1. Statistical Tests and Results
Determining a Relationship between Soil Moisture and Maximum
Temperatures
Parker Malek, Atmospheric Sciences, Dr. Abigail Swann, Atmospheric Sciences, Biology
Motivation
Soil moisture is a variable that is difficult to quantify due to its large spatial heterogeneity,
its lack of extensive ground-based measurement networks, and its strong dependence on
many meteorological and biological factors. However, records of soil moisture are essential
for developing more accurate climate models and providing constraints on processes
controlling plant productivity and heat fluxes. My research aims to further develop these
records by determining whether maximum temperatures can be related to the amount of
soil moisture in a particular region. We hypothesize that the variability of maximum
temperatures in vegetated regions is determined by the amount of soil water available
there. By analyzing latent heat, temperature, and soil moisture data collected from
various flux measurement towers around the United States, we show that days with
relatively low latent heat flux values occurring at high maximum temperatures have
statistically drier soils compared to days where high latent heat flux values occur at high
maximum temperatures.
1.) S. Seneviratne, T. Corti, E. Davin, M. Hirschi, E. Jaeger, I. Lehner, B. Orlowsky, and A. Teuling. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3):125–161, 2010.
2.) Bonfils, C., A. Angert,C. C. Henning, S. Biraud, S. C. Doney, and I. Fung (2005), Extending the record of photosynthetic activity in the eastern United States into the presatellite period using surface diurnal temperature range, Geophys. Res. Lett., 32, L08405, doi:10.1029/ 2005GL022583.
3.) http://ameriflux.ornl.gov/
Why Use Latent Heat?
• Previous studies have shown that plant transpiration alone accounts for more than 80%
of evapotranspiration (Seneviratne et al. 2010).1
• This assumption allows us to pair soil moisture with a variable that is more consistently
measured and a known control of temperature.
Fig 1: Simple visualization of stomatal
conductance, the primary mechanism for plant
transpiration.
Locations
• Three sites chosen based on the abundance and type of vegetation, the availability of
water in the region, and the availability of data at each site.
• Large evergreen needleleaf forests
• Data is recorded through various eddy covariance techniques and is gathered from
Ameriflux, a flux measurement network.3
Temperature Data
• Temperature data organized into time series consisting of daily
maximums.
• Created time series of maximum temperatures selected every 5
days to reduce masking of vegetation from clouds (Bonfils et
al. 2005).2
Fig 3: Map of the
western United States
with labels pinpointing
the location of the
three sites being
analyzed. State names
and borders are
outlined in white.
Fig 4: Visualization of
method used to
determine 5 day
maximum temperatures.
Adding Latent Heat and Soil Moisture
• Time series of 5 day maximum temperatures is correlated with
coincident time series of average daytime latent heat flux
values and average volumetric soil water content values.
Fig 5-7: Correlation plots
of 5 day maximum
temperatures vs.
average daily latent
heat fluxes on the same
day. Colors correspond to
average volumetric soil
water content. Days
chosen for analysis are
limited by the growing
season (April 1st –
November 1st).
Fig 5: Niwot Ridge
Fig 6: Blodgett Forest
Fig 7: Wind River
Fig 8: Visualization of
the buffer regions used
for statistical tests
comparing maximum
temperatures to soil
moisture.
Unexpected
Expected
Expected
Conclusion
• Low latent heat flux values occurring at high maximum
temperatures have statistically drier soils compared to days
where high latent heat flux values occur at high maximum
temperatures.
• Hypothesis only works well in wet environments that are
both rarely limited by water or energy (see figure 2).
Fig 2: Definition of soil moisture regimes
and corresponding evapotranspiration
regimes. EF denotes evaporative fraction.1
Linear Regression Model 1
𝐿𝑎𝑡𝑒𝑛𝑡 𝐻𝑒𝑎𝑡 𝐹𝑙𝑢𝑥 = 𝛼𝑥1 + 𝛽𝑥2
Table 1: Mean
values of soil
water content
above and
below original
linear
regression line
with
associated p
value for each
site.
Results show that high latent heat fluxes occurring at high maximum
temperatures exhibit larger average soil water amounts for Niwot Ridge and
Blodgett Forest with the opposite being true for Wind River.
Linear Regression Model 2
𝐿𝑎𝑡𝑒𝑛𝑡 𝐻𝑒𝑎𝑡 𝐹𝑙𝑢𝑥 = 𝛾𝑥3
𝑥1 = 5 day maximum temperature 𝑥3 = soil water content𝑥2 = precipitation
Table 2: R2 values associated with the linear regressions of latent heat flux and its
corresponding residuals as a function of either 5 day maximum temperatures and
precipitation or soil moisture.
Linear regression models of latent heat flux as a function of maximum
temperature and precipitation are found to be statistically more effective at
capturing the latent heat flux variable when compared to linear regression
models of latent heat flux as a function of soil moisture alone.
Maximized signal from
vegetation expected
in transitional phase
SoilWaterContent(%)SoilWaterContent(%)SoilWaterContent(%)
Initial
Hypothesis: high
latent heat
fluxes occurring
at high
maximum
temperatures
are associated
with days where
the soil is wet
WET SOILS
DRY SOILS
DRY SOILS?
DRY SOILS
WET SOILS
WET SOILS?
Areas of dry and
wet soils less
distinguishable in
Wind River plot
Residual Calculation
𝐿𝐻𝐹𝑅 = 𝐿𝑎𝑡𝑒𝑛𝑡 𝐻𝑒𝑎𝑡 𝐹𝑙𝑢𝑥 𝑂𝑏𝑠 − 𝐿𝑎𝑡𝑒𝑛𝑡 𝐻𝑒𝑎𝑡 𝐹𝑙𝑢𝑥 (𝑀𝑜𝑑𝑒𝑙)
EF ~ Latent Heat