Soil water repellency (SWR) increases surface runoff and preferential
flows. Thus, quantitative evaluation of SWR distribution is necessary to
understand water movements. Because the variability of SWR distribution
makes it difficult to measure directly, we developed a method for
estimating an SWR distribution index, defined as the areal fraction of
surface soil showing SWR (SWRarea). The theoretical basis of the method
is as follows: (1) SWRarea is equivalent to the probability that a
position on the soil surface is drier than the critical water content
(CWC); SWR is present (droplets absorbed in >10 s) when the
soil surface is drier than the CWC and absent when it is wetter. (2) CWC
and soil moisture content (θ) are normally distributed independent
variables. (3) Thus, based on probability theory, the cumulative normal
distribution of θ – CWC (f(x)) can be obtained from the
distributions of CWC and θ, and f(0), the cumulative probability
that θ – CWC < 0, gives the SWRarea. To investigate whether
the method gives reasonable results, we repeatedly measured θ at 0–5 cm
depth and determined the water repellency of the soil surface at
multiple points in fixed plots with different soils and topography in a
humid-temperate forest. We then calculated the CWC from the observed
θ–SWR relationship at each point. We tested the normality of the CWC
and θ distributions and the correlation between CWC and θ. Then, we
determined f(x) from the CWC and θ distributions and estimated
the SWRarea on each measurement day. Although CWC and θ were both
normally distributed, in many cases they were correlated. Nevertheless,
the CWC–θ dependency had little effect on the estimation error, and
f(x) explained 69% of the SWRarea variability. Our findings show
that a stochastic approach is useful for estimating SWRarea.
Pyrene and nile red fluorescence probes for in-situ
study of polarity and viscosity of soil organic coatings implicated in soil water repellency
