WATER RESOURCES RESEARCH, VOL. 38, NO. 5, 1047, 10.1029/2000WR000076, 2002

Soil property database:
Southern Great Plains 1997 Hydrology Experiment
B. P. Mohanty1 and P. J. Shouse
George E. Brown Salinity Laboratory, Riverside, California, USA

D. A. Miller
EMS Environment Institute, Pennsylvania State University, University Park, Pennsylvania, USA

M. T. van Genuchten
George E. Brown Salinity Laboratory, Riverside, California, USA
Received 6 November 2000; revised 4 October 2001; accepted 4 October 2001; published 7 May 2002.

[1] Many models used in land surface hydrology, vadose zone hydrology, and hydroclimatology
require an accurate representation of soil properties. Unfortunately, existing soil property databases
are limited in terms of reliability, precision, and their usefulness in evolving soil-vegetationatmosphere-transfer (SVAT) schemes of general circulation models (GCMs) or regional-scalehydrologic models. Furthermore, not many site-specific, comprehensive soil property measurement
campaigns have been carried out concurrently with large-scale remote sensing hydrologic
campaigns. To better understand the complex and interdependent geophysical processes in the nearsurface, we conducted an extensive soil property measurement campaign during the Southern Great
Plains 1997 (SGP97) Hydrology Experiment. We measured soil physical, hydraulic, and thermal
properties across the SGP97 study region. The resulting soil property database not only is useful forevaluating the SVAT schemes in GCMs and other hydrologic models but also can be used as a basis
for transfer function modeling, extrapolating point estimates of soil properties to larger spatial
scales, testing point and nonpoint source pollution modeling, and evaluating evolving hypotheses inwater and energy transfer across the land-atmosphere boundary. The complete data report [Shouse
et al., 2002] and raw data are available upon request from the George E. Brown Salinity Laboratory.
Summarized data are given by Mohanty et al. [1999].
INDEX TERMS: 1875 Hydrology:
Unsaturated zone; 1878 Hydrology: Water/energy interactions; 1833 Hydrology:
Hydroclimatology; 1836 Hydrology: Hydrologic budget (1655); KEYWORDS: SGP97, soil
properties, database, Oklahoma hydrology

1. Introduction[2] Hydrologists have recognized the critical role of soil moisture and tried to develop models that extend the point-scale orlocal-scale physics of soil hydrology to the larger domains of
mesoscale meteorological and global circulation models (GCMs).
These models require pixel-scale information about the physical,
hydraulic, and thermal characteristics of the soil in order toproperly simulate soil hydrologic processes as part of the combinedenergy and water balances between the land surface and theatmosphere. Estimating pixel-scale soil properties is complicatedbecause of the overwhelming heterogeneity of both the soil surface
and the subsurface and the highly nonlinear nature of local-scalewater and heat transport processes. A related and still largely
unresolved problem is the measurement or estimation of thesubsurface unsaturated hydraulic functions (the constitutive functions relating soil water content, soil water pressure head, andunsaturated hydraulic conductivity) and the soil thermal properties(heat capacity, heat diffusivity, and thermal conductivity) at different spatial scales. Although the importance of these soil properties

1
Now at Department of Biological and Agricultural Engineering, Texas
A&M University, College Station, Texas, USA.

Copyright 2002 by the American Geophysical Union.
0043-1397/02/2000WR000076$09.00

has always been recognized, earlier large-scale hydrologic campaigns have not measured soil properties as rigorously as the otherhydroclimatic driving factors such as precipitation, topography,
and vegetation. Because of the lack of measured soil properties,
researchers regularly approximate them in different ways and thenassociate any errors or uncertainties in model predictions with theirsoil property estimates.
[3] Soil heterogeneity affects the distribution of soil moisturethrough variations in texture, organic matter content, porosity,
structure, and macroporosity. All of these soil properties affectthe fluid transmission and retention functions. The variability insoil hydraulic conductivity and soil water retention characteristics
greatly influences the vertical and lateral transmission properties.
Significant soil moisture variations may exist over very small
distances because of variations in soil particle and pore sizes.
Additionally, soil color influences its albedo and thus the rate ofevaporative drying. In a watershed-scale study in Chicksha, Oklahoma, Hawley et al. [1983] found significant differences in surface
soil moisture due to differences in soil texture and antecedentmoisture. Brutsaert [1982] recognized two stages of soil profile
drying: (1) an atmosphere-controlled stage followed by (2) a soilcontrolled stage. In the first-stage the moist soil profile can fully
supply all the water demanded by the atmosphere. As the soil near
the surface dries out, moisture can no longer be delivered at the rate
demanded by the atmosphere. Instead, the moisture delivery rate islimited by the properties of the soil profile. Brutsaert [1982] notes

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MOHANTY ET AL.: DATA AND ANALYSIS NOTE

that at any one point, the transition from atmosphere to soil control
is rapid, but over the entire catchment the transition will be gradual.
Famiglietti et al. [1992] demonstrated the effects of soil heterogeneity on flux rates using a distributed catchment-scale waterbalance model.
[4] Entekhabi [1994] presented a sensitivity analysis of theinfluence of soil texture on land-atmosphere interaction andpointed out the complex nature of the relationship. More recently,
Kim et al. [1997] showed the impact of soil heterogeneity on thewater budget of the unsaturated zone. They showed that in highly
conductive soils, where evapotranspiration is limited by percolation through a lower boundary, heterogeneity increases the spatially averaged evapotranspiration relative to the uniform soil. Forless conductive soils, decreasing infiltration rates due to soil
heterogeneity cause evapotranspiration to become smaller. Kim
et al. [1997] also concluded that equivalent parameters derived forthe long-term average water budget are not valid for transient
behavior and depend not only on the soil hydraulic parameters andtheir heterogeneity but also on the climate and the spatially uniform
parameters. In a related study, Kim and Stricker [1996] showed thatthe effects of soil spatial heterogeneity on the water budget arestronger for a loamy soil as compared to a sandy soil. Theysuggested that soil heterogeneity has a greater influence in the
loamy soil because most of the variation of the water budget ispresent at the smaller (field) scale. On the other hand, most of thewater budget variation for the sandy soil occurs at larger scales andis temporally correlated to the rainfall field. These findings ledthem to conclude that from the perspective of the annual water
budget a homogenous equivalent soil exists for the sandy soil but
not for the loamy soil. Some other significant studies of therelationship between soil moisture variability and different soil
properties were made by Reynolds [1970], Henninger et al. [1976],
Niemann and Edgell [1993], Crave and Gascuel-Odoux [1997],
and Famiglietti et al. [1998]. Studies [e.g., Famiglietti et al., 1998]
show that soil properties are equally or even more important forcontrolling upward (evapotranspiration) and downward (infiltration) fluxes than topography, vegetation, or precipitation, with therelative importance depending on the antecedent moisture content
and the wetting or drying sequence of the soil. Mohanty and
Skaggs [2001] also discovered the dominance of soil texture oversurface slope and vegetation type in soil moisture time stability/
instability features at selected remote sensing footprints during theSouthern Great Plains 1997 (SGP97) Hydrology Experiment.
[5] SGP97 was sponsored by the National Aeronautic and Space
Administration (NASA) and cosponsored by U.S. Department ofAgriculture Agricultural Research Service (USDA ARS), National
Oceanic and Atmospheric Administration, Department of Energy,
National Science Foundation, and other federal and state agencies.
Building upon the success of its predecessors such as the FirstInternational Satellite Land Surface Climatology Project (ISLSCP)
Field Experiment (FIFE), and the Little Washita 1992 experiments(Washita’92 and Washita’94), the SGP97 Hydrology Experiment
was an interdisciplinary investigation conducted by scientists working in the area of hydrology, soil science, boundary layer meteorology, atmospheric science, and ecology. The experiment covered a
region of approximately 40 km  250 km (10,000 km2) in thesubhumid environment of Oklahoma. Soil moisture data werecollected over a one-month period between 18 June and 17 July
1997 using remote sensing and ground-based techniques. We
conducted an extensive soil property measurement campaign during
the SGP97 hydrology experiment. Our major objectives included
measuring soil physical, hydraulic, and thermal properties acrossthe Southern Great Plains 1997 study region. The resulting databaseserves as a basis for developing pedo-topo-vegetation transfer
function models for extrapolating point estimates of soil properties
to larger scales, testing surface and subsurface hydrologic andcontaminant transport modeling, and evaluating evolving hypotheses in water and energy transfer across the land-atmosphereboundary within the context of regional-scale hydrology and soilvegetation-atmosphere-transfer (SVAT) schemes in GCMs.

2. Sampling Plan
[6] We collected soil cores from different depths at representative (soil, topography, and vegetation) sites based on a priori
information (gleaned from digital maps and overlays available
from Earth System Science Center, Pennsylvania State University,
at http://www.essc.psu.edu/nasa_lsh/) and concurrent site inspection. Although in the database we provided more detailed andunbounded site classifications for future researchers, various combinations among soil texture (12 USDA classes, see Figure15-03 of
Gee and Bauder [1986]), relative position (valley, hillslope, hilltop), and vegetation type (grass, shrub, crop) were used as theprimary groups for our site selection protocol. A total of 157
surface soil cores were collected from 46 quarter sections within
the Little Washita (LW), El Reno (ER), and Central Facility (CF)intensive study areas (Figure 1). In addition to the surface cores,
four or five subsurface soil cores were collected at depths of up to
1 m at selected sites (based on soil morphologic characteristics)
within the LW, ER, and CF areas. Soil cores were analyzed in thelaboratory for soil particle size, bulk density, organic carbon, soil
water retention, dynamic outflow, saturated and unsaturated
hydraulic conductivities, thermal conductivity, heat diffusivity,and heat capacity. Selected soil cores were also used to measuresoil water hysteresis (drying versus wetting) and the effects oftemperature on the hydraulic properties.

3. Site Description and Field Procedures[7] The SGP97 region covers a 40 km  250 km area within thecentral part of the U.S. Great Plains. The climate is classified as
subhumid with a north-south precipitation gradient [Famiglietti
et al., 1999]. The topography of the region is moderately rolling.
Soils include a wide range of textures with large areas of both coarse
and fine textures (State Soil Geographic Database (STATSGO) andSoil Survey Geographic Database (SSURGO), Natural Resources
Conservation Service). Rangeland and pasture with significant
areas of winter wheat and other crops dominate land use. Additional
background information on the Little Washita watershed in thesouthern part of the SGP97 region is given by Allen and Naney[1991] and Jackson and Schiebe [1993]. Other relevant surfacehydrometeorological, vegetation, soil, and topographic information
for the SGP97 region are given by Southern Great Plains 1997
Science Team [1997].
[8] Soil cores (in brass cylinders, 5.3 cm diameter and 5.9 cmlong) at different depths were collected from representative (soil,
slope, and vegetation) sites guided by thematic maps created using
geographic information systems overlays. A pickup-truck-mounted
Giddings drilling rig and a hand core or bulk density sampler wereused to extract soil cores. The Giddings rig was used to extract
profile core samples that were on the order of 100 cm or slightly
longer. These core samples were used to tentatively classify the soil
profile and determine the location of each soil horizon on the basisof field morphology (primarily texture and color). Other featuresthat we looked for in terms of stratifying the soil horizons were
wormholes or animal burrows, root channels, and macroporousMOHANTY ET AL.: DATA AND ANALYSIS NOTE

5-3

Figure 1. Geographical location of Southern Great Plains 1997 Hydrology Experiment in Oklahoma. The light graywindow (40 km  250 km) indicates the SGP97 flight line area for remote sensing measurements of soil moisture.
Little Washita watershed in the south, El Reno in the center, and Central Facility in the north are the SGP97 focus
regions for ground data collection and validation activities. See color version of this figure at back of this issue.
anomalies. We observed and noted the extent and density of the rootsystems. We took soil surface (3 – 9 cm depth) samples from 46 outof a total of 48 quarter sections (800 m  800 m) in the LW, ER, andCF intensive study areas. Using a priori information and field site
confirmation, one or more representative soil cores were collectedfrom each quarter section, with additional subsurface soil cores
retrieved (based on soil stratigraphy) from depths up to 1 m atselected sites. A total of 157 cores were collected. Once the soil had
been classified and the depths of the different soil horizons had been
defined, we sampled the depth increments for subsequent hydraulicproperty determination. For this purpose we excavated the soil
above a particular depth with a large soil probe (6 – 9 cm OD)
attached to the Giddings rig and then took an ‘‘undisturbed’’ soilcore using the bulk density sampler. Each soil core was encapsulated in a 5.3 cm ID by 5.9 cm long brass ring (within the sampler).
The brass rings were customized to fit inside the available Tempe
cell apparatus (to be discussed in section 4). At all but 12 locations
we collected duplicate soil samples (approximately 300 – 500 g foreach sampled depth) in order to measure field soil water content,
soil organic carbon, and particle size distribution in the laboratory.
Soil temperature was measured in the field at different depths using
a thermocouple thermometer (Baranant 100, model 600 – 2820,
Industrial Instruments and Supply, Southampton, Pennsylvania)
with a readout unit. Other pedological information such as soilcolor and structure were determined during the soil core extraction

process. Correspondingly, the local (30 m diameter) topography,
including the east-west and north-south slopes, was measured witha hand level (Abney Level, PECO). General landscape, relative
field position, microtopography, vegetation type, canopy height,
canopy density, rooting depth, current weather, and other sitespecific observations were determined by visual inspection. Eachsampling location was identified with a differential global positioning system (DGPS) having a precision of ±10 m. On several
occasions some field data could not be measured or recorded
because of technical difficulties.

4.

Laboratory Measurement Methods[9] For the sake of brevity we summarize here the important
points of our laboratory procedures. Detailed descriptions of themethods are given by Shouse et al. [2002] and Mohanty et al. [1999].
4.1.

Physical Properties

4.1.1. Particle size distribution. [10] A complete particle
size distribution function (composed of 18 – 21 data points) for
each soil sample was measured. The basic hydrometer method
outlined by Gee and Bauder [1986] was used to determine the silt
and clay fractions from approximately 50 microns to 1.4 microns.
The same sample was used for determining the sand fractions
between 2000 microns and 50 microns using a wet sieve method.

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MOHANTY ET AL.: DATA AND ANALYSIS NOTE

4.1.2. Bulk density. [ 11 ] The core method [Blake andHartge, 1986] was used to measure the bulk density. In mostcases we obtained at least two different estimates of the bulk
density for each sample location and depth. The average of these
two estimates is reported. Total soil porosity [Danielson andSutherland, 1986] for each sample was calculated using themeasured bulk density of the sample and a particle density of
2.65 g/cm3.
4.1.3. Soil organic carbon. [12] Soil organic carbon content
was measured directly by furnace combustion at 375C using a
UIC Full Carbon System150 with a CO2 coulometer 5011 (UIC
Inc., Joliet, Illinois).
4.1.4. Soil structure and color. [13] Soil color, structure,and other pertinent profile data were gathered during soil coringwith the truck-mounted Giddings probe. A Munsell soil chart wasused to identify soil color under wet and dry conditions. Soil
structure was defined by manual and visual inspection. Thismethod is limited to soil coring (based on various practicalissues); however, it must be noted that soil structure can be best
described by opening a soil profile.
4.2. Hydraulic and Thermal Properties
4.2.1. Saturated hydraulic conductivity. [14] We used a
constant head permeameter to measure the saturated hydraulicconductivity as described by Klute [1986]. To reduce air
entrapment, we used a dilute calcium chloride solution instead ofwater for these measurements.
4.2.2. Soil water retention. [15] The soil water retention
curve was measured using a combination of measurement
techniques. We used Tempe cells [Reginato and van Bavel, 1962;
Klute, 1986; Eching et al., 1994] for retention measurements
between 10 cm and 500 cm of pressure head and used the pressure
plate apparatus [Klute, 1986] for measurements at 333, 500, 1000,
3000, 8000, and 15,000 cm of pressure head. The method is detailed
by Klute [1986]. The equilibration time varied between 10 and
60 days depending upon the texture of the soil and the pressurehead.
[16] An important new feature of our measurement method was asemiautomatic Tempe cell that was used to control and record thesimultaneous measurement of 22 Tempe cells. The apparatus
produced a series of outflow data as a function of time. The
cumulative outflow was recorded every 6 min using a data acquisition system and pressure transducers calibrated to measure water
volumes (dynamic outflow). We allowed the outflow to cease
completely before changing to a higher pressure. With this approach
we measured the dynamic multistep outflow as well as an equilibrium retention point. The dynamic outflow data can be analyzed
using inverse procedures to calculate the soil hydraulic parameters.
The more conventional static measurements can be used to help
refine the inverse problem, thereby lessening nonuniqueness problems and improving convergence. Static retention measurements
are available for all 157 soil cores while dynamic outflow data are
available for approximately 127 soil cores. Dynamic outflow data
for the other soil cores were not obtained because of variety ofreasons, including technical difficulties during the laboratory measurements. All water retention and dynamic outflow measurements
were accomplished within a period of 2 years.
4.2.3. Soil water retention (hysteresis). [17] Forty-four
samples were selected for measurement of soil water retention
hysteresis. We measured hysteresis using Tempe cells. Each core
was equilibrated at 500 cm pressure head following the drainage
sequence, after which the pressure was decreased sequentially for
imbibition to occur until the core was again saturated. The dynamic

inflow rate was measured in exactly the same manner as theoutflow, and a mass balance equation was used to calculate thewater content.
4.2.4. Soil water retention (temperature effect). [18] Thesame 44 soil cores were used to measure the temperaturedependence of the soil water retention curve. This consisted ofmeasuring the retention at 20C, 25C, and 30C by changing theroom temperature before the cores were allowed to saturate. The
equilibration time was 24 hours. The dynamic outflow wasmeasured, and the mass balance was again used to calculate thewater content of the cores.
4.2.5. Saturated hydraulic conductivity (temperatureeffect). [19] In addition to soil water retention, we also measured
the saturated hydraulic conductivity for selected soil cores at 20C,
25C, and 30C room temperatures.
4.2.6. Heat transmission. [20] We used the dual-probe heatpulse technique of Bristow et al. [1994] to measure heattransmission in the soil cores. A heat pulse was generated byapplying a voltage from a direct current source to the heater for a
fixed period of time (8 s). We used a CR7 (Campbell ScientificInc., Logan, Utah) data logger to control the heat pulse, monitor thecurrent through the heater, and measure the temperature of thesensor as a function of time. The dynamic temperature data wereused to estimate the soil thermal properties using a numericalinversion procedure developed by Welch et al. [1996].

5. Data Quality[21] We took care to assure good and consistent quality of ourentire soil property database. Some important quality control
measures include the following: (1) replicate sampling, (2) veryfine time resolutions, (3) multiple operators verifying each others’
data, (4) state-of-the-art experimental facilities including humidity
and temperature control rooms, and (5) verification of mass orenergy balances within reasonable limits.

6. Data Analyses[22] In sections 6.1 – 6.3 we provide few typical examples of theuses for our SGP97 soil property data.
6.1. Example 1: Particle Size Distribution
and Pedo-Transfer Function[23] Our 18 – 21 point particle size data were grouped into sand(<2000 – 50 mm), silt (50 – 2 mm), and clay (<2 mm) fractions
according to the USDA standard. The grouped data (% sand, %silt, and % clay) were used to derive the textural class of each soil
sample according to the USDA textural triangle [Gee and Bauder,1986]. Soil textural distributions at LW, ER, and CF are shown in a
USDA textural triangle in Figure 2. Evidently, there is a wide rangeof textural classes represented by the samples. Sand contents range
from 15 to 95%, and clay contents range from 2 to 50%. Also
apparent from Figure 2 is that the Little Washita watershed
contains soils with much more variation in texture than the El
Reno or Central Facility locations. In general, Little Washita
watershed soil texture ranged between sand, loamy sand, sandy
loam, loam, and silt loam.
[24] Soil water retention or hydraulic conductivity functions canbe predicted from particle size distribution data using pedo-transfer
functions (PTFs) [e.g., van Genuchten et al., 1992; Tietje andTapkeenhinrichs, 1993], which can be directly applied in a wide
range of hydrologic models. One of the early PTF studies was by
Arya and Paris [1981], who presented a model for predicting theMOHANTY ET AL.: DATA AND ANALYSIS NOTE

5-5

Figure 2. Soil texture distribution across the LW, ER, and CF
focus regions based on particle size distribution measurements of
the 157 soil samples.

water retention curve from the particle size distribution, bulk
density, and particle density. Their approach has been extended
and modified by many researchers. Among others, Schaap andBouten [1996] used neural networks (NNs) to model the drying
water retention curve from particle size distribution, soil organic
matter content, and bulk density. In a follow-up study, Schaap
et al. [1998] showed improved accuracy by using a hierarchical
NN-based approach with an increased number of input parameters.
Our SGP database is well suited for developing hierarchical PTFs
for a hierarchy of spatial scales (e.g., quarter section, watershed,
and region). Combining collocated topographic and vegetation datawith basic soil property data may further improve the accuracy ofsoil hydraulic property estimation by extending PTFs to pedotopography-vegetation-transfer functions (PTVTFs).
6.2. Example 2: Water Retention, Dynamic Outflow,
and Estimation of Soil Hydraulic Functions[25] Computer models are routinely used to simulate water flowin the vadose zone and across the land-atmosphere boundary (e.g.,
SVAT schemes). Closed-form expressions [e.g., van Genuchten,
1980; Brooks and Corey, 1964] have been widely employed todescribe the unsaturated soil hydraulic properties for various
reasons including the following: (1) They are attractive to model
the q(h) and K(h) or K(q) relationships in numerical models, (2)nontabular data simplify input to computer models, and (3)hydraulic properties can be estimated using inverse procedures.
[26] We used our dynamic outflow and static retention data in
the HYDRUS-1D inverse modeling procedure [Šimunek et al.,Figure 4. (a) Sample dynamic outflow data for soil sample 24
and (b) corresponding dynamic pressure steps. Total number ofdata points on each curve is 2126.

1998] to estimate the van Genuchten-Mualem hydraulic parameters [van Genuchten, 1980] for each soil core. Example water
retention (Figure 3), dynamic outflow (Figure 4a), and corresponding pressure-time (Figure 4b) data are presented. The inverse
optimization procedure was based upon minimization of an
objective function that expresses the discrepancy between theobserved values and the predicted system response in terms of
cumulative outflow and/or soil water retention. Initial estimates ofthe optimized hydraulic parameters were iteratively improved
during the minimization process until a desired degree of precision
was obtained. We performed the inverse optimization procedure
twice with (1) equal weights for both dynamic flow and static
retention data and (2) double weights for the static retention dataas compared to the dynamic outflow data. For the soil cores withno dynamic outflow data we used the RETC estimation procedure[van Genuchten et al., 1991] for estimating the soil hydraulicparameters from the retention data only (no value of Ks could be
estimated in this manner). The van Genuchten [1980] functions
contain six independent parameters: qr , qS, KS, a, n, and l. During

Figure 3. Soil water retention data for three soil samples (samples 26, 86, and 132) between 0 and 15,000 cm soil
water tension.

5 -6

MOHANTY ET AL.: DATA AND ANALYSIS NOTE

Figure 5. Comparison of measured and optimized soil water retention curve(s) with HYDRUS-1D inverse
modeling procedure using (1) only dynamic outflow data and (2) dynamic outflow and soil water retention data.
the inverse optimization procedure, goodness-of-fit was quantified
by the value of the objective function  being minimized and ther2 value for regression of observed versus fitted values. For soilcores where both dynamic outflow and retention data are available, two sets of optimized parameters (qr (WCR), qS(WCS),
KS (CONDS), a (ALPHA), n (N), and l(L)) with goodness-of- fit
values ( (SSQ), r2 (RSQ)) are reported in the summarized database [Mohanty et al., 1999]. A single set of retention parameters isavailable for soil cores without dynamic outflow data where
KS (CONDS = 1.0) and l (L = 0.5) were fixed. Soil water retentionfunctions estimated from combined dynamic outflow and static
retention data fit the measured retention curves better than functions estimated from dynamic outflow data alone (see example
shown in Figure 5). The use of both dynamic outflow data andstatic retention data in the inverse procedure helps constrain the
inverse procedure in the dry end of the curve. These optimized van
Genuchten parameters can be used directly in a soil hydrology
component of SVAT schemes for the SGP97 study area. The
parameters for Brooks and Corey [1964] and other hydraulicfunctions can be similarly optimized from our soil water retention
and dynamic outflow data as necessary.
6.3. Example 3: Soil Thermal Properties[27] Using the HPC inverse optimization procedure [Welchet al., 1996], we estimated soil thermal properties on the basis of
the dual-probe heat pulse data. The computer model allows for
simultaneous estimation of the volumetric heat capacity rc, thethermal diffusivity k , and the thermal conductivity (l = rck). The
parameter estimation scheme requires extraction of the temperature maximum Tm and the time needed to reach the maximum
temperature, tm, from a temperature-time record. The constants Tm
and tm are subsequently used in a heat conduction model for an
infinite line heat source in order to determine rc, k, and l. The
approach involves a ‘‘single-point’’ parameter estimation scheme
since the model is fitted to the data at a single point, themaximum temperature. With only a few exceptions the heatpulse data and model were in good agreement, suggesting thatreasonable estimates of the soil thermal properties weregenerated.
[28] The database has many more potential uses in surface andsubsurface hydrologic modeling. The database can be used to test
models of water and energy dynamics at the land-atmosphereboundary, as input to data assimilation schemes, for calibration/
validation of land surface data products of airborne and spaceborne
remote sensors and for nonpoint source contaminant transport
studies in the southern Great Plains region. Assuming that the soil

properties in our database are static (i.e., independent of time), thedatabase can be used with remotely sensed data sets collected in
the southern Great Plains during Washita’92, Washita’94, SGP97,
and SGP99, as well as forthcoming missions (e.g., the planned Soil
Moisture (SMEX) campaigns of NASA and others). Data fromother historical field campaigns are located at the NASA Goddard
Space Flight Center Distributed Active Archive Center Web sites.
While our SGP97 database was developed in a focused region
using consistent methods throughout, few other soil property
databases developed in the United States and Europe, based onvolunteer contributions from different researchers, need amention here. For example, Unsaturated Soil Hydraulic Database(UNSODA), by F. J. Leij, W. Alves, and M. T. van Genuchten,
USDA ARS Salinity Laboratory, Riverside, California, was developed in the United States, and Grizzly Soil Database, by
R. Haverkamp, C. Zammit, and F. Bouraoui, Laboratoire d’Etude
des Transferts en Hydrologie et Environnement, Grenoble, France,and Hydraulic Properties of European Soils Database (HYPRES),
by H. Woesten and A. Lilly, Winand Staring Centre for Integrated
Land, Soil and Water Research (SC-DLO), Wageningen, Netherlands, were developed in Europe. In a more recent effort, joint
contribution from various U.S. and European agencies has created a
Global Soil Data Task, assimilating data from various sources.
These data are distributed by International Geosphere-Biosphere
Programme, Toulouse, France, and are available at http://www.
meteo.fr/cnrm/igbp/. As the sources of data in various databases
cover a wide range of soils, hydrogeologic conditions, and measurement methods, proper pretreatment and quality check are warranted
before their use. Further details of some of these databases andassociated research efforts were compiled by Bruand et al. [1996]
and van Genuchten et al. [1999].

7. Summary[29] A much-needed comprehensive database of soil physical,
hydraulic, and thermal properties across the SGP97 hydrology
study region has been developed. The data can be used fordeveloping and testing evolving hypotheses in soil hydrology,
SVAT schemes in GCMs, calibration/validation of remote sensor(s)land surface data products, and contaminant transport studies atvarious scales.

8. Data Files[30] Summary data files of the soil physical, hydraulic, andthermal properties, associated topographic and vegetation data, andMOHANTY ET AL.: DATA AND ANALYSIS NOTE
other ancillary information are available in Excel spreadsheetsfrom Mohanty et al. [1999]. Raw dynamic outflow data, soil water
retention data (including hysteresis and temperature effects), andparticle size distribution data are given in the report by Shouse
et al. [2002], which is available upon request. Data are organizedby ascending sample identification (1, 2, . . ., 157). Corresponding
quarter section identification (LW1 to LW23, ER1 to ER16, CF1 toCF9), allocated site number (1, 2, . . .), and/or their geographic
location (latitude and longitude) are also tagged.
[31] Acknowledgments. The help of many SGP97 participants in thefield data collection is acknowledged. This study was funded by NASA
Land Surface Hydrology Program grant NAG5-8682.

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D. A. Miller, EMS Environment Institute, Pennsylvania State University,
2217 Earth-Engineering Science Building, University Park, PA 16802,
USA.
B. P. Mohanty, Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843-2117, USA.
(bmohanty@tamu.edu)
P. J. Shouse and M. T. van Genuchten, George E. Brown Salinity
Laboratory, 450 W. Big Springs Road, Riverside, CA 92507, USA.

WATER RESOURCES RESEARCH, VOL. 38, NO. 5, 10.1029/2000WR000076, 2002

Figure 1. Geographical location of Southern Great Plains 1997 Hydrology Experiment in Oklahoma. The light graywindow (40 km  250 km) indicates the SGP97 flight line area for remote sensing measurements of soil moisture.
Little Washita watershed in the south, El Reno in the center, and Central Facility in the north are the SGP97 focus
regions for ground data collection and validation activities.

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