10.4

clay 11.4

Source: Clapp and Hornberger (1978).

7.4.10.1 Database Compilation for Fraction Organic Carbon and Percent Organic

Matter. The USSOILS data table containing the percent organic matter for the entire soil

column needed no further processing. The percent organic matter (oml and omh) contained in

STATSGO by layer and component needed to be compiled to obtain the percent organic matter

for the top 20 cm of soil. As shown in Equation 7-2, a depth-weighted average of average (oml

and omh) percent organic matter by STATSGO layer within the standard CONUS layers was

taken and then averaged (area-weighted) further across all components to obtain the percent

organic matter by map unit and standard CONUS layer as follows:

where

i = STATSGO layer

j = STATSGO component

k = CONUS layer

AveOM = average percent organic matter by STATSGO layer and component

h = height of STATSGO layer in CONUS layer

compact = percentage of component in map unit.
Section 7.0 Soil Data

7-28

(7-3)

(7-4)

A simple depth-weighted average over the top three CONUS layers (20 cm) was then

taken to obtain the average percent organic matter for the top 20 cm of soil.

7.4.10.2 Database Processing for Fraction Organic Carbon and Percent Organic

Matter. The percent organic matter was obtained directly from USSOILS using the map unit

with the largest area in the WMU area. The map unit with the largest area in the area of concern

(WMU or watershed subbasin) also was used to obtain the fraction organic carbon. Both the

fraction organic carbon for the entire soil column and for the top 20 cm of soil were calculated

from the percent organic matter using the following equation (U.S. EPA, 1996):

where

foc = fraction organic carbon

%OM = percent organic matter

174 = conversion factor.

The value for the entire soil column (WMU) was calculated from the percent organic

matter from USSOILS, and the value for the top 20 cm was calculated from the percent organic

matter derived from STATSGO data.

7.4.11 Soil pH

The soil pH was derived from STATSGO pH data by layer (phl and phh) and determined

for the entire soil column.

7.4.11.1 Database Compilation for pH. The soil pH for the entire soil column by map

unit was calculated as the depth- and area-weighted averages of the average pH by layer and

component as follows:

where

i = STATSGO layer

j = STATSGO component

laydepth = depth of the STATSGO layer (high and low).

7.4.11.2 Database Processing for pH. Using the map unit with the largest area in the

area concerned (WMU or watershed subbasin), the pH value was obtained from the table of pH
Section 7.0 Soil Data

7-29

(7-5)

by map unit discussed in the previous section. For the farm and watershed pH, the value was

further averaged across all watershed subbasins at the site.

7.4.12 Silt Content

Silt content was obtained from CONUS layer data for the percentage of silt and was

obtained for the top 20 cm of soil only. CONUS was taken as the most reliable source for this

parameter because CONUS values are based directly on the CONUS/STATSGO soil texture and

the USDA soil textural triangle. According to Miller and White (1998), the percentages of sand,

silt, and clay content values are suspect because they often do not add up to 100 percent.

7.4.12.1 Database Compilation for Silt Content. Silt content by map unit for the top

20 cm of soil was obtained using the following equation :

where

i = CONUS layer (top three only)

layerdepth = depth of CONUS layer.

CONUS layers with textures of other, no data, and bedrock were excluded from the averaging.

7.4.12.2 Database Processing for Silt Content. The map unit with the largest area in

the WMU was used to obtain the percentage of silt from the table discussed in the previous

section.

7.4.13 USLE Erodibility Factor (K)

The USLE erodibility factor (K) was obtained directly from USSOILS, which provides

depth- and area-weighted averages by map unit. The map unit with the largest area in the

watershed subbasin was used to obtain the K value from the USSOILS data table.

7.4.14 USLE Cover Factor (C)

The USLE cover factor (C) was obtained using a Wanielista and Yousef (1993) table of

C factors by land use. Values for C were collected by watershed subbasin for the Watershed

Module and by subarea for the LAU and Waste Pile Modules. 

7.4.14.1 Database Compilation for USLE Cover Factor (C). Land use descriptions in

the Wanielista and Yousef (1993) table were matched to Anderson land use codes to provide a

database lookup table of C values by Anderson land use code (Table 7-14).

7.4.14.2 Database Processing for USLE Cover Factor (C). A GIS table of Anderson

land use codes by watershed was used to obtain the corresponding C values from the database
Section 7.0 Soil Data

7-30

lookup table. A land use area-weighted average C value was calculated by watershed. As with

the soil properties, the C value for the regional watershed was used for all subareas of the local

watershed.

7.4.15 USLE Erosion Control Factor (P)

Similar to the USLE cover factor (C), the USLE erosion control practice factor (P) was

obtained using a Wanielista and Yousef (1993) table of P values by land use. Values for P were

indexed by watershed subbasin for the Watershed Module and by local watershed subarea for the

LAU and Waste Pile Modules. 

7.4.15.1 Database Compilation for USLE Erosion Control Factor (P). Land use

descriptions from the Wanielista and Yousef (1993) table of P values also were matched to

Anderson land use codes to create a lookup table of P values by Anderson land use code

(Table 7-15).

Table 7-14. USLE Cover Factor C

Land Use Anderson Codea C

Crop land 21, 24 0.08

Forest land 41, 42, 43 0.005

Pasture land 22, 31, 32, 33, 81, 82, 84, 85 0.01

Urban land 11, 12, 13, 14, 15, 16, 17 0.01

No cover 23, 71, 72, 73, 75, 76, 83 1

No erosion 74, 91, 92 0

Water 51, 52, 53, 54, 61, 62 0

a

 See Table 7-3 for a description of Anderson land use codes. 

Source: Wanielista and Yousef (1993). 

Table 7-15. USLE Erosion Control Factor

Land Use Anderson Codea P

Cropland 21 0.5

Forest land 41, 42, 43 1

Pasture land 22, 23, 24, 31, 32, 33, 81, 82 1

Urban land 11, 12, 13, 14, 15, 16, 17 1

No erosion control 51, 52, 53, 54, 61, 62, 71, 72, 73, 74, 75, 76, 83, 84, 85, 91, 92 1

a

 See Table 7-3 for a description of Anderson land use codes.

Source: Wanielista and Yousef (1993). 
Section 7.0 Soil Data

7-31

7.4.15.2 Database Processing for USLE Erosion Control Factor (P). The processing

for the P value was the same as that for the USLE cover factor. A GIS table of Anderson land

use codes by watershed was used to obtain the P value from the database table. A land use areaweighted average was taken to calculate an average P value by watershed. The P value for the

regional watershed in which the local watershed lies was used as the P value by the local

watershed subarea.

7.4.16 Soil Column Temperature

The soil column temperature for the entire site was assumed to be the same as the ground

water temperature and was extracted from a Collins (1925) geographical distribution of ground

water temperatures for the continental United States.

7.5 Quality Assurance and Quality Control

7.5.1 Quality Control of Data Compilation

Two main types of QC were performed during database compilation: 100 percent checks

of manual data entry and manual checks of automated calculations done on processed data. 

Manual data entry was checked by comparing the original paper copy of the table (in the original

reference) to the entries in the database. Automated data transfer from the database tables into

the final deliverable database also was checked against the original data sources as a QC check

of both data processing and database values. To check data compilation from STATSGO and

CONUS tables within the soil database, all data processing operations were checked using hand

calculations for soil map units randomly selected from the database. When special processing

rules applied to certain data categories, at least one of each type was chosen and checked.

7.5.2 Quality Control of Data Transfer

To ensure that data were correctly obtained from the database tables and transferred to

the final deliverable, soil data for a few sites were hand-checked to ensure that they

corresponded to the correct map unit, land use code, and soil type or hydrologic soil group. Any

averaging was checked by hand calculations as well. All of the final system-ready data were

checked automatically using database queries to ensure that the variable name, data group,

dimensions, data type, and units agreed with those in the model specifications and that the

minimum and maximum limits in the model specifications had not been violated. The soil

variable indices also were checked automatically to ensure that for each site the largest index

was equal to the correct number of watersheds, local watersheds, or local watershed subareas on

which the variable was indexed.

7.5.3 Quality Assurance

Quality assurance was conducted to ensure that an adequate QC methodology was in

place and correctly implemented and recorded. QA/QC records for soil data processing can be

provided on request.
Section 7.0 Soil Data

7-32

7.6 Assumptions and Uncertainties

In general, the site-specific soil data readily available from STATSGO and its associated

databases are more than adequate for a national screening analysis. Although soil properties do

vary significantly on a much smaller spatial scale than the nationwide soil data in STATSGO,

the 3MRA modeling system, which assumes average soil properties across a watershed or

vadose zone, could not take advantage of such variability if the data were available. Given the

demonstrated quality of the soil data in STATSGO, the QA/QC measures designed to ensure

effective data processing and transfer, and the national scale of the 3MRA modeling system, we

do not believe soil data are a significant source of overall uncertainty. The uncertainties

associated with the soil data, however, are summarized here.

One of the assumptions associated with the soil data was that soil properties are

homogeneous over an area represented by a map unit. Based on STATSGO layer and

component data, many of the soil properties vary on a much smaller scale than the map unit

level. The representation of this complexity was not possible, however, nor could it be taken

advantage of by the models, so the predominant or average values were used to characterize the

soil for the representative national data set.

A few more minor uncertainties are associated with the soil data. First, in the few cases

when data were missing, an infill value, representing the most common value nationally, was

substituted. Infill values for soil properties were required for 25 sites. In most cases, infill

values were only necessary for a couple of the watersheds at each site and there were no sites

completely missing soil data. Infill values resulted from two problems: missing hydrologic soil

groups (see Section 7.4.2) and a soil type of organic materials (four sites, see Section 7.4.7). 

A second uncertainty is the scale at which data were available for the local watershed. 

Because the scale of the STATSGO data (1:250,000) was much larger than that required to

delineate the local watersheds, the soil properties for the local watershed and subareas were

assumed to be the same as the regional watershed subbasin.

Two uncertainties associated with the land use data are lack of data currency and the

scale of the data. The GIRAS land use coverages are from the mid-1970s to the early 1980s and

may not accurately reflect current conditions at all sites; however they are roughly

contemporaneous with the 1985 Industrial D Screening Survey used to define the sites used in

this analysis. The 1:250,000-scale GIRAS land use coverages also limit the spatial resolution of

the land use data. However, GIRAS data are the same scale as the digital elevation model (DEM)

and STATSGO coverages. Thus, although some resolution may be lost, the level-of-detail is

fairly consistent across these data sources.

7.7 References

Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A land use and land cover

classification system for use with remote sensor data. Geological Survey Professional

Paper 964. In: U.S. Geological Survey Circular 671. U.S. Geological Survey,

Washington, DC. Website at http://wwwnmb.usgs.gov/pub/ti/LULC/lulcpp964/lulcpp964.txt.
Section 7.0 Soil Data

7-33 7-33

Carsel, R. F., R. S. Parrish, R. L. Jones, J. L. Hansen, and R. L. Lamb. 1988. Characterizing the

uncertainty of pesticide leaching in agricultural soils. Journal of Contaminant Hydrology,

2:111-124.

Carsel, R. F., and R. S. Parrish. 1988. Developing joint probability distributions of soil water

retention characteristics. Water Resources Research, 24(5):755-769. May.

Clapp, R. B., and G. M. Hornberger. 1978. Empirical equations for some soil hydraulic

properties. Water Resources Research, 14:601-604.

Collins, W. D. 1925. Temperature of Water Available for Industrial Use in the United States.

Contributions to Hydrology of United States, 1923-1924. Water Supply Paper 520-F.

U.S. Geological Survey, Washington, D.C. pp. 97-104.

Dunne, Thomas, and Luna B. Leopold. 1978. Water in Environmental Planning. W. H.

Freeman and Company, New York.

Miller, D. A., and R. A. White. 1998. A Conterminous United States Multilayer Soil

Characteristics Dataset for Regional Climate and Hydrology Modeling. Website at

http://www.essc.psu.edu/soil_info/index.cgi?soil_data&index.html.

Schwarz, G. E. and R. B. Alexander. 1995. State Soil Geographic (STATSGO) Data Base for

the Conterminous United States. Edition 1.1. Open-File Report 95-449. U.S. Geological

Survey, Reston, VA. Website at

http://water.usgs.gov/GIS/metadata/usgswrd/ussoils.html. September 1.

USDA (Department of Agriculture). 1986. Urban Hydrology for Small Watersheds. TR-55

(210-VI-TR-55), second edition. U.S. Department of Agriculture, Engineering Division,

Soil Conservation Service, Washington, DC. pp. 2-5. June.

USDA (Department of Agriculture). 1994a. State Soil Geographic (STATSGO) Data Base. 

Natural Resources Conservation Service, Fort Worth, TX. Available online:

http://www.ftw.nrcs.usda.gov/stat_data.html.

USDA (Department of Agriculture). 1994b. State Soil Geographic (STATSGO) Data Base.

Data Use Information. Miscellaneous Publication Number 1492. National Cartography

and GIS Center, Fort Worth, TX. December.

U.S. EPA (Environmental Protection Agency). 1994. 1:250,000 Scale Quadrangles of

Landuse/Landcover GIRAS Spatial Data in the Conterminous United States: Metadata.

Office of Information Resources Management, Washington, DC. Website at

http://www.epa.gov/ngispgm3/nsdi/projects/giras.htm.

U.S. EPA (Environmental Protection Agency). 1996. EPA's Composite Model for Leachate

Migration with Transformation Products; EPACMTP. Background Document. U.S.

Environmental Protection Agency, Office of Solid Waste, Washington, DC. September.
Section 7.0 Soil Data

7-34

USGS (Geological Survey). 1990. Land Use and Land Cover Digital Data from 1:250,000-

and 1:100,000-Scale Maps. Data Users Guide 4. U.S. Geological Survey, Reston, VA.

Wanielista, M. P., and Y. A. Yousef. 1993. Stormwater Management. John Wiley & Sons, Inc.,

New York, NY. pp. 399-410.