3.1 Spatial-temporal patterns of soil moisture
The variogram analysis and histogram of soil moisture storage indicated
that interpolated soil moisture maps exhibited seasonal alignments of
soil moisture storage along topographic convergent areas (Fig. 3). We
found the sample variograms with a clear sill and nugget and observed
that the geostatistical structure of soil moisture was seasonally
evolved. During the wet winter period, high sills (15-25
(%)2) and low correlation lengths (20-30 m) were
observed, whereas during the dry summer periods, sills were smaller
(10-15 (%)2) and correlation lengths were longer
(30-40 m). Regardless of the wetness conditions, the wettest soil was
always located within the swales and the valley floor (i.e., near-stream
zone). These wet-up and dry-down patterns were consistent with the
overall distribution of the soil types and the topographic wetness index
within the catchment. There was an exponential increase in the
catchment-wide soil moisture variability with increased
averaged-catchment moisture contents (Zhao et al., 2012). These
conditions were obvious due to the well-drained and steep-sloped soils
within the catchment that confined saturated areas to the swales and the
valley floor.
The soil moisture variability was explained by using only the first few
EOF patterns within the Shale Hills (Table 1). At the soil depths, the
first four EOFs together explained approximately 87% of the total
variability, whereas only the first EOF (or EOF1) explained about 76%
of the total soil moisture variance, indicating that a single spatial
structure may explain much of the overall soil moisture pattern. With
increased soil depths, the total variations explained by the derived
EOFs also increased. These results indicated that the seemingly complex
patterns of soil moisture within the Shale Hills may largely be
explained by a very small number of the underlying spatial EOFs. In the
EOF analysis of spatial patterns, the impacts of temporally variable
factors, which do not affect the whole area uniformly, also resulted in
noise and would also be expected to have decreased the amount of the
variance explained by the significant EOFs.
A close examination of the EOF patterns associated with soil land units
in Figure 4 reveals that the EOF1 displayed high values within the
valley floor, and low values within the hillslopes, respectively.
Obviously, the high EOF values indicated the clustered site with the
above average soil moistures, and conversely low EOF values is
equivalent to the sites of below average soil moisture values (Fig. 4).
From the weighted EC series (Fig. 5), the variance explained by the EOF1
values closely followed the increased field mean moisture contents,
e.g., the variance is sharply increased with increased moisture contents
following rainfall recharge. Therefore, the EOF analyses seem to
represent a very powerful set of tools that helped explain the patterns
in the variance associated with the general spatial patterns, the
indications of positional characteristics, and the temporal dynamics.
Perry and Niemann (2007) applied an EOF analysis for a 10.5 ha
Tarrawarra grassland catchment, and the first EOF in their study
explained 55% of the soil moisture spatial variability. The explained
variances found at the Shale Hills are higher than the previously
mentioned studies that were about 55% to 70% of surface soil moisture
variability that may be explained by the stable spatial patterns
associated with the soil parameters and topography at their study sites
(Perry and Niemann, 2007; Korres et al., 2010). Because of the strong
combined soil-topographic effects, the observed soil moisture patterns
in the Shale Hills was high, and can largely be explained by only a few
underlying spatial structures or EOF patterns that are obviously
correlated to the various geophysical characteristics.