Estimates of Soil Moisture for CYs 2000 and 2001 from the Oklahoma Mesonet
Oklahoma Climatological Survey
Norman, OK 73019
February 2002
Version 1.0


Table of Contents

            1. Summary of important notes to users
            2. Definition of quantities provided
            3. Ancillary data sets which may be useful in analysis of data
            4. Background on the estimates of soil moisture from the
              Oklahoma Mesonet
            5. Processing applied to the data in this delivery
            6. Contacts for further information

              Appendix A: Table of coefficients for conversion to soil water content
              Appendix B: Diagram and description of sensor operation
  1. Summary of important notes to users
  1. Definition of quantities provided

    The variables provided in this data delivery include quantities measured at 4 depths (5, 25, 60,
    and 75 cm below the surface). For each depth, four variables are reported, and each variable has
    a separate quality assurance flag (Q). The definitions of the variables are provided here. More
    detailed explanations of the variables and quality control flags are provided in Section V of this
    document. Users will note that when a quality control flag is equal to 0, the data are rated
    “good”; a value greater than zero indicates that the data are suspect, missing, or not checked.

    The variable provided for each depth is:

    TR: The DeltaT reference temperature. This is the value of DeltaT (which is the quantity
    directly observed by these sensors), but normalized for individual sensor response. After
    normalization, the quantity is referred to as the DeltaT reference temperature.

    MP: Matric, or soil water potential (bars)
    WC: Volumetric soil water content (m3water/m3soil)
    ST: Pre-heating temperature (C)
    Times shown are UTC. The naming convention for the variables is such that the first two letters
    refer to the quantity (TR) and the last two numbers refer to the depth of the measurement for that
    quantity. A quality control flag (Q) is provided after each datum.

    The data provided in space delimited ascii format are:

    ST05, Q, TR05, Q, MP05, Q, WC05, Q, ST25, Q, TR25, Q, MP25, Q, WC25, Q,
    ST60, Q, TR60, Q, MP60, Q, WC60, Q, ST75, Q, TR75, Q, MP75, Q, WC75, Q, TREF

    "TREF" is another quality control parameter, and is used to check the other values. It is defined in the quality assurance portion of this document.

  2. Ancillary data tables which may be useful in analysis of data

     At least two sets of ancillary data may be of interest to users:

    a) A table of spatial coordinates for Mesonet sites (GEOMESO.TBL, provided in this
    data release).

    b) Tables that summarize the soil and vegetation characteristics surrounding the
    Mesonet sites.

    These tables are intended to be helpful to users who desire to compare point-based Mesonet
    measurements to areally integrated estimates generated with models or remote sensing data.
    They provide information related to the distribution of different soils and vegetation types
    surrounding the Mesonet stations. The tables are available in at 4km X 4km and at 10km X
    10km footprints centered on Mesonet sites. These support data are available for all Mesonet and
    all ARM/SWAT sites within Oklahoma. The tables related to soils information exist for all 4
    depths of the soil moisture measurements. The input data used to generate these tables are the
    NRCS/MIADS 200 m resolution raster data set for soils and land use/land cover. The tables
    will be updated as better data sources become available (for example, when NRCS/SURGO data
    become available for more Oklahoma counties). To obtain these tables, please contact Karen
    Humes (khumes@uidaho.edu; 208-885-6506).

  3. Background on the estimates of soil moisture from the Oklahoma Mesonet

    The Oklahoma Mesonetwork (Mesonet) began in 1991 as a state-wide mesoscale environmental
    monitoring network. (Brock et al. 1995). In 1996, with NSF EPSCoR support, soil moisture
    sensors were added to 60 of the approximately 115 Mesonet sites. At these 60 sites, sensors
    were installed at 3 depths: 5cm, 25cm, and 60cm below the surface. For most sites, a fourth
    sensor was installed at 75 cm below the surface. The soil profile at a few sites would not
    accommodate the deepest sensor. The data are averaged over 30 minute intervals and
    transmitted to a central ingest computer at the Oklahoma Climatological Survey (OCS) at the
    University of Oklahoma. In partnership with Oklahoma State University, OCS is the Oklahoma
    agency which maintains and operates the Mesonet.

    The soil moisture sensor, which is described in detail in Appendix B, operates on the principle of
    heat dissipation. It is the same sensor deployed in two other networks in the Southern Great
    Plains region which include soil moisture measurements: a) the ARM/SWATS network
    (Schneider et al. 2002) and b) the USDA/ARS SHAWMS network (Starks 1999) (located
    primarily in the Little Washita Experimental Watershed). The quantity measured by these
    sensors is the temperature change over time within a needle enclosed in a porous ceramic in
    contact with the soil. These measurements of temperature change (DeltaT) have been carefully
    calibrated to soil water potential, which is typically expressed in units of pressure (or force per
    unit area); potential represents the force with which the water is is "held" in the soil. This is an
    important quantity in soil physics because spatial gradients in soil water potential provide the
    “driving force” for soil water movement.     One of the advantages of a sensor which is sensitive
    to soil water potential is that the calibration equation used to convert the directly observed
    quantity to soil water potential is typically not dependent on site-specific factors such as soil
    texture.

    Though soil water potential is the variable which controls the movement of water in the
    subsurface soil layers and from the soil to plants, many models that utilize mass balance as an
    important constraint require volumetric soil water content for a measure of the soil moisture.
    The relationship between soil water potential and water content (usually referred to as the "soil
    water characteristic curve" or "retention curve") is highly dependent on many soil
    characteristics, primarily pore size distribution, macropore space and organic matter content.

    Additionally, this relationship sometimes exhibits hysteresis. Soil water retention curves can be
    measured in the laboratory with a pressure plate apparatus by desorbing an initially saturated
    sample to a known soil water pressure, then measuring the equilibrium soil water content, and
    fitting curves to the measured points. These pressure plate tests should be performed on intact,
    "undisturbed" samples, which are carefully extracted in the field.

    Because of the large number of sites and depths involved in the acquisition of Mesonet soil
    moisture data, it was not feasible to perform laboratory measurements of the soil water retention
    curve for all sites and depths. The soil water retention relationships used to compute soil water
    content have been derived using a combination of published techniques and empirical corrections
    to these techniques. This approach utilized information on soil particle size distribution available
    for all sites and depths (12-15 size categories for each site/depth location), measured bulk density
    at some sites, and an empirical correction derived from examination of laboratory-measured
    retention curves for the ARM/SWATS sites. More details on this approach are available in
    Humes et al. (2002).

    Soil water content validation data were acquired at a large number of sites via both gravimetric
    sampling of the soil profile and neutron probe measurements. Details of these results are
    provided in Humes et al. (2002). The validation results indicate that the estimates of soil water
    content derived by these techniques are reasonable at most sites. There is some indication that
    the sensor values tend to overestimate soil water content slightly under very dry soil conditions.
    We feel this is due to inaccuracies in the Mesonet™s ability to convert soil water potential
    measurements to soil water content values for some sites.

    Using NSF support obtained via an MRI (Major Research Infrastructure) grant for a project
    known as OASIS, soil moisture sensors were installed at approximately 40 additional sites
    during 1999. Thus, the data record for these 40 i0newl. sites does not span the entire CY1999.

    The OASIS Project expanded the measurement capabilities of the Mesonet to include other
    variables, which are of potential interest to researchers. Surface energy flux measurements,
    including net radiation, soil heat flux, sensible heat flux are being acquired at approximately 100
    sites. Ten of these sites are designated as issupersitesló, and are specially equipped to allow the
    measurement of net radiation and surface energy fluxes with multiple types of instrumentation.
    Measurements at these supersites include separate measurements of all four components of the
    surface radiation balance, as well as measurement of sensible and latent heat flux with eddy
    correlation techniques. Notice of these additional measurement capabilities within the
    Oklahoma Mesonet is for informational purposes only.

  4. Processing applied to the data in this delivery

    A) QA procedures

    The Oklahoma Mesonet uses the Campbell Scientific Model 229L sensor to provide indirect
    measurements of the soil matric potential (MP) and volumetric soil water content (WC).
    First the sensor temperature (STxx) for each depth (xx) is measured. Then the sensor is
    heated for a short period, after which the sensor temperature is measured again (FTxx). The
    difference in the two temperatures (Tdiff) is used to derive the reference delta temperature
    (TRxx). Because of the way the sensors are wired to the datalogger, a temperature bias exists
    in STxx and FTxx. A reference thermistor is used to remove this bias. The measurement
    returned from the reference thermistor (TREF) is used only for QA purposes.

    The Oklahoma Mesonet performs several routine QA procedures on the variables STxx,
    TRxx, and TREF listed above. The procedures used consist of range, freeze, and step tests.
    The range test is used to ensure that ST, TR, and TREF do not go beyond normal pre-
    determined limits. The freeze test is used to identify possible frozen sensors.

    The step test checks for spikes in TRxx values (large change in consecutive readings). The
    change in TRxx values is calculated as the TRxx value for the current time period minus the
    TRxx value from the preceding time period. Only the endpoints of the run of suspicious data
    are flagged. Visual inspection is necessary to determine the duration of the event. The
    particulars of each test are given below.

    The very wet limit of sensitivity for the sensor has been determined to Œ0.10 bars. Values of
    soil water potential (MP) between 0.00 and Œ0.10 bars are not accurate. Because the lower
    limit of observed values of DeltaT reference is approximately 1.4 °C, the equation for
    computing soil water potential does not return values much more moist than Œ0.10 bars.

    Soil water potential values drier (i.e., more negative) than -6.0 bars should be considered
    "suspect". Although data values are drier than -6.0 bars are still considered useful, soil
    potential values in that range have been unreproducable in the laboratory.

    QA flags (Q) may have a value ranging from 0 to 9:
      0 Good Datum has passed all QA tests
      1 Suspect There is concern about accuracy of the datum
      2 Warning Datum is questionable but information can be extracted
      3 Failure Datum is unusable
      4 Not Installed Station awaiting installation of sensor
      7 Not Checked Datum not checked by QA processes
      8 Never Installed This station is not intended to measure this parameter
      9 Missing Data Datum is missing for this station and parameter

Another QA indictor is implied with the value used to replace missing data:

  -996 Datum is missing
  -997 Datum is missing due to missing calibration value
  -998 Datum is missing due to no instrument installed
  -999 Datum is missing due to QA applied


Another possible source of sensor error has been identified in Basara and Crawford (2000). This
error is identified by the rapid wetting of the 60 cm and 75 cm sensors after a precipitation event,
due to preferential flow through the (refilled) trench dug to install the sensors. A potential
problem occurred with the installation of a subsurface sensor. It typically takes time for a
refilled trench to iohealld (the term applied to the development of good contact between the
disturbed soil in the trench and the undisturbed soil in which the sensor is placed). In some soils,
it is nearly impossible to avoid the development of permanent macropores near the interface
between the disturbed and undisturbed soils. Soils whose properties are conducive to the
development of large macropores upon drying (such as those with shrink/swell clays) are most
susceptible to preferential flow problems.

The most common condition when this type of problem occurs is when a heavy precipitation
event follows an extended dry period (i.e., the soil profile is dry throughout). The data pattern
characteristic of this problem is an immediate wetting of the deeper (60 cm and 75 cm) sensors
after a heavy rain event, followed by a rapid drying of these sensors, which returns the sensor to
its pre-wetted condition. When this problem occurs, the shape of the time series of the data is an
upside-down spike (an immediate decrease followed by a rapid increase in TR). The ihnormallw
response for a recently wetted-soil is a decrease in TR, followed by a few days of level,
unchanging values, then a slow increase in TR as the soil begins to dry.

It is difficult to codify this behavior in an automated QA routine; thus, the data in this delivery
were not "filtered" to remove this preferential flow problem.

The errant deep-layer values of soil moisture have been detected following extended dry periods
in soils consisting of high silt and clay fractions (such as the Norman Mesonet site). However, in
most cases and at most sites, these measurement errors do not occur.

It should be noted that this problem is an anomaly in the standard operation of soil moisture
sensors across Oklahoma. Of over three million observations of soil moisture conditions
between 1996 and 1999, the number of observations affected by this installation error account
for less than one percent.


B) Conversion of DeltaT to DeltaT reference (TR)

This conversion requires sensor-specific coefficients which are determined based on the
maximum and minimum observed DeltaT values for each individual sensor. The maximum
DeltaT is determined in the lab by placing the sensor in a sealed container and drying the sensor
completely using a desiccant pack. The minimum DeltaT is determined after the sensor has been
installed in the field and has undergone several wetting-and-drying cycles. The minimum DeltaT
is the lowest value of DeltaT to which the sensor drops after being completely wetted.

A linear regression is used to "normalize" the response of an individual sensor to that of an
idealized reference sensor having the following maximum and minimum DeltaT (dT) values:

dTmax = 3.96 C
dTmin = 1.38 C.

The linear regression used to normalize an individual sensor's response is of the form:

TR = m * dT + b

where TR is referred to as the DeltaT reference temperature.

The regression coefficients m and b are determined by substituting the maximum and minimum
dT values for an individual sensor and for the reference sensor into the regression equation, and
solving for m and b. This results in the equations:

m = ( dTmaxref - dTminref ) / ( dTmaxsensor - dTminsensor )
or
m = ( 3.96 - 1.38 ) / (dTmaxsensor - dTminsensor )
and
b = 3.96 - m * dTmaxsensor.

Each sensor, therefore, has its own unique coefficients for normalizing its response.
Determining coefficients for each sensor results in a table of sensor-specific coefficients used to
compute the DeltaT reference (TRxx) values provided in this data set.

C) Conversion of DeltaT reference (TR) values to Soil Water Potential (MP)

The following generalized equation was applied to all DeltaT reference values (TR):

MP = - (c * exp ( a * TR))/100

where
MP = matric potential (bars)
TR = DeltaT reference ( o C)
a = 1.788
c = 0.717

The limit of sensitivity for the sensor has been determined as -0.10 bars. Values of soil water
potential (MP) between 0.00 and -0.10 bars are not accurate. Because the lower limit of
observed values of DeltaT reference is approximately 1.4 deg C, the equation for computing MP
does not return values much more moist than -0.10 bars.

D) Conversion of Soil Water Potential Values to Soil Water Content

WC = WC r + ( WC s - WC r ) / ( 1 + ( a * -MP) ^ n ) ^ ( 1 - 1 / n )

where
WC = soil water content on a volume basis (m3water/m3soil).
WC r = residual water content (m3water/m3soil).
WC s = saturated water content (m3water/m3soil).
a , n = empirical constants
MP = matric (soil-water) potential (bars).

The four coefficients, WC r , WC s , a , and n, for each depth for each site are in a table of
coefficients in Appendix A. The empirical relationship to estimate volumetric water content
(section V. D.) and the latest coefficients (Appendix A) are provided. Users may obtain an
electronic version of these coefficients by contacting Jeff Basara (jbasara@ou.edu; (405-325-1760).

  1. Contacts for further information

    This data delivery is the product of a combined effort by the soil moisture research team and the
    Mesonet team at the Oklahoma Climatological Survey (OCS).

    Additional processing to compute the volumetric water content was provided by Alan Robock and Haibin Li
    of the Department of Environmental Sciences of Rutgers, the State University of New Jersey and Lifeng Luo of
    the Environmental Engineering and Water Resource Department of Civil and Environemental Engineering,
    Princeton University. The data was reformatted, as well, to be consistent with previous OCS soil moisture data sets.

    The research team consisted of Jeff Basara (University of Oklahoma/OCS), Ron Elliott
    (Oklahoma State University), Ken Fisher (private consultant), Gary McManus (OCS), and Karen
    Humes (University of Idaho). The data processing team consisted of Jessica Thomale (OCS),
    David Demko (OCS), Gary McManus (OCS), Jeff Basara (University of Oklahoma/OCS), Renee
    McPherson (OCS), Brad Illston (University of Oklahoma/OCS), and Mike Wolfinbarger (OCS).
    For questions regarding the processing of the data, particularly the conversion of raw variables to
    soil water potential or soil water content, please contact Jeff Basara (jbasara@ou.edu; 405-325-
    1760) or other members of the research team.

    For any question about the QA procedures applied to the operational data processing stream, or
    questions relating to the format and content of the variables delivered, please contact Gary
    McManus of the Oklahoma Climatological Survey (gmcmanus@ou.edu; 405-325-3076).

    References:

    Basara, J.B., and T.M. Crawford, 2000. Improved installation procedures for deep layer soil
    moisture measurements. Journal of Atmospheric and Oceanic Technology, 17, 879-884.

    Brock, F.V., K.C. Crawford, R.L. Elliott, G.W. Cuperus, S.J. Stadler, H.L. Johnson, and M.D.
    Eilts, 1995. The Oklahoma Mesonet: A technical overview. Journal of Atmospheric and
    Oceanic Technology, 12, 5-19.

    Humes, K.S., J.B. Basara, R.L. Elliott, D.K. Fisher, and K.C. Crawford, 2002. Validation of soil
    moisture observations from the Oklahoma Mesonet. To be submitted to the Journal of
    Hydrometeorology.

    Schneider, J.M., D.K. Fisher, R.L. Elliott, G.O. Brown, and C.P. Bahrmann, 2002.
    Spatiotemporal Variations in Soil Water: First Results from the ARM SGP CART Network,
    Submitted to Journal of Hydrometeorology. In Review.

    Starks, P. J., 1999: A general heat dissipation sensor calibration equation and estimation of soil
    water content. Soil Sci., 164, 655-661.

 

Appendix A: Table of coefficients for conversion to soil water content

Version 8 - May, 2001
Sites with measured bulk density:

Site Depth Texture WCr WCs - n
- cm * mm/mm mm/mm 1/bar -
 
acme 5 SL 0.189 0.519 131.413 1.646  
acme 25 SCL 0.221 0.428 37.376 1.756  
acme 60 SCL 0.181 0.411 1384.677 1.215  
acme 75 SL 0.212 0.411 38.497 1.803  
 
adax 5 SL 0.188 0.462 272.847 1.463  
adax 25 SL 0.180 0.342 65.528 1.603  
adax 60 x x x x x  
adax 75 x x x x x  
 
apac 5 LS 0.176 0.464 33.705 2.597  
apac 25 SL 0.162 0.422 1168.364 1.260  
apac 60 CL 0.159 0.397 257.715 1.168  
apac 75 CL 0.207 0.389 68.662 1.340  
 
bixb 5 SL 0.170 0.660 126.685 1.717  
bixb 25 SiL 0.000 0.424 1447.533 1.099  
bixb 60 L 0.180 0.386 11.851 1.970  
bixb 75 SL 0.222 0.513 13.893 1.783  
 
blac 5 SiL 0.000 0.460 98.006 1.129  
blac 25 CL 0.239 0.449 11.723 1.683  
blac 60 CL 0.207 0.397 25.413 1.366  
blac 75 CL 0.189 0.394 34.301 1.295  
 
bowl 5 SL 0.180 0.418 51.011 1.534  
bowl 25 SL 0.201 0.418 51.368 1.532  
bowl 60 CL 0.206 0.431 195.526 1.249  
bowl 75 CL 0.211 0.431 116.793 1.314  
 
brec 5 SiL 0.148 0.457 90.267 1.248  
brec 25 SiCL 0.268 0.438 6.008 1.846  
brec 60 SiC 0.211 0.397 10.370 1.338  
brec 75 x x x x x  
 
byar 5 LS 0.167 0.445 134.245 1.766  
byar 25 SCL 0.185 0.347 285.978 1.310  
byar 60 SCL 0.182 0.373 2826.528 1.198  
byar 75 SCL 0.187 0.309 7313.215 1.241  
 
calv 5 SL 0.178 0.459 20.657 1.962  
calv 25 L 0.172 0.392 24.244 1.773  
calv 60 x x x x x  
calv 75 x x x x x  
 
cato 5 SiCL 0.226 0.467 11.827 1.488  
cato 25 SiCL 0.180 0.467 33.982 1.213  
cato 60 C 0.243 0.466 37.712 1.180  
cato 75 x x x x x  
 
chan 5 SCL 0.234 0.562 764.274 1.352  
chan 25 C 0.192 0.373 323.736 1.089  
chan 60 C 0.164 0.435 178.366 1.103  
chan 75 C 0.226 0.409 1064.987 1.132  
 
copa 5 L 0.175 0.510 47.692 1.464  
copa 25 L 0.158 0.435 168.613 1.225  
copa 60 x x x x x  
copa 75 x x x x x  
 
elre 5 SiL 0.224 0.535 12.847 1.878  
elre 25 SiL 0.000 0.493 118.331 1.127  
elre 60 SiCL 0.230 0.425 8.208 1.763  
elre 75 SiCL 0.182 0.390 21.185 1.283  
 
fair 5 SiL 0.000 0.455 354.067 1.110  
fair 25 SiL 0.229 0.455 9.707 1.895  
fair 60 CL 0.246 0.431 14.503 1.598  
fair 75 SiCL 0.163 0.443 38.142 1.179  
 
ftcb 5 LS 0.199 0.554 122.466 1.873  
ftcb 25 SCL 0.197 0.376 591.041 1.434  
ftcb 60 LS-SL 0.191 0.426 33.517 2.545  
ftcb 75 LS 0.179 0.419 54.178 2.321  
 
guth 5 SL 0.193 0.453 642.443 1.319  
guth 25 L 0.158 0.364 364.040 1.158  
guth 60 x x x x x  
guth 75 x x x x x  
 
hask 5 SiL 0.178 0.454 6.983 1.678  
hask 25 SiL 0.152 0.455 19.741 1.391  
hask 60 SiCL 0.253 0.443 6.954 1.685  
hask 75 SiC 0.281 0.455 7.424 1.771  
 
hect 5 L 0.024 0.442 520.328 1.127  
hect 25 SL 0.143 0.408 193.180 1.312  
hect 60 SL 0.181 0.389 99.877 1.621  
hect 75 CL 0.226 0.430 382.277 1.170  
 
hint 5 SL 0.178 0.462 34.267 2.177  
hint 25 LS 0.175 0.408 30.834 2.651  
hint 60 LS 0.178 0.447 30.412 2.658  
hint 75 LS 0.179 0.442 27.414 2.547  
 
ketc 5 L 0.000 0.576 1034.369 1.115  
ketc 25 CL 0.191 0.415 75.840 1.237  
ketc 60 C 0.193 0.416 417.889 1.141  
ketc 75 CL 0.223 0.392 850.116 1.195  
 
king 5 L 0.192 0.511 18.481 1.926  
king 25 SiL 0.000 0.469 420.103 1.118  
king 60 L 0.220 0.425 14.691 1.801  
king 75 L 0.225 0.403 9.025 2.029  
 
laho 5 SiL 0.000 0.464 387.322 1.110  
laho 25 CL 0.242 0.424 7.858 1.985  
laho 60 SiCL 0.166 0.386 31.183 1.227  
laho 75 SiCL 0.134 0.412 8.867 1.233  
 
mare 5 SCL 0.217 0.435 48.200 1.604  
mare 25 L 0.222 0.449 25.893 1.819  
mare 60 SCL 0.229 0.405 32.879 1.667  
mare 75 SCL 0.218 0.380 45.967 1.641  
 
mars 5 SiL 0.217 0.438 9.689 1.848  
mars 25 SiC 0.220 0.463 13.572 1.328  
mars 60 SiC 0.202 0.422 11.120 1.304  
mars 75 SiCL 0.172 0.381 59.897 1.184  
 
miam 5 SiL 0.140 0.531 13.635 1.510  
miam 25 SiL 0.000 0.454 13.010 1.240  
miam 60 SiC 0.192 0.459 18.968 1.188  
miam 75 SiC 0.061 0.428 13.273 1.129  
 
newk 5 SiL 0.232 0.558 11.289 1.956  
newk 25 SiL 0.196 0.462 22.702 1.413  
newk 60 SiCL 0.236 0.414 7.164 1.749  
newk 75 x x x x x  
 
norm 5 SiL 0.000 0.480 56.590 1.133  
norm 25 SiC 0.213 0.421 12.533 1.311  
norm 60 SiC 0.205 0.412 16.542 1.300  
norm 75 SiC 0.213 0.391 15.094 1.347  
 
nowa 5 SiL 0.126 0.457 14.805 1.349  
nowa 25 SiL 0.149 0.463 16.668 1.379  
nowa 60 SiCL 0.187 0.419 26.313 1.218  
nowa 75 C 0.236 0.391 13.260 1.362  
 
oilt 5 SL 0.188 0.455 56.132 1.570  
oilt 25 SiL 0.164 0.374 13.581 1.296  
oilt 60 L 0.143 0.409 934.606 1.181  
oilt 75 x x x x x  
 
okem 5 L 0.196 0.468 13.267 2.133  
okem 25 L 0.150 0.445 462.370 1.185  
okem 60 x x x x x  
okem 75 x x x x x  
 
paul 5 SiL 0.196 0.529 12.318 1.996  
paul 25 SiL 0.208 0.472 10.279 1.816  
paul 60 SiL 0.196 0.445 10.589 1.551  
paul 75 x x x x x  
 
perk 5 L 0.190 0.452 30.602 1.767  
perk 25 L 0.191 0.438 28.672 1.701  
perk 60 L 0.196 0.422 74.670 1.303  
perk 75 L 0.177 0.405 125.457 1.290  
 
pryo2 5 SiL 0.140 0.467 8.341 1.513  
pryo2 25 SiL 0.089 0.453 17.717 1.269  
pryo2 60 x x x x x  
pryo2 75 x x x x x  
 
putn 5 L 0.201 0.442 14.543 1.932  
putn 25 SiL 0.161 0.455 256.148 1.230  
putn 60 SiL 0.232 0.431 8.604 2.022  
putn 75 CL 0.238 0.431 11.954 1.836  
 
redr 5 L 0.000 0.525 770.016 1.109  
redr 25 C 0.197 0.452 120.224 1.165  
redr 60 C 0.193 0.411 38.766 1.150  
redr 75 x x x x x  
 
shaw 5 SiL 0.199 0.472 9.727 1.827  
shaw 25 SiL 0.193 0.418 7.164 1.846  
shaw 60 SiCL 0.144 0.390 20.915 1.175  
shaw 75 x x x x x  
 
skia 5 SL 0.153 0.660 346.353 1.417  
skia 25 SL 0.165 0.435 861.890 1.308  
skia 60 SCL 0.197 0.420 1057.671 1.189  
skia 75 SL 0.177 0.312 88.866 1.600  
 
stil 5 SiCL 0.000 0.560 28.148 1.132  
stil 25 L 0.217 0.456 19.104 1.636  
stil 60 L 0.235 0.451 14.885 1.787  
stil 75 L-CL 0.234 0.435 11.296 1.894  
 
stua 5 LS 0.160 0.407 106.945 1.646  
stua 25 SL 0.166 0.417 104.341 1.572  
stua 60 LS 0.165 0.377 254.532 1.530  
stua 75 SL 0.185 0.393 332.329 1.476  
 
vini 5 SiL 0.184 0.495 12.981 1.668  
vini 25 SiL 0.149 0.459 27.453 1.347  
vini 60 SiCL 0.191 0.435 20.909 1.290  
vini 75 x x x x x  
 
wyno 5 SiL 0.178 0.419 19.283 1.385  
wyno 25 CL 0.230 0.439 11.954 1.643  
wyno 60 x x x x x  
wyno 75 x x x x x  
 
- - - - - - - -
 
Sites with estimated bulk density:
 
Site Depth Texture WCr WCs - n  
  cm * mm/mm mm/mm 1/bar  
 
 
altu 5 CL 0.233 0.454 37.179 1.343  
altu 25 CL 0.267 0.455 14.652 1.729  
altu 60 CL 0.246 0.431 25.271 1.389  
altu 75 CL 0.271 0.431 13.179 1.723  
 
alv2 5 CL 0.236 0.455 44.890 1.334  
alv2 25 CL 0.237 0.455 78.598 1.216  
alv2 60 x x x x x  
alv2 75 x x x x x  
 
antl 5 LS 0.142 0.407 3103.902 1.260  
antl 25 LS 0.153 0.407 1339.638 1.338  
antl 60 LS 0.118 0.383 16491.185 1.152  
antl 75 LS 0.110 0.383 32782.609 1.135  
 
ardm 5 L 0.209 0.440 29.441 1.488  
ardm 25 C 0.181 0.490 55.583 1.151  
ardm 60 C 0.228 0.466 25.965 1.230  
ardm 75 C 0.023 0.466 68.781 1.079  
 
arne 5 SL 0.188 0.419 25.859 1.785  
arne 25 L 0.212 0.442 18.226 1.822  
arne 60 L 0.188 0.417 44.093 1.397  
arne 75 L 0.210 0.416 34.930 1.570  
 
bbow 5 L 0.046 0.442 74.192 1.159  
bbow 25 L 0.033 0.442 48.599 1.151  
bbow 60 SiL 0.025 0.430 24.297 1.139  
bbow 75 SiL 0.022 0.431 17.783 1.132  
 
beav 5 L 0.209 0.442 25.547 1.796  
beav 25 CL 0.246 0.455 16.076 1.803  
beav 60 L 0.182 0.419 4026.084 1.161  
beav 75 L 0.208 0.417 134.004 1.343  
 
bess 5 SiL 0.169 0.445 11.746 1.608  
bess 25 SiL 0.157 0.24 24.598 1.556  
bess 60 x x x x x  
bess 75 x x x x x  
 
bois 5 CL 0.219 0.455 138.507 1.234  
bois 25 CL 0.130 0.455 171.663 1.117  
bois 60 SiCL 0.206 0.443 12.850 1.285  
bois 75 SiCL 0.198 0.443 12.989 1.288  
 
bris 5 SL 0.163 0.418 68.043 1.612  
bris 25 SL 0.178 0.419 41.487 1.654  
bris 60 L 0.142 0.419 66.595 1.272  
bris 75 x x x x x  
 
buff 5 L 0.191 0.443 20.017 1.871  
buff 25 SiL 0.218 0.455 10.392 2.032  
buff 60 CL 0.235 0.431 11.563 1.797  
buff 75 CL 0.245 0.431 11.564 1.762  
 
burn 5 LS 0.165 0.399 95.350 1.782  
burn 25 SL 0.171 0.412 76.916 1.809  
burn 60 LS 0.167 0.378 60.599 1.852  
burn 75 LS 0.166 0.376 76.211 1.881  
 
butl 5 SiL 0.165 0.445 79.614 1.217  
butl 25 SiCL 0.221 0.467 11.344 1.399  
butl 60 SiCL 0.215 0.443 7.245 1.326  
butl 75 SiCL 0.191 0.443 19.555 1.278  
 
cama 5 L 0.177 0.441 21.78 1.87  
cama 25 SL 0.172 0.419 20.127 1.875  
cama 60 x x x x x  
cama 75 x x x x x  
 
cent 5 SL 0.181 0.419 31.996 1.706  
cent 25 L 0.151 0.443 452.779 1.213  
cent 60 CL 0.184 0.431 377.203 1.162  
cent 75 CL 0.192 0.431 86.417 1.249  
 
cher 5 L 0.186 0.443 16.449 1.891  
cher 25 L 0.148 0.443 1293.875 1.209  
cher 60 L 0.183 0.419 25.094 1.757  
cher 75 L 0.181 0.419 16.856 1.800  
 
chey 5 SL 0.201 0.419 38.474 1.643  
chey 25 SL 0.191 0.419 30.910 1.785  
chey 60 L 0.169 0.419 88.710 1.224  
chey 75 L 0.192 0.419 55.930 1.312  
 
clou 5 SiL 0.141 0.447 21.917 1.528  
clou 25 SiL 0.171 0.452 20.902 1.461  
clou 60 x x x x x  
clou 75 x x x x x  
 
cook 5 SiL 0 0.44 11.974 1.255  
cook 25 x x x x x  
cook 60 x x x x x  
cook 75 x x x x x  
 
dura 5 SL 0.189 0.418 46.070 1.564  
dura 25 CL 0.177 0.455 729.180 1.174  
dura 60 C 0.263 0.467 46.929 1.303  
dura 75 C 0.251 0.467 100.797 1.225  
 
eric 5 LS 0.176 0.407 47.527 2.139  
eric 25 LS 0.177 0.406 52.604 2.055  
eric 60 SL 0.180 0.395 50.492 1.781  
eric 75 SL 0.191 0.395 31.379 1.861  
 
eufa 5 L 0.178 0.443 13.689 2.054  
eufa 25 L 0.234 0.442 10.314 2.082  
eufa 60 C 0.240 0.467 29.250 1.328  
eufa 75 SiC 0.000 0.455 3.039 1.180  
 
fora 5 SL 0.194 0.419 42.915 1.594  
fora 25 SL 0.202 0.419 60.114 1.542  
fora 60 SCL 0.223 0.429 38.309 1.709  
fora 75 SCL 0.193 0.428 616.921 1.280  
 
free 5 SiL 0 0.455 159.885 1.131  
free 25 SiL 0 0.455 127.882 1.129  
free 60 x x x x x  
free 75 x x x x x  
 
good 5 L 0.210 0.441 48.048 1.426  
good 25 L 0.212 0.443 47.060 1.372  
good 60 CL 0.199 0.431 49.771 1.279  
good 75 CL 0.206 0.431 498.650 1.170  
 
gra2 5 CL 0.233 0.455 12.079 1.584  
gra2 25 SiCL 0.000 0.467 79.299 1.093  
gra2 60 x x x x x  
gra2 75 x x x x x  
 
hoba 5 SiCL 0.000 0.467 7.723 2.022  
hoba 25 SiC 0.237 0.479 8.416 1.282  
hoba 60 SiC 0.195 0.455 83.300 1.641  
hoba 75 SiC 0.175 0.455 46.705 1.175  
 
holl 5 SiC 0.000 0.478 219.279 1.076  
holl 25 C 0.207 0.490 123.181 1.146  
holl 60 SiC 0.196 0.454 15.380 1.190  
holl 75 C 0.006 0.464 181.149 1.057  
 
hook 5 L 0.202 0.443 20.973 1.742  
hook 25 L 0.196 0.443 48.54 1.422  
hook 60 x x x x x  
hook 75 x x x x x  
 
hugo 5 L 0.179 0.442 17.74 1.792  
hugo 25 L 0.125 0.441 197.674 1.18  
hugo 60 x x x x x  
hugo 75 x x x x x  
 
idab 5 SiL 0.2 0.455 4.225 1.758  
idab 25 SiCL 0.228 0.467 6.676 1.38  
idab 60 x x x x x  
idab 75 x x x x x  
 
jayx 5 SiL 0.164 0.455 6.017 1.591  
jayx 25 SiL 0.135 0.454 5.935 1.432  
jayx 60 x x x x x  
jayx 75 x x x x x  
 
kent 5 L 0.105 0.436 1489.671 1.139  
kent 25 L 0.168 0.436 1337.706 1.151  
kent 60 x x x x x  
kent 75 x x x x x  
 
lane 5 SL 0.158 0.419 47.399 1.555  
lane 25 SL 0.163 0.419 26.217 1.575  
lane 60 SL 0.157 0.393 30.101 1.514  
lane 75 L 0.176 0.419 29.672 1.514  
 
mang 5 S 0.162 0.406 48.471 3.563  
mang 25 S 0.168 0.406 41.177 3.767  
mang 60 SCL 0.189 0.431 3527.601 1.215  
mang 75 C 0.247 0.467 79.726 1.277  
 
mayr 5 SL 0.195 0.419 25.861 1.774  
mayr 25 L 0.233 0.443 19.910 1.877  
mayr 60 SiCL 0.207 0.431 28.542 1.247  
mayr 75 CL 0.207 0.431 21.332 1.282  
 
mcal 5 LS 0.155 0.407 90.776 1.592  
mcal 25 SCL 0.237 0.455 60.887 1.529  
mcal 60 x x x x x  
mcal 75 x x x x x  
 
medi2 5 SL 0.127 0.415 130.341 1.248  
medi2 25 SiL 0.157 0.240 24.598 1.556  
medi2 60 x x x x x  
medi2 75 x x x x x  
 
minc 5 SiL 0.208 0.453 8.71 1.902  
minc 25 SiL 0.199 0.455 7.73 1.78  
minc 60 x x x x x  
minc 75 x x x x x  
 
okmu 5 L 0.185 0.441 13.781 1.926  
okmu 25 SiL 0.188 0.451 11.29 1.701  
okmu 60 x x x x x  
okmu 75 x x x x x  
 
pawn 5 SiCL 0.147 0.467 69.942 1.159  
pawn 25 SiCL 0.184 0.464 41.754 1.188  
pawn 60 CL 0.000 0.428 95.936 1.091  
pawn 75 SiCL 0.000 0.442 34.878 1.087  
 
port 5 SL 0.164 0.417 62.253 1.508  
port 25 SL 0.179 0.418 57.974 1.538  
port 60 SL 0.181 0.394 80.581 1.44  
port 75 x x x x x  
 
pres 5 SL 0.190 0.418 30.602 1.767  
pres 25 SL 0.194 0.418 29.572 1.914  
pres 60 SL 0.184 0.391 74.609 1.704  
pres 75 SL 0.177 0.391 88.643 1.731  
 
ring 5 SL 0.172 0.418 32.25 1.888  
ring 25 SCL 0.201 0.455 115.926 1.341  
ring 60 x x x x x  
ring 75 x x x x x  
 
seil 5 L 0.209 0.442 20.924 1.795  
seil 25 L 0.214 0.443 31.382 1.601  
seil 60 SL 0.199 0.395 32.104 1.779  
seil 75 SL 0.199 0.394 31.228 1.710  
 
skia2 60 SL 0.207 0.395 47.771 1.787
   
slap 5 S 0.171 0.407 322.69 1.758  
slap 25 SL 0.172 0.419 803.637 1.203  
slap 60 x x x x x  
slap 75 x x x x x  
 
spen 5 SL 0.180 0.416 100.928 1.736  
spen 25 SCL 0.222 0.451 863.802 1.334  
spen 60 x x x x x  
spen 75 x x x x x  
 
stig 5 SiL 0.165 0.455 7.831 1.857  
stig 25 SiL 0.173 0.455 8.199 1.836  
stig 60 SiL 0.164 0.431 8.621 1.638  
stig 75 x x x x x  
 
tahl 5 SiL 0.163 0.436 8.516 1.862  
tahl 25 x x x x x  
tahl 60 x x x x x  
tahl 75 x x x x x  
 
tali 5 C 0.164 0.491 67.008 1.098  
tali 25 C 0.154 0.49 59.135 1.097  
tali 60 x x x x x  
tali 75 x x x x x  
 
tipt 5 SL 0.195 0.418 25.562 1.878  
tipt 25 SL 0.194 0.418 25.217 1.888  
tipt 60 SL 0.199 0.394 36.384 1.747  
tipt 75 SL 0.186 0.395 1675.771 1.279  
 
tish 5 SiL 0.127 0.455 18.808 1.408  
tish 25 SiL 0.143 0.455 20.443 1.346  
tish 60 x x x x x  
tish 75 x x x x x  
 
vano 5 SL 0.198 0.418 30.494 1.808  
vano 25 L 0.183 0.443 636.554 1.212  
vano 60 C 0.261 0.467 92.159 1.25  
vano 75 C 0.217 0.466 1350.125 1.136  
 
walt 5 SiL 0.221 0.455 6.260 2.244  
walt 25 C 0.205 0.491 54.623 1.184  
walt 60 C 0.183 0.467 121.985 1.150  
walt 75 CL 0.201 0.430 41.815 1.250  
 
wash 5 L 0.189 0.443 31.385 1.624  
wash 25 SCL 0.23 0.454 36.432 1.764  
wash 60 SL 0.196 0.395 36.608 1.729  
wash 75 x x x x x  
 
wato 5 L 0.195 0.443 18.797 1.802  
wato 25 L 0.213 0.443 14.489 1.860  
wato 60 L 0.141 0.419 37.727 1.231  
wato 75 L 0.147 0.419 68.541 1.208  
 
waur 5 SL 0.184 0.419 81.654 1.712  
waur 25 SL 0.194 0.419 107.525 1.617  
waur 60 SL 0.195 0.395 83.300 1.641  
waur 75 SL 0.191 0.395 89.635 1.605  
 
weat 5 SiL 0.176 0.446 14.607 1.867  
weat 25 L 0.183 0.438 18.742 1.74  
weat 60 x x x x x  
weat 75 x x x x x  
 
west 5 SiL 0.000 0.449 53.026 1.158  
west 25 SiCL 0.160 0.462 13.238 1.269  
west 60 L 0.052 0.429 39.546 1.152  
west 75 x x x x x  
 
wilb 5 SiL 0.105 0.453 22.208 1.297  
wilb 25 SiL 0.138 0.45 25.27 1.358  
wilb 60 x x x x x  
wilb 75 x x x x x  
 
wist 5 SiL 0.193 0.454 10.206 1.591  
wist 25 SiL 0.198 0.454 10.593 1.544  
wist 60 SiL 0.232 0.424 8.604 2.022  
wist 75 SiC 0.127 0.454 26.026 1.147  
 
wood 5 SL 0.194 0.419 27.379 1.907  
wood 25 L 0.203 0.443 33.506 1.751  
wood 60 L 0.142 0.419 1016.867 1.185  
wood 75 L 0.145 0.419 1310.176 1.185  
 
 

Note:

Textual Classifications Codes:

C clay  
CL clay loam  
L loam  
LS loamy sandy  
S sand  
SC sandy clay  
SL sandy loam  
SCL sandy clay loam  
Si silt  
SiL silt loam  
SiC silty clay  
SiCL sitly clay loam  

 

Appendix B: Diagram and description of sensor operation


The sensor is a Model 229L Matric Potential Sensor, manufactured by Campbell Scientific, Inc.
of Logan, Utah. The sensor is designed to provide estimates of matric (or soil-water) potential,
and operates on a heat dissipation principle.

The sensor consists of a ceramic matrix, into which a hypodermic needle has been inserted.
Inside the hypodermic needle are a thermocouple junction and a resistance heater. A rigid plastic
body encases the body of the hypodermic needle and firmly attaches the ceramic matrix, the
thermocouple and heater wiring. A sketch of the 229L sensor is shown below.

The process by which temperature and estimates of soil-water potential and soil-water content
are calculated consists of several steps:

  1. Measure the initial sensor/soil temperature (ST) with the thermocouple
  2. Introduce a heat pulse into the sensor by running an electrical current through the
    resistance heater
  3. Turn off the heating current and measure the sensor temperature (FT)
  4. Calculate the sensor temperature rise (DeltaT) by subtracting the initial temperature, before
    heating (ST), from the temperature after heating (FT)
  5. Convert the sensor temperature rise to that of a "reference" sensor (TR)
  6. Estimate the soil-water potential (MP) as a function of "reference" sensor response (TR)
    using the sensor calibration function
  7. Estimate the soil-water content (WC) as a function of soil-water potential (MP) using a soil-
    water retention curve.