Scott revised proposoal - Soil Physics

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Revised
Proposal for Matla
b Tool, Soil 6110

Bethany Scott

Friday, September
3
0, 2011


Many
research efforts in

hydrometeorology, hydroclimatology, surface and vadose

zone
hydrology utilized the unparalleled environmental monitoring systems of the Oklahoma Mesonet
. These
projects frequently
model the spatial and temporal variability of soil moisture

(
Lakhanka
r et al., 2010
)
.
The Oklaho
ma Mesonet consists of 120 sites, with at least one site in each county in Oklahoma
(
Illston
et al., 2008
)
. The Campbell Scientific 229
-
L
heat
-
dissipation
sensor is installed at most sites at
5, 25, 60
and 7
5

cm depths wi
th readings taken every thirty minutes.
The 75 cm is currently scheduled for
termination;

however archived data will be remain available.

The output of the sensors,

T
sensor
, is
the
measured temperature difference after a heat pulse
is
introduced
(
Basara
and Crawford, 2000
)
.
The sensor output is normalized to remove sensor to sensor
variability based on sensor specific properties. The normalized sensor output is available for download
through the Oklahoma Mesonet website,
Mesonet.org
, as

T
ref

(
Illston et al., 2008
)
.


The


T
ref

data is
converted to the Fractional Water Index
(FWI)
as described by
the primary reference for this study,
Illston
(
2008
)
. Fractional water index can be a useful tool in mesoscale weather monitoring. However, it
can be difficult to interpret for land managers and
less accurate

than variable
s

such as soil moisture
,

which require knowledge of the site specific soil pr
operties.

To improve the ease of use
as well as the accuracy
of the Oklahoma Mesonet sensor output, the
Matlab tool

MesoTheta

will be developed. The
proposed
tool will calculate

M
e
sonet soil m
oisture and
plant available w
ater

from the sensor output.

Cu
rrently,
t
he
sensor data from the Mesonet stations is available to the public as a normalized
senso
r output,


t
s
. The sensor data
is

then converted to matric potential
(MP)
via Eq. (1)



















(1)

where MP= matric potential,

T
ref
is the sensor output and
c

and
a

are calibration constants
.
The
matric
potential
is

converted to soil moisture based on
water

retention curve
parameters

estimated from
particle
-
size distribution and bulk density using the Arya

and Paris
(
1981
)

method which does not take
into account soil structure. Methods failing to account for structure can lead t
o significant error in
medium and fine texture soils in which the WRC is highly influenced by soil structure
(
Hillel, 2004
)
.

MesoTheta


will also calculate MP using Eq. (1)
However, it
will

then be converted to soil
moisture

using the van Genuchten equation
, Eq. (2)
(
Illston et
al., 2008
)
.















[










]
(




)





(2)

where
θ

is the volumetric water content,
θ
r

is the residual water content,
θ
s

is saturated water content,
and α

and
n

are empirical constants
. These
variables

will be
provided by
a
soil physical and hydraulic
property
database of the Mesonet station
s

which is
currently under development.
The artificial neural
network Rosetta
(
Schaap et al., 2001
)

will
be replacing
the Arya and Paris
(
1981
)

method for estimation
of the param
eters. Preliminary results of the database show an increase in accuracy
in many predicted
variables
.

The plant available water (PAW)
will

then be found by via Eq. (3) (Ochsner, personal
correspondence).




(





)















(3)

where
θ
i

is the current water content of layer
i

,
θ
wpi
is the permanent wilting point for layer
i,
dz
i

is the
thickness of layer
i,
and
n

is the number of layers
.


The development of

MesoTheta
will allow Mesonet

soil moisture sensor data to be more
readily

understood by land managers and will
allow greater interpretation of the data by researchers
.
Users will
be able to down
load the

t
s

data from the Mesonet website

for processing by
MesoTheta

for

any
numbe
r of sites, from 1 to all 120.
The calculation output will be in numerical table format.

References

Arya, L.M., and J.F. Paris. 1981. A PHYSICOEMPIRICAL MODEL TO PREDICT THE SOIL
-
MOISTURE
CHARACTERISTIC FROM PARTICLE
-
SIZE DISTRIBUTION AN
D BULK
-
DENSITY DATA. (in English) Soil
Science Society of America Journal 45:1023
-
1030.

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.

Hillel, D. 2004. Introduction to environmental soil physics Elsevier Academic Press, Amsterdam ; Boston.

Illston, B.G., J.B. Basara, C.A. Fiebrich, K.C. Crawford, E. Hunt, D.K. Fisher, R. Elliott, and K. Humes. 2008.
Mesoscale monitoring of soil mois
ture across a statewide network. (in English) Journal of
Atmospheric and Oceanic Technology 25:167
-
182.

Lakhankar, T., A.S. Jones, C.L. Combs, M. Sengupta, T.H.v. Haar, and R. Khanbilvardi. 2010. Analysis of
large scale spatial variability of soil moisture

using a geostatistical method. (in English) Sensors
10:913
-
932.

Schaap, M.G., F.J. Leij, and M.T. van Genuchten. 2001. ROSETTA: a computer program for estimating soil
hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251
:163
-
176.