ABSTRACT
Soil moisture is a critical component of many regional and global
climate studies. Unfortunately, long-term, spatially distributed soil moisture
data sets are rare because conventional soil moisture instrumentation is
point based and procedurally intensive. Electrical Resistance Sensors (ERS),
however, are capable of collecting nearly continuous data using data-loggers
with little maintenance and virtually no supervision. In this experiment,
hourly ERS and intermittent Time Domain Reflectometer (TDR) measurements
taken concurrently in 1990 and 1991 were used to develop a soil moisture
data set for sensors located in the Walnut Gulch Experimental Watershed.
Ultimately, these calibrations yielded a 16 month, hourly data set for sensors
buried in six different trenches across the Lucky Hills subwatershed at
5, 15 and 30 cm depths. These data were then used to validate the one-dimensional
Simultaneous Heat and Water (SHAW) model which simulates heat and water
movement through plant cover, snow, residue and soil. Robust spatial and
temporal characteristics of these data should provide important insights
into rangeland management issues. |
BACKGROUND
Soil moisture is a basic link between the hydrologic cycle
and the energy budget of land surfaces. Therefore, soil moisture data sets
are useful in many disciplines including agriculture, forest ecology, civil
engineering, water resources, meteorology and soil science. In semiarid
rangelands, soil moisture is a major control of evapotranspiration and a
significant factor in runoff generation. Consequently, soil moisture is
one primary component of many hydrologic models.
Unfortunately, long term soil moisture data sets are rare
because conventional soil moisture measurement techniques (e.g., gravimetric
and Time- Domain- Reflectometry) have historically been procedurally and
labor intensive. Electrical Resistance Sensors, however, are capable of
collecting nearly continuous data with little maintenance using data-loggers.
Like most soil moisture instruments, ERS require a calibration to convert
a measured signal to a volumetric water content. In this experiment, we
calibrated a network of in situ ERS sensors in the Walnut Gulch Experimental
Watershed to create a long term, temporally continuous soil moisture data
set.
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RESEARCH OBJECTIVES
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 METHODS
AND RESULTS
Eighteen pairs of ERS and TDR probes were installed
horizontally into trench faces under three bare and three shrub covered
surfaces in the Lucky Hills subwatershed at 5, 15 and 30 cm depths. Data
loggers recorded hourly ERS values and Agricultural Research Service scientists
collected TDR samples at varying time intervals. ERS readings were stored
as a series of resistances (ohms) while TDR values were recorded as Volumetric
Water Content ( ) values (m3m-3). |
 ERS and TDR measurements taken at identical times between August
of 1990 and December of 1991 were extracted for individual trenches and
depths. Calibration parameters for each ERS sensor were derived using the
expression TDR = a ERSb, where a and b are calibrated parameters. Data presented
in Figure 1 show a representative calibration curve with matched TDR and
ERS values (R2= 0.88).
Parameters a and b were optimized with non-linear curve
fitting techniques from a statistical software package. Once acceptable
parameters were calculated for each sensor, the calibration expression was
applied to the entire hourly ERS data set to produce 16 month, hourly . Coefficient of determination (R2) values for all 18 sensors ranged
from 0.33 to 0.88. Statistical tests were also completed for the "b"
parameter to ensure that a significant calibration relationship was established. |
SHAW MODEL
The Simultaneous Heat and Water (SHAW) model is a detailed
process model which simulates heat and water movement through a plant-residue-soil
system. A vertical, one dimensional profile extending from the vegetation
canopy to a specified depth within the soil is represented in this model.
At various points in this vertical profile, nodes represent various canopy
and soil layers. For each of these nodes, interrelated water, water vapor,
heat and solute fluxes can be calculated. As demonstrated in a 1990 experiment
in Walnut Gulch, the SHAW model was capable of simulating multi-species
plant canopies; variable heat and water fluxes at grass and shrub dominated
sites were simulated well (SHAW Model, Gerald Flerchinger, 1996).
In this experiment, we tested the ability of the SHAW
model to simulate the calibrated ERS sensors at 5, 15 and 30 cm depths under
bare and shrub covered surfaces between 1990 and 1991 (DOY 229-210). Preliminary
results of this analysis are given in Table 2.
Mean SHAW model and observed ERS values are shown with their respective R2 values. Figure 2 shows
the SHAW model simulations and ERS estimates of
at 5 and 15 cm depths.
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CONCLUSIONS
These findings indicate that an in-situ calibration
of ERS sensors that integrated high frequency ERS data with reliable TDR
data worked well. An hourly soil moisture data set for a 16 month period
was composed for 6 replicate sites at 3 different depths. This simple calibration
procedure also demonstrates the utility of the ERS sensor for future soil
moisture studies. Overall, the SHAW model provided a reasonable simulation
of the calibrated ERS sensors. Future studies may focus on the calibration
of the model to the ERS data sets. |
ACKNOWLEDGMENTS
The authors would like to acknowledge Leslie Bach, Saud Amer, Bill Kustas,
Gerald Flerchinger, the Water Conservation Laboratory (Phoenix, AZ) and
the Tombstone field office staff for their efforts. This research was funded,
in part, by an EOS grant (NASA NAGW-2425), a grant from the Landsat Science
Team (NASA S-41396-F) and the Monsoon '90 project (IDP-88-086). Carl Unkrich,
Scott Miller and Gary Richardson deserve thanks for their patience.
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