Revised Proposal for Matlab Tool, Soil 6110
Bethany Scott
Friday, September 30, 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 (Lakhankar et al., 2010).
The Oklahoma 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 75 cm depths with 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, ∆Tsensor, 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 ∆Tref (Illston et al., 2008). The ∆Tref 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 variables such as soil moisture,
which require knowledge of the site specific soil properties.
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 Mesonet soil moisture and
plant available water from the sensor output.
Currently, the sensor data from the Mesonet stations is available to the public as a normalized
sensor output, ∆ts. The sensor data is then converted to matric potential (MP) via Eq. (1)
𝑀𝑃 = −𝑐 ∗ exp⁡(𝑎∆𝑇𝑟𝑒𝑓 )
(1)
where MP= matric potential, ∆Tref 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 to 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).
𝜃(−𝑀𝑃) = 𝜃𝑟 +
𝜃𝑠 +𝜃𝑟
1
[1+(−𝛼∗−𝑀𝑃)𝑛 ](1−𝑛)
(2)
where θ is the volumetric water content, θr is the residual water content, θs 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 stations 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 parameters. 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).
𝑃𝐴𝑊 = ∑𝑛𝑖=1(𝜃𝑖 − 𝜃𝑤𝑝𝑖 ) 𝑑𝑧𝑖
(3)
where θi is the current water content of layer i , θwpi is the permanent wilting point for layer i, dzi 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 download the ∆ts data from the Mesonet website for processing by MesoTheta for any
number 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 AND 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 moisture 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 251:163176.