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www.elsevier.com/locate/geoderma
Geoderma 131 (2
Soil moisture patterns in a forested catchment:
A hydropedological perspective
H.S. LinT, W. Kogelmann, C. Walker, M.A. Bruns
Department of Crop and Soil Sciences, 116 ASI Building, The Pennsylvania State University, University Park, PA 16802, United States
Available online 23 May 2005
Abstract
To connect pedon and landscape scales of soil moisture, a key lies in the distribution of different soils over the landscape.
Mapping the fabric of soils over a watershed helps optimal sampling design and appropriate modeling of landscape hydrology.
Such a hydropedological perspective is examined in a 7.9-ha forested catchment in central Pennsylvania in order to understand
the spatio-temporal organization of soil moisture and its relationships with soil-landscape features. Soil moisture changes at four
depth intervals (0–0.06, 0.11–0.29, 0.51–0.69, and 0.91–1.09 m) were monitored at 30 sites in the catchment for over 3 months in
2004. In addition, a reconnaissance campaign was conducted at 189 points on ten days during a 2.5-month period in 2003. Soil
distribution and topographic metrics were correlated with the observed soil moisture patterns to reveal their relationships with soil
type, depth to bedrock, topographic wetness index, slope, precipitation, and stream discharge. Among the five soil series
identified in the catchment, the Ernest soil series (fine-loamy, mixed, superactive, mesic Aquic Fragiudults) remained the wettest
throughout the monitoring periods, which was consistent with its morphology and topographic position (e.g., many redox
features and a fragipan-like layer starting at 0.3–0.5 m depth). The Weikert soil series (loamy-skeletal,mixed, active, mesic Lithic
Dystrudepts) had the driest condition because of its shallow depth to bedrock and steep slopes. Cluster analysis based on soil
depth, topographic wetness index, and local slope showed that the 30 monitoring sites could be grouped into wet, moderately wet,
moderately dry, and dry locations that exhibited different spatio-temporal patterns of subsurface soil moisture. Such a grouping
correlated with the soil series plus local slopes. Because of complex interplays between soils and topography, the individual
contributions from soils and topography to the soil moisture grouping were hard to separate. Time series data showed a quick
stream flow response to precipitation forcing, indicating the rapid movement of water within the catchment into the stream
channel. A conceptual model of hillslope hydrology in this catchment was developed to elaborate the patterns of soil moisture
distribution along the hillslope and within soil profiles, their relations to the soil series, and four main flow pathways downslope
(i.e., subsurface macropore flow, subsurface lateral flow at A–B horizon interface, return flow at footslope and toeslope, and flow
at the soil–bedrock interface). This conceptualization enhances the understanding and modeling of preferential flow dynamics at
the small catchment scale, particularly with regard to the role of detailed soil mapping and lateral flow in hillslope hydrology.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Landscape hydrology; Spatio-temporal organization; Hydropedology; Soil map; Topographic wetness index
T Corresponding author. Tel.: +1 814 865 6726; fax: +1 814 863 7043.
0016-7061/$ - s
doi:10.1016/j.ge
E-mail addr
006) 345–368
ee front matter D 2005 Elsevier B.V. All rights reserved.
oderma.2005.03.013
ess: [email protected] (H.S. Lin).
H.S. Lin et al. / Geoderma 131 (2006) 345–368346
1. Introduction
Much effort by non-pedologists is hampered
because soil distribution and processes are not well
understood such that site selection for sampling or
monitoring and the design of modeling do not
represent actual distribution and processes. To con-
nect pedon and landscape phenomena, one of the
keys lies in the distribution of various soils over the
landscape (i.e., soil patterns). We normally monitor
pedons to collect point data and model landscapes
trying to understand areal distributions. The key
connecting the two is the mapping of various soils
and related landscape features. The fabric of soil
cover in the landscape, together with topography,
vegetation and geology, helps optimal sampling
design as well as appropriate modeling of landscape
hydrology. This bmap first, then designQ at the
landscape scale or blook first, then measureQ at the
local scale is important in monitoring spatio-temporal
dynamics of soil moisture over a landscape (Lin et
al., 2005a).
Hillslopes are fundamental landscape units.
Watersheds are comprised of sub-watersheds which
in turn are comprised of multiple hillslopes. Thus,
there is a need to connect point observations to
hillslope phenomena and then to whole catchment
response. The spatial variability of soil properties
(both horizontal and vertical) will improve the
understanding of hillslope hydrology. Yet the exist-
ing soil maps (such as the Soil Survey Geographic
Database or SSURGO in the U.S.)—often devel-
oped for general land-use planning—may not be
suited for more localized applications (such as
hillslope hydrology and precision agriculture). Some
recent case studies in catchment hydrology have
scrutinized soils data and their effects on the
representation of catchment response. For example,
Houser et al. (2000) reported that the addition of
spatially variable soil properties based on the Order
II soil map produced unrealistic polygon artifacts in
the simulated soil moisture patterns. This suggests
that caution should be exercised in distributed
hydrological modeling when allocating soil
hydraulic properties on the basis of soil types as
indicated by soil maps. The issues include the scale
of soil map used, variability within soil map units,
and discrete representation of a continuous phenom-
enon (e.g., Lin et al., 2005b). When used appropri-
ately with sufficiently detailed soil map, insights
regarding hydrological variability could be gained.
For example, Duffy et al. (1981) demonstrated that
when soil map was properly used at the right scale
it could help explain the spatial variability of soil
hydraulic properties. They measured quasi-steady
state infiltration rates on surface soils at 20
locations scattered throughout a 100-ha farm in
New Mexico. If the seven soil series on the farm
were ignored, there was basically no relation
between measured and estimated infiltration rates.
But when the infiltration rates were grouped by soil
series based on the Order I soil map, the measured
and estimated geometric mean values were highly
correlated.
The optimal use of existing soil maps is
generally not possible if map scale, within-map-unit
variability, and soil boundary uncertainty are not
well understood. There are five orders of soil survey
and mapping in the U.S., ranging from the Order I
for the most detailed mapping (minimum delineation
size V1 hectare, 1 :15,840 or larger cartographic
scale, mapping units mostly consociations of phases
of soil series) to the Order V for very general
mapping (minimum delineation size 252–4000
hectare, 1 :250,000 or smaller cartographic scale,
mapping units largely associations consisting of two
or more dissimilar components) (Soil Survey Divi-
sion Staff, 1993). The orders are intended to assist
the identification of operational procedures for the
conduct of a soil survey, and to indicate general
levels of quality control that affect the kind and
precision of subsequent interpretations and predic-
tions. However, soil surveys have traditionally
overlooked spatial variability within map units for
a variety of reasons, including scale limitations, lack
of appropriate sampling design, and inadequate
quantitative data (Lin et al., 2005b). Although
acknowledged, variation within soil map units is
generally described qualitatively in vague terms. In
addition, the question of how best to conceptualize
soils as discrete polygons or continuous entities is
unresolved (Burrough and McDonnell, 1998). With
growing utilization of digital soil maps and related
databases for diverse applications, the variability of
soil taxa and that of map units have become more
recognized. It has been suggested that the most
H.S. Lin et al. / Geoderma 131 (2006) 345–368 347
detailed Order I soil mapping would be in great
demand for site-specific applications such as pre-
cision agriculture and landscape hydrology (Lin et
al., 2005a).
Besides soils distribution, topographic attributes
are also useful indicators of hillslope and catchment
hydrological dynamics. Ridolfi et al. (2003) pointed
out that hillslope hydrology remains challenging
because a number of processes interact at different
scales, significantly contributing to the complexity of
the system that hampers the possibility of a general
theory. Some of the most important issues include
horizontal and vertical heterogeneity of soil types and
various soil properties, lateral redistribution of water
along the hillslope, and 3-D hillslope geometry and
the presence of spurs and hollows (Ridolfi et al.,
2003). Attempts to relate topographic variability to
soil properties and hillslope hydrology have been
numerous. A common belief regarding soil moisture
distribution over a hillslope is that topography
becomes increasingly important in wet periods, but
during dry periods soil moisture patterns depend
primarily on soil properties with little effect from
topography (e.g., Grayson and Bloschl, 2000). Partic-
ularly useful terrain attributes, which are now
routinely calculated from Digital Elevation Models
(DEM), include topographic wetness index (TWI),
slope, curvature, specific catchment area, relative
elevation, and others. High TWI areas in a catchment
tend to saturate first and therefore indicate potential
surface or subsurface contributing areas. The expan-
sion and contraction of such areas as a catchment wets
and dries is then indicated by the pattern of the TWI
under a steady-state assumption (Beven, 1997).
Various studies have attempted to correlate the TWI
with actual soil wetness or zones of surface saturation,
but the results vary widely (e.g., Yeh and Eltahir,
1998; Western et al., 1999; Sulebak et al., 2000).
Improvements to TWI include 1) incorporating soil
transmissivity at saturation, leading to soil-topo-
graphic index (Beven, 1986) and 2) considering
variable effective upslope contributing area instead
of a fixed value, leading to dynamic wetness index
(Beven and Freer, 2001).
Lin (2003) suggested hydropedology as an inter-
woven branch of soil science and hydrology that
encompasses multiscale basic and applied research of
interactive pedological and hydrological processes
and their properties in the unsaturated zone. Lin et
al. (2005a) proposed some hydropedological
approaches to enhance the study of landscape-soil-
water dynamics across scales, including 1) mapping,
monitoring, and modeling of landscape-soil-water
systems; 2) integrating geostatistical and geospatial
techniques into a Bayesian hierarchical multiscale
modeling framework; and 3) strategic spatial model-
ing and scaling. In this study, we examine the first
approach to improve the understanding of the spatio-
temporal organization of soil moisture in a forested
catchment. Relatively static properties of soil and
landscape features (such as topography and soil type)
could be mapped to assist in scaling and modeling of
landscape–soil–water dynamics, while more dynamic
properties (such as precipitation and soil moisture)
could be monitored to refine model predictions. Soil
mapping is about identifying soil–landscape patterns.
Once pattern is identified, it helps demystify soil
variability (Lin et al., 2005a). Several recent catch-
ment hydrology field investigations have demonstra-
ted how the understanding and modeling of
hydrological processes can be improved by the use
of observed spatial patterns (e.g., Grayson and
Bloschl, 2000). For example, analysis of remotely
sensed soil moisture patterns in the semi-arid Walnut
Gulch watershed in Arizona indicated that, following
a rainstorm, these patterns were organized but this
organization faded away after the storm, and the
pattern became random (Houser et al., 2000). Houser
et al. (2000) suggested that this change-over was a
reflection of the changing control on soil moisture
from the storm rainfall pattern to the pattern of soil
characteristics during the dry-down process. How-
ever, some spatial patterns of soil moisture are
temporally persistent (the notion of btime stabilityQ)(Vachaud et al., 1985). Evidence for time stability
has been recognized (e.g., Kachanoski and de Jong,
1988; Grayson and Western, 1998; Mohanty and
Skaggs, 2001). Time stability of spatial pattern may
be a function of spatial scale and may vary across a
landscape with different soil types, as shown by
Kachanoski and de Jong (1988) and by Zhang and
Berndtsson (1991). This implies that soil moisture
variability need to be analyzed in both space and
time.
Observed spatio-temporal patterns of pedologically
and hydrologically important variables are not very
H.S. Lin et al. / Geoderma 131 (2006) 345–368348
common (Grayson and Bloschl, 2000). But once
pattern information is obtained, it can be utilized in
many beneficial ways, such as: 1) designing exper-
imental setup and field data collection strategy; 2)
stratified interpolations/extrapolations of sparse point
data; 3) characterizing and modeling variability such
as spatial correlation and connectivity; 4) refining
model structure for enhanced modeling of landscape–
soil–water dynamics; 5) use in combination with time
series data to provide more realistic space–time
descriptions of soil moisture and other pedological
and hydrological phenomena; and 6) testing hypothe-
sized process models of the landscape behavior
(Grayson et al., 2002; Lin et al., 2005a).
bWhere, when, and howQ water moves through
various landscapes and how water flow impacts soil
processes and subsequently soil spatial patterns need
to be better understood. Conceptual and mathematical
models of water movement over the landscape are key
aspects of hydrological modeling, contaminant trans-
port, and terrestrial ecosystem predictions. However,
many current hydrological models do a poor job in
accurately predicting the relative amounts of subsur-
face lateral flow, baseflow, and surface runoff in total
streamflow (Wood, 1999). But sloping topography,
stratification, and soil layering all favor lateral flow
(Richardson et al., 2001). The convergence of surface
and subsurface lateral flow within a landscape results
in the formation and distribution of streams and rivers
and contributes to the spatial heterogeneity of soil and
vegetation across the landscape.
The objectives of this study are to characterize the
spatio-temporal patterns of surface and subsurface soil
moisture in a small forested catchment using a hydro-
pedological approach as described above, and to link
such soil moisture patterns to soil–landscape features.
In particular, this paper seeks to improve the method-
ologies for understanding the interactions between
soils and topography in determining hydrological
response in humid forest catchments. Little work has
been done on analyzing such interactions.We also hope
to develop an improved conceptual model of the
hillslope hydrology in this catchment through detailed
soil mapping and in situ soil morphological observa-
tions. An underlying hypothesis is that a sufficiently
detailed soil map is helpful in enhancing the under-
standing of spatial distribution and temporal dynamics
of soil moisture in a catchment.
2. Materials and methods
2.1. The catchment
AV-shaped forested catchment typical of the Ridge
and Valley Physiographic Province in central Penn-
sylvania (Fig. 1) was selected for this study. The 7.9-
ha catchment, located in Huntingdon County, PA is
characterized by steep slopes (up to 25%–48%) and
narrow ridges. There are four basic landforms in this
catchment: 1) north-facing slope with deciduous
forest and little underbrush, 2) south-facing slope
with deciduous forest and thicker underbrush, 3)
valley floor or floodplain of a first-order headwater
stream (a tributary of the Shavers Creek that reaches
the Juniata River and onto the Susquehanna River),
with evergreen trees on western section and deciduous
forest on eastern end, and 4) topographic depressional
areas (swales) containing deciduous forest and deeper
soils. The valley is oriented in an east–west direction
separating steep almost true north-facing and south-
facing slopes. Elevation of the area ranges from 256 m
at the outlet of the catchment to 310 m at the highest
ridge. The relatively uniform side slopes are periodi-
cally interrupted by seven distinct swales of varying
sizes on both sides of the stream that were mapped out
using a Trimble Pro XR Global Positioning System
(GPS) unit. Several species of maple, oak, and
hickory are typical deciduous trees found on the
sloping areas and on ridges, while the valley floor is
encompassed by eastern hemlock coniferous trees.
Mushrooms, an indicator of wet conditions, can be
found throughout the catchment during the wet
seasons, especially extensive in the valley floor
region.
An irrigation system was installed to apply
simulated rainfall to this catchment in the 1970s to
gain a better understanding of the effects of ante-
cedent soil moisture on controlling stormwater vol-
umes and timing (Lynch, 1976). In the mid-1990s this
catchment was revisited for the purpose of validating
a dynamical model for hillslopes and small catch-
ments developed by Duffy (1996). In both these
previous studies, a detailed soil map and vertical soil
heterogeneity were not considered. To support hydro-
pedological studies, we have chosen this catchment as
a field laboratory for developing robust datasets to
improve the understanding of fundamental processes
A) Soil Map (Order I)
B) Depth to Bedrock
C) Topographic Wetness Index
Transect
StudyArea
D) Slope
0 50 100 200Meters
0 50 100 200Meters
0 50 100 200Meters
0 50 100 200Meters
Stream
2 m Contours Berks
Blairton
Ernest
Rushtown
Weikert
Dry
Soil Moisture Cluster
High : 1.38 + m
Soil BoundariesMulti-Depth Monitoring Site
Low : 0.25 m
Bedrock Depth
Soil Type
Moderately Dry
Moderately Wet
Wet
High : 52%
Soil BoundariesMulti-Depth Monitoring Site
Low : 0%
Slope (%)
High : 19.42
Soil BoundariesMulti-Depth Monitoring Site
Low : 2.89
Wetness Index
Fig. 1. (A) The Order I soil map of the Shale Hills catchment and the 30 monitoring sites used in this study. (B) Map of depth to bedrock interpolated from 223 auger observations. (C)
Map of topographic wetness index calculated using the Eq. (1). (D) Slope map derived from the refined DEM.
H.S.Lin
etal./Geoderm
a131(2006)345–368
349
H.S. Lin et al. / Geoderma 131 (2006) 345–368350
of landscape–soil–water interactions and for testing
various hydropedological models.
2.2. Soil survey and landscape mapping
The soils of the Shale Hills were formed from
shale colluvium or residuum, with typical features
expected of a steeply sloping landscape. Soils on
the hillsides have a shallow depth to bedrock, while
the valley floor and depression areas have deeper
depth to the shale bedrock. Channerry shale frag-
ments (2–150 mm) are found throughout most soil
profiles (Table 1). Soils on the hillslopes generally
have silt loam texture, moderately developed soil
structure, high permeability, and hence are well
drained (Table 1). Redoximorphic features were
found in soils along the valley floor as a result of
seasonal soil saturation. The entire catchment is
covered by forest, thus all soils have an organic
layer (Oe horizon) approximately 0.05 m thick that
is comprised of decaying leaf litters and other
organic materials.
In cooperation with the USDA Natural Resources
Conservation Services personnel, a grid method
with transects was used to conduct a detailed Order
I soil survey (Soil Survey Division Staff, 1993),
supplemented by additional augering and Ground
Penetrating Radar (GPR) investigations. Soil map-
ping transects were spaced every 50 m and were
perpendicular to the bedrock’s southwest to north-
east orientation in order to capture the greatest
variability of soils. Soil samples were examined
every 25 m along each transect with augers. The
swale areas were sampled additionally across the
slope with samples taken every 2 m. Many addi-
tional locations were also sampled in order to refine
soil boundaries. Features recorded in all samples
included type of horizon, thickness of horizon,
depth to bedrock, color, texture by hand, rock
fragment content, structure, redoximorphic features,
and slope position. Eight representative soil pits and
many core samples were also investigated to
provide additional details on each of the soil series
identified in the catchment.
A total of five soil series were identified in the
catchment (Table 1 and Fig. 1). They were the
Weikert, Berks, Rushtown, Blairton, and Ernest series.
The Rushtown, Blairton, and Ernest series were the
closest established official soil series that matched
reasonably but not completely with the soils identified
in the Shale Hills. For the lack of new soil series
names and the need of communication convenience,
we have adopted these existing series names in this
study. Depth to bedrock and landscape positions were
the main criteria used to identify each of these soil
series in the field. The Weikert soils are shallow, with
depth to bedrock less than 0.5 m, while the Berks
series has 0.5–1 m depth to bedrock. If depth to
bedrock is over 1 m, we then used landscape position
to differentiate the Rushtown series (in swales) from
the Ernest and Blairton series (in valley floor).
Presence or absence of a fragipan-like layer and depth
to redoximorphic features were used to further
separate the Ernest series (with many redox features
and a fragipan-like layer starting at 0.3–0.5 m depth)
from the Blairton series (with few redox features
starting at 1.1 m depth).
All field observation locations were recorded using
a Trimble Pro XR GPS receiver and post-processed
using a base station to achieve optimal accuracy.
These locations were then imported into ArcGIS
(ESRI, Redlands, CA) for further processing. After
the field data were collected and the base maps
developed, initial soil boundaries were drawn in
ArcView GIS. A second phase of field checking was
then conducted to refine the soil boundaries before a
final soil map was developed (Fig. 1A). We would
like to point out that the existing SSURGO (Order II)
soil map for the study area was not suitable for our
study, because the existing general soil map not only
shows overly coarse map units, but also significant
error in the location of the stream floodplain and some
map unit boundaries.
Many landscape features in the catchment were
also mapped in this study (Fig. 1), including
landform (through field survey), topography (exiting
10-m DEM with improvements by local survey),
vegetation (interpreted from existing aerial photo-
graph with refinement from local survey), and depth
to bedrock (interpolated from field observations).
The existing 10-m DEM originally did not show the
swales well in the catchment. Thus, we conducted a
local survey of swales and ridges to refine the
elevation model. The field survey and data process-
ing involve GPS of swale boundaries and slope
breakpoints along the center of the swales, record-
Table 1
Basic characteristics of the five soil series identified in the Shale Hills catchment
Horizon Depth (m) Boundary Color
(moist,
matrix)
Redoximorphic
features
Concentrations Texturea Pedality
(structure)bPed surface
features
Roots Consistence
(moist)
Rock
fragments
(%)c
pH
Weikert Series (loamy-skeletal, mixed, active, mesic Lithic Dystrudepts) (78.69% area; located in backslopes, shoulders, and summits; b0.5 m depth to bedrock; well drained)
Oe 0–0.05 Abrupt smooth Very many fine
roots throughout
A 0.05–0.12 Clear smooth 7.5YR 3/2 (none) (none) Silt loam 2 f gr (none) Very many fine
roots throughout
Friable 0 4.5
Bw 0.12–0.24 Clear smooth 5YR 4/6 (none) (none) Silt loam 2 f sbk (none) Common fine
roots in cracks
Friable 60 4.5
CR 0.24–0.37 Clear smooth 5YR 4/6 (none) (none) Silt loam 1 f sbk (none) Few fine roots
in cracks
Friable 90 4.5
R 0.37+
Berks Series (loamy-skeletal, mixed, active, mesic Typic Dystrudepts) (9.81% area; located in both sides of swales and edges of valley floors; 0.5-1 m depth to bedrock; well drained)
Oe 0–0.05 Abrupt smooth Very many fine
roots throughout
A 0.05–0.08 Clear smooth 5YR 4/3 (none) (none) Silt loam 2 f gr (none) Very many fine
roots throughout
Friable 0 4.5
Bw1 0.08–0.14 Clear smooth 7.5YR 4/4 (none) (none) Silt loam 1 vf sbk (none) Many medium
roots throughout
Friable 2 4.5
Bw2 0.14–0.53 Clear smooth 7.5YR 4/6 (none) (none) Silt loam 1 f abk (none) Many medium
roots throughout
Friable 2 4.5
Bw3 0.53–0.69 Clear smooth 7.5YR 4/6 (none) (none) Silty clay
loam
1 f abk (none) Many medium
roots throughout
Friable 50 4.5
C 0.69–1.45 Abrupt smooth 5YR 4/6 (none) (none) Silty clay
loam
(massive) (none) Few fine roots
throughout
Friable 90 4.5
R 1.45+
Rushtown Series (loamy-skeletal over fragmental, mixed, mesic Typic Dystrochrepts) (6.34% area; located in bottom of swales and northeastern part of the catchment; N1 m depth to bedrock; N3 m
depth to redox features; well to moderately well drained)
Oe 0–0.05 Abrupt smooth Very many fine
roots throughout
A 0.05–0.11 Abrupt smooth 10YR 2/1 (none) (none) Silt loam 3 f gr (none) Very many fine
roots throughout
Friable 5 4.5
Bw1 0.11–0.17 Clear smooth 7.5YR 4/2 (none) (none) Silt loam 2 f sbk (none) Many fine roots
throughout
Friable 5 4.5
(continued on next page)
H.S.Lin
etal./Geoderm
a131(2006)345–368
351
Horizon Depth (m) Boundary Color
(moist,
matrix)
Redoximorphic
features
Concentrations Texturea Pedality
(structure)bPed surface
features
Roots Consistence
(moist)
Rock
fragments
(%)c
pH
Rushtown Series (loamy-skeletal over fragmental, mixed, mesic Typic Dystrochrepts) (6.34% area; located in bottom of swales and northeastern part of the catchment; N1 m depth to bedrock; N3 m
depth to redox features; well to moderately well drained)
Bw2 0.17–0.26 Clear smooth 7.5YR 4/4 (none) (none) Silt loam 1 m sbk (none) Common fine
roots throughout
Friable 5 4.5
Bw3 0.26–0.38 Clear smooth 5YR 4/6 (none) (none) Silty clay
loam
1 m sbk (none) Common
fine–coarse
roots throughout
Friable 5 4.5
BC 0.38–0.60 Clear smooth 5YR 4/6 (none) (none) Silty clay
loam
(massive) (none) Few medium
roots throughout
Friable 50 4.5
C 0.60–1.78+ 7.5YR 4/6 (none) (none) (massive) (none) Friable 80 4.5
Blairton Series (fine-loamy, mixed, active, mesic Aquic Hapludults) (0.22% area; located in a narrow band of eastern end of the valley floor; N1 m depth to bedrock; 1.1-m depth to redox features;
moderately well drained)
Oe 0–0.05 Abrupt smooth Very many fine
roots throughout
5.0
A 0.05–0.11 Clear smooth 7.5YR 3/2 (none) (none) Silt loam 3 m sbk
parting to
2 f gr
(none) Very many
fine–coarse
roots throughout
Friable b2 5.0
BA 0.11–0.18 Clear smooth 5YR 4/4 (none) (none) Loam 2 f sbk (none) Many fine–coarse
roots throughout
Friable b2 5.0
Bt1 0.18–0.29 Clear smooth 5YR 4/6 (none) (none) Clay loam 1 m sbk b5% distinct
patchy clay films
Common medium
roots throughout
Friable b2 4.5
Bt2 0.29–0.80 Clear smooth 7.5YR 4/6 (none) (none) Clay loam 1 c sbk b5% distinct
patchy clay films
Common medium
roots throughout
Friable 10 (very soft) 4.5
CB1 0.80–1.10 Clear smooth 7.5YR 4/4 2% fine
distinct
irregular
2% fine
distinct
irregular
Sandy clay
loam
1 c sbk 2% faint
discontinuous
clay films
Few fine roots in
cracks
Friable 80 (very soft) 4.5
Fe-depletions
(7.5YR 5/3)
Fe-concretions
(7.5YR 5/6)
Table 1 (continued)
H.S.Lin
etal./Geoderm
a131(2006)345–368
352
CB2 1.10–1.52+ 7.5YR 4/6 5% fine
prominent
irregular
5% fine
prominent
irregular
Sandy clay
loam
1 c sbk 2% faint
discontinuous
clay films
Few fine roots
throughout
Friable 80 (very soft) 4.5
Fe-depletions
(2.5YR 5/1)
Fe-concretions
(7.5YR 5/8)
Ernest Series (fine-loamy, mixed, superactive, mesic Aquic Fragiudults) (4.94% area; located in floodplain and western end of the valley floor; N1 m depth to bedrock; 0.4-m depth to redox features;
somewhat poorly drained)
Oe 0–0.05 Abrupt smooth Very many fine
roots throughout
0
A 0.05–0.15 Clear smooth 10YR 3/2 (none) (none) Silt loam 1 c sbk
parting to
2 f gr
(none) Very many fine
roots throughout
Friable 0 6.0
AE 0.15–0.20 Clear smooth 10YR 3/1 (none) (none) Silt loam 1 f gr (none) Few fine roots
throughout
Friable 0 5.5
Bw 0.20–0.30 Clear smooth 10YR 4/2 (none) (none) Silty clay
loam
1 m sbk (none) Few coarse roots
throughout
Friable 0 5.5
Bt 0.30–0.53 Abrupt smooth 10YR 5/2
(60%)
20% medium
prominent,
2.5Y 6/1
20%,
7.5YR 5/6
Silty clay 1 c pr
parting to
1 c sbk
60% distinct
continuous
clay films
Few coarse roots
in cracks
Firm 0 5.0
2C 0.53–0.89 Abrupt smooth 10YR 5/3 (none) (none) Sandy loam (massive) (none) Few coarse roots
in cracks
Friable 80 (soft) 4.5
3Cg 0.89–0.97 Abrupt smooth 2.5Y 7/1
(60%)
40% coarse
prominent,
7.5YR 6/8
(none) Clay (massive) (none) (none) Friable 0 4.5
4Cg 0.97–1.37 Abrupt smooth 10YR 5/3
(80%)
5% medium
prominent,
10YR 6/1
5%,
7.5YR 6/8
Sandy loam (massive) (none) (none) Friable 90 (soft) 4.5
5Cg 1.37–1.43 Abrupt smooth 2.5 Y 7/1
(60%)
40% coarse
prominent,
7.5YR 5/8
(none) Clay (massive) (none) (none) Friable 0 4.5
6Cg 1.43–1.47+ (massive) (none) (none) Friable 90 (soft)
a Hand texture.b Pedality is described using ped grade, ped size and ped shape; 1, 2, 3 for weak, moderate, and strong ped grades, respectively; vf, f, m and c for very fine, fine, medium an coarse ped sizes, respectively;
gr, pr, abk, and sbk for granular, prismatic, angular blocky, and subangular blocky ped shapes, respectively.c Rock fragments are all shale channers of 2–150 mm thick, many are soft or very soft.
H.S.Lin
etal./Geoderm
a131(2006)345–368
353
H.S. Lin et al. / Geoderma 131 (2006) 345–368354
ing angle between swale center points and edges
using a field clinometer, then calculating the
planimetric distance between the swale center points
and edge points and vertical displacement from
edge to center of the swales, determining the
function that describes the elevation profile of each
swale, resampling the 10-m DEM to 3-m and
running a 3x3 low pass filter to smooth the
surface, and using the AGREE method (Maidment,
2002) of DEM conditioning to bburnQ the stream
vector into the elevation surface.
We measured local slope for each of the 30
monitoring sites investigated in this study. A meter
stick was placed on the land surface and oriented in
the steepest downslope direction. The downhill end of
Table 2
Basic characteristics used to group subsurface soil moisture of the 30 mo
Soil
series
Soil
moisture
cluster
Local
slope
(%)
Topographic
wetness
index
Weikert Dry 13.4 4.92
Weikert Dry 14.8 4.22
Weikert Dry 15.3 4.94
Weikert Dry 16.2 4.28
Weikert Dry 18.5 4.05
Weikert Dry 20.5 4.50
Weikert Dry 20.6 4.66
Weikert Dry 24.4 4.81
Weikert Moderately dry 25.0 5.00
Weikert Moderately dry 29.0 4.79
Weikert Moderately dry 29.7 4.85
Weikert Moderately dry 31.2 5.62
Weikert Moderately dry 31.2 7.36
Weikert Moderately dry 31.8 4.71
Weikert Moderately dry 36.7 4.70
Weikert Moderately dry 38.1 5.16
Berks Moderately dry 30.5 5.08
Berks Moderately dry 38.4 5.16
Rushtown Moderately dry 30.2 5.05
Rushtown Moderately dry 31.9 6.14
Rushtown Moderately wet 22.7 7.42
Rushtown Moderately wet 24.3 4.45
Rushtown Moderately wet 26.4 4.56
Rushtown Wet 6.2 7.88
Rushtown Wet 15.1 6.59
Blairton Wet 6.2 10.67
Ernest Moderately dry 31.2 5.18
Ernest Moderately wet 18.6 6.09
Ernest Wet 15.7 10.41
Ernest Wet 16.5 11.87
the stick was raised until level (using a leveling tool)
and the distance from the downhill end of the stick to
the ground was recorded. The local slope is simply the
ratio of rise over run. A comparison of the local slopes
measured in situ and the slopes derived from the
refined 10-m DEM shows a reasonable correlation
(Table 2) that is within 95% prediction interval. The
refined DEM was then used to calculate terrain
attributes including the topographic wetness index
(Fig. 1C) defined as:
TWI ¼ ln a=tanbð Þ; ð1Þ
where a is the upslope contributing area calculated
with the D-inf algorithm described in Tarboton (1997)
and tanh is the slope calculated as the steepest
nitoring sites in the Shale Hills catchment
Depth to
bedrock
(m)
Slope derived
from DEM
(%)
Elevation
(m)
Site #
0.33 12.7 278.8 3
0.30 24.0 295.5 8
0.38 18.4 277.5 29
0.35 18.7 287.0 28
0.30 10.0 294.1 23
0.25 33.1 279.3 25
0.30 18.7 277.0 27
0.30 24.5 274.3 30
0.66 32.0 294.8 14
0.30 33.8 266.8 4
0.53 29.6 274.4 5
0.40 28.1 275.6 9
0.38 20.0 283.8 16
0.53 36.9 271.2 2
0.23 36.7 287.0 10
0.40 35.8 281.2 7
0.60 26.5 278.5 24
1.04 32.2 289.4 22
1.14 17.7 291.4 18
1.09 25.2 282.0 15
1.52 23.6 277.6 21
1.93 28.0 294.2 19
1.85 23.3 289.5 17
1.52 15.5 281.7 13
3.77 15.3 271.5 20
1.56 7.2 275.1 12
1.02+ 24.0 264.3 26
1.52+ 14.4 260.0 1
1.52+ 9.0 268.2 6
1.52+ 8.2 262.7 11
H.S. Lin et al. / Geoderma 131 (2006) 345–368 355
outward slope on one of eight triangular facets
centered at each grid cell, measured as drop/distance
(i.e., tan of the slope angle) (Tarboton, 1997).
The bedrock of the catchment is composed of
Rose Hill shale over 200 m thick (Berg et al., 1980).
Depth to bedrock was determined using augers and
GPR units, revealing depth ranges from b0.25 m on
the ridge tops and upper side slopes to greater than 2
m in the valley bottom and swales. Depth of bedrock
observations were collected during soil mapping and
instrument installation. A third-order local polyno-
mial interpolation based on 223 auger observations
was implemented within ArcGIS Geostatistical Ana-
lyst to generate a map of depth to bedrock for the
entire catchment (Fig. 1B). Anisotropy was
accounted for with a major semi-axis (approximately
parallel to the stream) of 115 m and a minor semi-
axis of 60 m. This interpolation method is not
necessarily exact (i.e., predicted surface is not forced
through sample points) but provides for a smooth
surface that accounts for short range variation
(Johnston et al., 2001).
2.3. Soil moisture and hydrological monitoring
Once the detailed soil map was developed, it
was used in combination with slope gradient and
water flow pathways to select 30 sites from
representative transects for monitoring soil moisture
at multiple depths (Fig. 1). Additional transects
crossing flowpaths were included to better capture
flow gradient. Prior to the whole soil profile
monitoring, a reconnaissance campaign was con-
ducted to explore the spatial and temporal varia-
bility of surface soil moisture and its relation to a
number of terrain attributes. Measurements at 189
surface points (Fig. 2) were conducted on ten days
from July 28 to October 13, 2003. After the
reconnaissance campaign, whole soil profile mon-
itoring was conducted from March 24 to June 30,
2004. Roughly twice a week measurements were
made from March 24 to June 7 and daily measure-
ments were conducted from June 14 to June 30.
Twelve of the 30 sites were also instrumented with
nested tensiometers, piezometers, thermocouples,
and shallow water table observation wells. This
paper focuses on the analysis of volumetric soil
moisture data.
Surface soil moisture measurements were per-
formed using a Theta Probe combined with a HH2
readout (Delta-T Devices, England), which uses
Frequency Domain Reflectometry (FDR) to deter-
mine volumetric water content. Each monitoring site
had a specific area designated to insert the probe.
Leaf litter (Oe horizon) was removed before taking
measurements and then replaced after five replicate
measurements were taken. The average of the five
replicates was used in the analysis. The Theta probe
was designed to take moisture measurements to a
soil depth of 0.06 m. Subsurface soil moisture
measurements were performed using a Trime-FM
Tube Probe (IMKO, Germany). This instrument uses
Time Domain Reflectometry (TDR) technology and
was designed to take volumetric soil moisture
readings while being placed at certain depth in a
0.051-m diameter Schedule 40 PVC access tube. The
access tubes were installed to a maximum depth of
1.1 m, with the aid of a Giddings Auger Kit. The
bulk of the soil was removed using a tapered bit on a
metal pipe that was pounded into the ground using a
slide hammer. A second metal tube was then used
with an inverse cutting bit that shaped the hole to
slightly smaller than 0.051-m diameter to ensure a
tight fit of the PVC tube against the soil. The bottom
end of the access tube was capped with PVC
cemented test cap. The tube was then placed into
the augured hole with a tight fit with the surrounding
soil. The top end was capped with a removable PVC
end cap. After installation, the access tubes were left
undisturbed for several months so that settling could
occur, resulting in good tube-soil contact. Actual
measurements were taken by lowering the Trime-FM
Tube Probe into the access tube with the waveguides
fitting tightly against PVC pipe walls. Readings were
then taken at three depth intervals of 0.11–0.29,
0.51–0.69, and 0.91–1.09 m (representing 0.2, 0.6,
and 1.0 m depths, respectively). The Trime-FM Tube
Probe is 0.18 m long and its midpoint was used to
determine the measurement depth from the soil
surface. If the access tube in a site did not allow
for deeper measurement to be taken (because of
shallow depth to bedrock), the last measurement was
taken at the bottom of the access tube, which is
labeled as the bdeepest measurement depthQ in this
paper. Three sets of measurements were taken, with
the probe rotated 1/3 of a turn between each set. The
B) Surface Soil Moisture Distribution Maps
A) Sampling Points, Daily Precipitation, and Selected Surface Soil Moisture Distribution Curves
C)
D)
July 28 Aug. 12 Aug. 15 Aug. 25
Cumulative Distribution by
Soil Series:
Weikert
Berks
RushtownErnest
Cumulative Distribution by Wetness Index:
High (>10)
Low (<7)
Medium (7-10)
Soil Moisture (v/v)
Surface SoilMoisture (%vol.)
High : 60.0
Low : 5.0
0 37.5 75 150Meters
6.0
4.5
3.0
1.5
0.0
Date
Pre
cipi
tatio
n (c
m)
0.35
0.30
0.25
0.20
0.15
30
20
10
0
0 0.1 0.2 0.3 0.4 0.5 0.6
Mea
n so
il m
oist
ure
(v/v
)
Surface Soil Moisture (v/v)
Per
cent
of O
bser
vatio
ns (
%)
7/30
/200
38/
9/20
038/
19/2
003
8/29
/200
39/
8/20
039/
18/2
003
9/28
/200
310
/8/2
003
28 July12 Aug.15 Aug.25 Aug.
100
50
0
0.1 0.2
Cum
ulat
ive
Per
cent
100
50
0
Cum
ulat
ive
Per
cent
0.3 0.4 0.5 0.6
0.1 0.2 0.3 0.4 0.5
100
50
0
0.1 0.2
100
50
0
0.3 0.4 0.5 0.6
0.1 0.2 0.3 0.4 0.5
100
50
0
0.1 0.2
100
50
0
0.3 0.4 0 0.6
0.1 0.2 0.3 0.4 0.5
100
50
0
0.1 0.2
100
50
0
0.3 0.4 0.5 0.6
0.1 0.2 0.3 0.4 0.5
H.S.Lin
etal./Geoderm
a131(2006)345–368
356
0.
.5
H.S. Lin et al. / Geoderma 131 (2006) 345–368 357
average of the three replicates was used in the
analysis.
A stream gauging station was used to monitor
stream flow at the outlet of the catchment. A V-notch
weir equipped with a continuous water level recorder
was programmed to collect stream stage every 15 min,
which was converted to discharge using a rating curve
and then integrated to daily stream discharge for each
day of the monitoring period. The rate of change of
discharge was calculated from the slope of the hydro-
graph. Daily precipitation was recorded in a weather
station about 0.8 km away from the study catchment.
2.4. Soil moisture spatial and temporal pattern
analysis
All geospatial maps involved in this study were
processed using ArcGIS to explore spatial correla-
tions. The surface soil moisture data at 189 points
were interpolated using universal kriging with ArcGIS
Geostatistical Analyst (Johnston et al., 2001) to depict
the spatial patterns of soil moisture in the catchment.
A number of terrain attributes (topographic wetness
index, slope, contributing area, aspect, curvature, plan
curvature, profile curvature, and flow length) were
calculated using the refined DEM, and were examined
in relation to the observed soil moisture patterns.
However, only the topographic wetness index and
slope were shown to provide useful interpretations of
the observed soil moisture data in this study.
Statistical analyses of the spatio-temporal data
were performed using SAS (SAS Institute Inc., Cary,
NC). In addition to routine statistical analysis, we
used cluster analysis to place the 30 monitoring sites
into groups as suggested by the data, not defined a
priori, such that sites in a given cluster tend to be
similar to each other in some sense, and sites in
different clusters tend to be dissimilar (sometimes this
method is also referred to as unsupervised pattern
recognition). The PROC CLUSTER in the SAS was
Fig. 2. (A) Site map of surface soil moisture reconnaissance campaign
reconnaissance campaign (with red dots indicating the catchment-wide av
days), and the distribution of surface soil moisture over the catchmen
distribution over the entire catchment (rendered in 3D, with the soil map
dry-down sequence. (C) Cumulative distributions of surface soil moisture
on the grid cells shown in (B). The Blairton soil series is not shown beca
of surface soil moisture content by topographic wetness index groups for
in (B).
used to hierarchically cluster the observations in a
data set using the Ward’s minimum-variance method
(SAS, 1999). In Ward’s minimum-variance method,
the distance between two clusters is the ANOVA sum
of squares between the two clusters added up over all
the variables. At each iteration, the within-cluster sum
of squares is minimized over all partitions obtainable
by merging two clusters from the previous iteration
(SAS, 1999). The PROC CLUSTER creates an output
data set that can be used by the TREE procedure to
draw a tree diagram of the cluster hierarchy. Prior to
the cluster analysis, PROC STDIZE was used to
standardize variables to mean zero and variance one.
The PROC PRINCOMP was also used to examine the
interrelationships among a set of variables used for
clustering. This analysis uses linear transformations of
original variables to create a new set of uncorrelated
variables, called the principal components (PCs),
which can then be used in cluster analysis. The first
dimension (first PC) shows the largest variance of the
projected data. The second PC displays the next
largest variance and is orthogonal to the first, and so
on. The eigenvectors for each of the PCs, which relate
the components to the original variables, are scaled so
that their sum of squares is unity. This allows the
determination of which, if any, of the original
variables dominates a component.
3. Results and discussion
3.1. Surface soil moisture patterns in relation to soil
type and topography
The maps of surface soil moisture distribution
obtained during the reconnaissance campaign (Fig. 2)
suggested that the five soil series in the catchment
contributed differently to the expansion and contrac-
tion of the wet areas as the catchment wetted and dried
due to the precipitation and evapotranspiration forcing.
at the Shale Hills (189 sites), the daily precipitation during the
erage volumetric soil moisture content on each of the ten campaign
ts in four representative days. (B) Map of surface soil moisture
overlaid) in the four representative days, illustrating a wet-up and a
content by soil series for each of the four representative days based
use of its tiny area in the catchments. (D) Cumulative distributions
each of the four representative days based on the grid cells shown
H.S. Lin et al. / Geoderma 131 (2006) 345–368358
The Ernest soil was always wetter and the Weikert soil
was, for the most part, drier than the other soil series
(Fig. 2). Cumulative distributions of the surface soil
moisture content by different soil series in the entire
catchment clearly separated out the drier Weikert soil
from the wetter Ernest soil, with the other soil series in
between (Fig. 2C). The topographic wetness index
also separated out relatively wet and dry surface areas
in the catchment (Fig. 2D). A visual inspection of Fig.
1A and C suggests that the pattern of the topographic
wetness index had some correlation with the overall
distribution of the soil types in this catchment. How-
ever, considerable overlaps in the topographic wetness
index and slope values (both the local slope and
DEM-derived slope) exist among different soil series,
but the depth to bedrock separates out the shallower
Weikert and Berks series from the deeper Rushtown,
Blairton and Ernest series (Table 2 and Fig. 4).
The surface and subsurface soil moisture data
collected during the 2004monitoring period at multiple
depths confirmed that the Ernest soil was generally the
wettest and the Weikert soil the driest in the catchment,
with the other three soil series in between (Fig. 3).
Approximately normal distribution of the surface
soil moisture content in the entire catchment was
observed in each of the ten campaign days (Fig. 2A).
The surface soil moisture maps in four representative
Measurem
0-0.06 0.11-0.29
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
0.0
0.1
0.2
0.3
0.4
0.5
0.6Weikert (nBerks (n=RushtownBlairton (nErnest (n
Fig. 3. Volumetric soil moisture content at four measurement depths as sum
to June 30, 2004. Error bars indicate one standard deviation.
days (Fig. 2B) illustrated that the wetting and drying
in the catchment occurred most noticeably in the
Weikert soils that were widely distributed on the steep
hillslopes, then progressed into the swales and the
valley floor with other wetter soils. This wet-up and
dry-down pattern is consistent with the overall
distribution of the soil types and the topographic
wetness index in the catchment (Figs. 1 and 2).
3.2. Subsurface soil moisture patterns and the
interactions between soils and topography
There are complex interplays between topography
and soils in controlling soil moisture distributions,
which often depend on the degree of soil wetness, soil
depth, dominant flow process involved, and seasonal
climatic condition. In our humid forested catchment
having similar vegetation and geology throughout, the
distribution of soil moisture is conditioned by the
combined effect from topography and soil character-
istics. Hence, a combination of terrain attributes with
soil distribution provides a good interpretation of the
observed soil moisture patterns. For all grid cells in
the catchment (each cell represents 9 m2), a general
increasing trend in the depth to bedrock and the
topographic wetness index is obvious from the
Weikert series to the Berks series and to the Rushtown
ent Depth (m)
0.51-0.69 0.91-1.09 or Deepest
=16) 2) (n=7) =1)
=4)
marized by the soil series for the monitoring period from March 24
H.S. Lin et al. / Geoderma 131 (2006) 345–368 359
series, but the overall slope distribution among these
three soil series is indistinguishable (Fig. 4 and Table
2). The difference in individual attributes of the depth
to bedrock, topographic wetness index, and slope
among the Rushtown, the Blairton, and the Ernest
series is also not obvious (Fig. 4 and Table 2).
Cluster analysis based on the depth to bedrock (i.e.,
soil depth, a key feature used to differentiate various
soil series in the catchment), topographic wetness
index, and local slope showed that the 30 monitoring
sites could be grouped into wet, moderately wet,
moderately dry, and dry locations that exhibited
different spatio-temporal patterns of subsurface soil
moisture (Figs. 1A and 5). The wet and moderately
wet groups include the Ernest, the Blairton, and the
140
90
40
Weikert Berks Rushtown Blairton Ernestn = 7446 n = 464n = 21n = 603n = 926
Dep
th to
Bed
rock
(cm
)
A)
Box plot
Median
3rd quartile (Q3)
1st quartile (Q1)
Whisker extends to thisadjacent value – the highestvalue within upper limit
Whisker extends to thisadjacent value – the lowestvalue within upper limit
Outliers*
Fig. 4. Box plot of (A) depth to bedrock (i.e., soil depth), (B) topographic
Shale Hills catchment, as grouped by the five soil series. Note that the depth
auger length used in the field investigations.
Rushtown series that are along the stream floodplain
or at the center of the swales. These are the soils with
N1 m depth to bedrock and 0.4 to N3 m depth to redox
features (Tables 1 and 2). The moderately dry group
sites are largely in the backslopes (middle of the
hillslopes) or at the edges of soil boundaries that
transit to another group. The local slopes of this group
are N25% (mostly N30%), with diverse soil morpho-
logical features (Tables 1 and 2). The dry group sites
include only the Weikert soils that are mostly at the
summit or shoulder of the hillslopes (local slopes
b25%). While the soil types reflect this clustering to a
significant extent, it is not the only factor. This is
expected because the soil series developed were
generally not based on their hydrological significance
0
10
20
30
40
50
Slo
pe (
%)
20
15
10
5
Wet
ness
Ind
exB)
C)
Weikert Berks Rushtown Blairton Ernestn = 7446 n = 464n = 21n = 603n = 926
Weikert Berks Rushtown Blairton Ernestn = 7446 n = 464n = 21n = 603n = 926
wetness index, and (C) DEM-derived slope for all grid cells in the
to bedrock for the Blairton and Ernest soil series was limited by the
DryModerately DryModerately WetWet
8-Apr-04
0.55A) 0.00-0.06 m depth
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
Mea
n S
oil M
oist
ure
(v/v
)
28-Apr-04 18-May-04
Date7-Jun-04 27-Jun-04
DryModerately DryModerately WetWet
8-Apr-04
0.55B) 0.11-0.29 m depth
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
Mea
n S
oil M
oist
ure
(v/v
)
28-Apr-04 18-May-04
Date7-Jun-04 27-Jun-04
DryModerately DryModerately WetWet
8-Apr-04
0.55C) 0.51-0.69 m depth
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
Mea
n S
oil M
oist
ure
(v/v
)
28-Apr-04 18-May-04
Date7-Jun-04 27-Jun-04
Dry
Moderately DryModerately WetWet
8-Apr-04
0.55D) 0.91-1.09 m or the Deepest
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
Mea
n S
oil M
oist
ure
(v/v
)
28-Apr-04 18-May-04
Date7-Jun-04 27-Jun-04
Fig. 5. Daily mean soil moisture content at four monitoring depths, each grouped by the four clusters of monitoring sites: (A) 0–0.06 m, (B)
0.11–0.29 m, (C) 0.51–0.69 m, and (D) the deepest (0.91–1.09 m or at the soil-bedrock interface if shallower depth to bedrock). The lines
linking the measurement points are intended for facilitating visual comparison, not implying the actual soil moisture change in between the
measurement dates.
H.S. Lin et al. / Geoderma 131 (2006) 345–368360
(most were probably conceived for agricultural or
forestry production purposes). As shown in Table 2,
the separation of different soil moisture clusters within
a soil series is largely controlled by the local slopes
(rather than the topographic wetness index or the
DEM-derived slope values). For example, the dry and
moderately dry Weikert sites are separated by the local
slopes of 25%. The differentiation of the three wetness
groups within the Rushtown and the Ernest series is
also reflected better by the differences in the local
slopes rather than the wetness index or the DEM-
derived slope values.
Fig. 5 shows the clear separations of the four soil
wetness clusters at three subsurface depths, with the
wet group sites having about 15% to 25% higher
volumetric moisture content than the dry group sites
throughout the 3-month monitoring period. The sites in
moderately wet and moderately dry groups generally
had soil moisture contents in between the wet and dry
groups. The deepest measured depth (0.91–1.09m or at
the soil-bedrock interface if depth to bedrock is
shallower) maintained a volumetric moisture content
of about 0.35m3/m3 or above in the wet andmoderately
wet group sites during the majority of the monitoring
days, while those in the dry and moderately dry group
sites were generally b0.25 m3/m3 (Fig. 5D). For
surface soil moisture, the distinction among three of
the four wetness groups was not significant (Fig. 5A).
This is probably related to the organic layer (Oe
horizon) above the mineral surface soils (A horizon)
H.S. Lin et al. / Geoderma 131 (2006) 345–368 361
throughout the catchment (Table 1). The Oe horizon
functions like a sponge and could therefore reduce the
differences among the four wetness groups in terms of
their surface soil moisture content.
To better understand how the topography and the
soil attributes contribute to the above grouping, the
principal components (PC) of the three original
variables (depth to bedrock, topographic wetness
index, and local slope) were examined. The first PC
accounts for 55.47% of the standardized variance, the
second PC explains 28.07%, and the third PC reflects
16.46%. The eigenvectors that relate the PCs to the
original variables indicate that the first PC has high
positive loadings on the soil depth and the topo-
graphic wetness index, as well as high negative
loading on the local slope (Table 3). This component
seems to measure the overall impact from soils and
topography. The second PC is dominated by the soil
depth and the local slope, with almost no loading from
the wetness index. Thus, the second PC may be a
measure of the landscape position, where different
local slopes and soil depths exist. The third PC shows
a positive relationship with the wetness index and the
local slope and a negative relationship with the soil
depth. This component seems to measure the overall
impact from topography and soils again. It is apparent
from Table 3 that the contribution from soils and
topography to the grouping of the monitoring sites is
hard to separate, although the soil’s impact seems to
be a bit more important, especially when supple-
mented by the information on local slopes (Table 2).
A couple of soil mapping issues are noted here
that affect the clustering results. One is the use of
clear-cut boundaries that do not reflect the often
gradual changes of soil properties, and the other is
the variability within a soil map unit. For example,
most sites (62%) in the moderately dry group belong
to the Weikert series, but five sites do not. Two of
Table 3
Eigenvectors of the principal components (PCs) used to group
subsurface soil moisture of the 30 monitoring sites in the Shale Hills
catchment
Principal components
(% variance explained)
PC1
(55.47%)
PC2
(28.07%)
PC3
(16.46%)
Depth to bedrock 0.5256 0.7246 �0.4458Topographic wetness index 0.6577 �0.0137 0.7532
Local slope �0.5396 0.6890 0.4837
these sites (Sites 22 and 24) are mapped as the Berks
series that are at the boundary with the Weikert soil
(Fig. 1). The other two sites are mapped as the
Rushtown series, with one site (Site 18) located near
the boundary with the Berks and Weikert soils, and
the second site (Site 15) behaving differently from
the typical Rushtown soils probably due to spatial
heterogeneity within that large soil map unit (Fig. 1).
The fifth site (Site 26) is mapped as the Ernest but is
at the boundary with the Berks and Weikert soils
(Fig. 1). Therefore, the issues of within-map-unit
variability and fuzzy boundaries need to be better
addressed in order to further enhance the accuracy of
soil map applications in landscape hydrology.
3.3. Temporal patterns of soil moisture and their
relations to precipitation and stream discharge
Despite the presence of the 0.05-m-thick forest litter
layer (Oe horizon) throughout the catchment (Table 1),
the surface soil moisture responded to the general pat-
tern of precipitation events (Fig. 6). Even some subsoils
displayed observable quick responses to high rainfall,
though in much smaller magnitude compared to the
surface soils (Fig. 6). This suggests the rapid move-
ment of water through the soils. This is supported by
the soil morphology observed in situ (e.g., medium soil
texture with many shale channers, moderately deve-
loped soil structures, and many tree roots) (Table 1).
Temporal patterns in soil moisture also corre-
sponded with the stream discharge. The stream
hydrograph at the catchment outlet displayed a peak
about one day after each major precipitation event
(over ~20 mm cumulative rainfall in the preceding
day or two), but smaller rainfall events did not
produce an increase in daily stream discharge (Fig.
6). The stream hydrograph during the monitoring
period in 2004 could be separated into the following 2
or 3 periods (Fig. 6): The first period is from late
March to mid-May (March 24 to May 17), when
stream discharge displayed significant peak rises (rate
of rise ranged from 2.25 to 3.54 m3/d, with an average
of 2.66 m3/d) and steep drops of recession (rate of
faster drop in initial 2 days after a major precipitation
event ranged from 0.87 to 1.68 m3/d, with an average
of 1.31 m3/d, and the rate of slower recession
thereafter ranged from 0.34 to 0.50 m3/d, with a
mean of 0.43 m3/d). The second period is from mid-
3/29/04 4/12/04 4/26/04 5/10/04 5/24/04 6/7/04 6/21/04
0
10
20
30
40
0
10
20
30
40
0.0
0.1
0.2
0.3
0.4
0.5
Daily PrecipitationDaily Stream DischargeSite 20 (Footslope) Rushtown Soil SeriesSite 21 (Backslope) Rushtown Soil SeriesSite 22 (Shoulder) Berks Soil Series
3/29/04 4/12/04 4/26/04 5/10/04 5/24/04 6/7/04 6/21/04
0
10
20
30
40
0
10
20
30
40
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Daily Precipita tionDaily Stream DischargeSite 1 (Footslope) Ernest Soil SeriesSite 2 (Backslope) Weikert Soil SeriesSite 3 (Shoulder) Weik ert Soil Series
North-Facing Hillslope Transect South-Facing Hillslope Transect
A) 0-0.06 m D) 0-0.06 m
3/29/04 4/12/04 4/26/04 5/10/04 5/24/04 6/7/04 6/21/04
0
10
20
30
40
0
10
20
30
40
0.0
0.1
0.2
0.3
0.4
3/29/04 4/12/04 4/26/04 5/10/04 5/24/04 6/7/04 6/21/04
0
10
20
30
40
0
10
20
30
40
0.0
0.1
0.2
0.3
0.4
0.5
B) 0.11-0.29 m
C) The Deepest Depth
3/29/04 4/12/04 4/26/04 5/10/04 5/24/04 6/7/04 6/21/04
0
10
20
30
40
0
10
20
30
40
0.0
0.1
0.2
0.3
0.4
3/29/04 4/12/04 4/26/04 5/10/04
0
10
20
30
40
0
10
20
30
40
0.0
0.1
0.2
0.3
0.4
0.5
E) 0.11-0.29 m
F) The Deepest Depth
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
Str
eam
Dis
char
ge (
m3 )
Dai
ly P
reci
pita
tion
(mm
)
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
Str
eam
Dis
char
ge (
m3 )
Dai
ly P
reci
pita
tion
(mm
)D
aily
Pre
cipi
tatio
n (m
m)
Str
eam
Dis
char
ge (
m3 )
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
Dai
ly P
reci
pita
tion
(mm
)
Str
eam
Dis
char
ge (
m3 )
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
Str
eam
Dis
char
ge (
m3 )
Dai
ly P
reci
pita
tion
(mm
)
Vol
umet
ric S
oil M
oist
ure
Con
tent
(m
3 /m3 )
5/24/04 6/7/04 6/21/04
Str
eam
Dis
char
ge (
m3 )
Dai
ly P
reci
pita
tion
(mm
)
Fig. 6. Temporal dynamics of soil moisture content at different depths in the soils of a north-facing hillslope and a south-facing transect. Daily
precipitation and stream daily discharge during the same monitoring period are also plotted. The lines linking the measurement points are
intended for facilitating visual comparison, not implying the actual soil moisture change in between the measurement dates.
H.S. Lin et al. / Geoderma 131 (2006) 345–368362
H.S. Lin et al. / Geoderma 131 (2006) 345–368 363
May to near the end of June (May 17 to June 20),
when the stream discharge had much smaller magni-
tude of rise and recession (rate of peak rise ranged
from 0.30 to 1.06 m3/d, with an average of 0.64 m3/d;
and rate of recession ranged from 0.21 to 0.39 m3/d,
with a mean of 0.30 m3/d), even though the rainfall
amounts in the second period were similar to that in
the first period. The separation of these two periods
was probably due to the significant volume of water
released from frozen soils and saturated rocks after
long period of precipitation accumulation over the
winter and early spring seasons. The third period in
the stream hydrograph is not yet very clear during our
monitoring period, but still may be separated out from
around late June onward, when the stream discharge
started to show a base flow condition (with an average
flow rate of around 11.60 m3/d) (Fig. 6).
The temporal changes in soil moisture (both
surface and subsurface) did not have contrasting
periods as that noted for the stream hydrograph.
Nevertheless, starting from mid-June, surface and
subsurface soil moistures appeared to enter into a dry-
down period that was consistent with the stream base
flow condition noted above (Figs. 5 and 6).
During a dry down period from June 14 to June
30, 2004, the averaged soil moisture daily drying rate
in the entire catchment was 1.4% by volume for the
0–0.06 m depth, and 0.4%, 0.2%, and 0.2% by
volume for the 0.11–0.29, 0.51–0.69, and the deepest
measurement depths, respectively. However, signifi-
cant spatial and temporal variability existed among
the monitoring sites and from day to day at the same
monitoring sites. In the surface, the dry cluster sites
had, on average, 69% of the days with decreasing soil
moisture content (there were 5 rainy days during the
17-day dry-down period); while the wet cluster sites
had an average of 60% of the days with decreasing
daily soil moisture content. In the subsurface, the
differences in drying and wetting between the dry and
wet sites were more complicated. At the 0.11–0.29 m
depth, the wet monitoring sites had a higher
percentage of drying days (74% on average) than
the dry sites (average 65%). But the reverse is true at
the deepest monitoring depth (0.91–1.09 m or
shallower depth to bedrock), with 58% drying days
for the wet sites and 65% for the dry sites. The
change in soil moisture content between June 14 and
June 30 illustrated that more drying occurred at or
near the surface throughout the catchment and in the
subsurface of the backslopes and shoulders of the
hillslopes, whereas the deeper soils in the wet sites
maintained a relatively consistent high moisture
content (Fig. 7A and B). The number of days a
shallow water table was observed during the 2004
monitoring period supported that the water had
accumulated towards the valley floor and the swales,
leading to wetter subsurface in the wet sites (Fig. 7C).
The coefficient of variation for surface soil moisture
in each of the 30 sites was averaged at 20.4% (range
11.9–27.0%) for the 2004 monitoring period, while
that for the subsurface was 8.1% (range 3.9-23.1%),
6.4% (range 2.3–22.4%), and 6.0% (range 2.4–
13.2%) for the 0.11–0.29, 0.51–0.69, and the deepest
measurement depths, respectively. Such a coefficient
of variation showed a general decreasing trend from
the dry to the wet group sites in most measurement
depths, especially in the subsurface.
3.4. A conceptual hydrological model in the Shale
Hills catchment
Based on the soil moisture spatial and temporal
patterns discussed above, supplemented by many in
situ observations, a conceptual model of the hillslope
hydrological processes in the Shale Hills is proposed
(Fig. 8). This conceptualization generalizes the soil
catena along the hillslopes, the soil moisture profile
distributions, and the main flow pathways downslope.
Overall, a bdry-moderately dry or moderately wet–
wetQ sequence from the shoulder of the hillslope
through the backslope to the footslope is observed in
this humid forest catchment during our monitoring
period (Figs. 1A and 8). The wet group sites are
largely distributed along the valley floor or at the
swale bottom, which generally have higher moisture
stored in the deeper subsurface. However, depending
on the soil distribution and soil depth, the above stated
hillslope soil moisture trend may vary. For example,
an increasing trend of surface soil moisture content
from the shoulder to the footslope was obvious in both
a north-facing slope and a south-facing slope illus-
trated in Fig. 6. But such a trend changed in the
subsurface. At 0.11–0.29 m depth, the backslopes in
both the hillslopes showed lower soil moisture
contents than the footslopes and shoulders (Fig. 6B
and E). At the deepest measurement depth, soil
Fig. 7. Change in volumetric soil moisture content during a dry-down between June 14 and June 30, 2004 at (A) 0.11–0.29 m and (B) the deepest monitoring depth (i.e., 0.91–1.09 m
or at the soil–bedrock interface if shallower depth to bedrock). Also shown in (C) is the number of days a shallow water table was observed in 12 of the 30 monitoring sites during the
monitoring period from March 24 to June 30, 2004.
H.S.Lin
etal./Geoderm
a131(2006)345–368
364
0.1 0.2 0.3 0.4 0.50
0.2
0.4
0.6
0.8
1
1.2
O
A
C
Bt
O
A
BworBt
C/R
OA
Bw
C/R
Valley Flooror Swale Bottom
(Wet Site)
Hilltop(Dry Site)
3) Return flow at footslope and toeslope during snow
melts or large storms
1) Subsurface seepage through macropore networks in subsoils
4) Flow at the soil-bedrock interface
Depth(m)
Backslope(Moderately Wet or
Moderately Dry Site)
Average volumetric soil moisturecontent (m3/m3)
WetSite
DrySite
Moderately
Wet Site
Moderately
Dry Site
2) Lateral flow through the interface between A and B horizons
BwStream or
Bt
Fig. 8. A conceptual model of the hillslope hydrology in the Shale Hills catchment. Three typical soil profiles along the hillslope are illustrated,
along with their averaged soil moisture profile distributions observed in this study (shown in the inset). Arrows indicate the main flow pathways
identified.
H.S. Lin et al. / Geoderma 131 (2006) 345–368 365
moisture in the north-facing hillslope showed a much
higher content at the footslope (the Ernest soil) than at
the backslope and shoulder (both the Weikert series)
(Fig. 6C). In contrast, the transect in the south-facing
hillslope displayed nearly indistinguiable moisture
contents among the three slope locations (Fig. 6F).
This was because this hillslope was in a swale (Fig.
1), with the soils at the footslope and backslope being
the Rushtown series and the soil at the shoulder being
the Berks.
We identified four main flow pathways downslope
along the swales or the sideslopes, upon which stream
flow in the catchment is sustained year-around (Fig.
8). The first one is the subsurface seepage through
macropore networks in the subsoils. This was
evidenced by many tree root channels and chipmunk
burrows that conducted considerable volume of water
during large storms or snow melts. Continuous
preferential flow paths via macropore networks have
been reported in other forested watersheds by hydro-
metric measurements and tracer tests and by direct
observations from staining tests (e.g., Noguchi et al.,
1999; Sidle et al., 1995, 2001). The second flow
pathway identified is the lateral flow at the interface
between the A and B horizons having different soil
structures, densities, and hydraulic conductivities.
This was evidenced by in situ observations in
excavated soil trenches. Consequently, a noticeable
feature in the soil moisture profile distributions (Fig. 8
inset) is the tendency of having the lowest moisture
content at the 0.11–0.29 m depth that corresponds to
the A–B horizon interface. The third flow pathway in
H.S. Lin et al. / Geoderma 131 (2006) 345–368366
the catchment is the return flow (as surface runoff) at
the footslopes and toeslopes of the Ernest soil area
where the stream channel is located. This flow
pathway is seasonal (related to snow melts) or
sporadic (related to large storms) but once activated
it may contribute considerable volume of water to the
stream. However, no surface runoff occurs in the rest
of the catchment, including the backslopes and the
shoulders of the steep hillsopes probably because of
the litter layer at the surface and highly permeable
soils underneath. The fourth flow pathway in this
catchment is the flow at the interface between the
bottom of soil profile and the underlying weathered
and fractured shales, largely in areas of the Weikert
and Berks soils with shallow depth to bedrock. Both
the number of days a shallow water table was
observed during the monitoring period (Fig. 7C) and
the soil moisture change during the dry down from
June 14 to June 30 (Figs. 7 A and B) indicated that
water has accumulated towards the valley floor
through the subsurface flow including that at or near
the soil–bedrock interface. Nevertheless, more direct
evidence of the fourth flow pathway is needed.
Earlier research on preferential flow paths focused
on vertical movement; however, lateral transport is
evident in steep forested slopes underlain by bedrock,
as is the case in this study. Based on the field
observations in a forest watershed in Japan, Sidle et al.
(2001) proposed that lateral preferential flow system
in forested watersheds is linked to a series of bnodesQof connectivity that can be conditioned by different
levels of antecedent soil moistures. The nodes may
include physical interconnection of short macropore
segments (e.g., living roots), buried pockets of organic
matter or loose soil (e.g., those caused by wind
throws), and direct interaction with a lithic boundary
(including fractures). As suggested by Sidle et al.
(2001), various bnodesQ require different levels of
local hydrologic conditioning to become activated and
are influenced strongly by soil depth, horizonation,
permeability, pore size distribution and tortuosity,
organic matter distribution, and surface and substrate
topography. On steep hillslopes, where lateral flow is
supported by large hydraulic gradients, preferential
flow paths may also tend to self-propagate downslope
as the result of momentum dissipation (Germann and
Niggli, 1998; Germann and Di Pietro, 1999). The
conceptual model of Sidle et al. (2001) emphasized
the importance of macropore networks in generating
lateral preferential flow in relation to the mechanism
of subsurface stormflow generation. The conceptual
model suggested in this study further elaborates on the
patterns of soil moisture distribution along the hill-
slope and within the soil profiles, their relations to the
soil series, and the four main flow pathways down-
slope that sustain the stream flow.
4. Conclusions
The detailed soil map (Order I) obtained in this
study has demonstrated its value in enhancing the
understanding of spatio-temporal patterns of soil
moisture at the Shale Hills catchment. Terrain attrib-
utes (such as the topographic wetness index and slope)
do not fully explain the hydrological processes and soil
moisture differences in the catchment. The soil series
coupled with local slope separated out reasonably well
the monitoring site groups (wet, moderately wet,
moderately dry, and dry) that exhibited different
spatio-temporal patterns of subsurface soil moisture.
While the individual contributions from soils and
topography to the wetness grouping are hard to
separate because of their complex interactions, soil
features are obviously significant (such as horizona-
tion, presence of a fragipan-like layer, depth to redox
features, rock fragment content, soil structure, and root
density). In addition, the value of detailed soil
mapping includes an understanding of the flow paths
and of soil depth variation as summarized in the
conceptual hydrological model developed for the
study watershed (Fig. 8). Hydropedological perspec-
tive emphasizes soil-landscape relationships by con-
sidering soils and topography simultaneously. It also
calls for adequate attention to soil morphology that is
indicative of soil hydrological characteristics. How-
ever, while a sufficiently detailed soil map is
valuable in differentiating soil moisture patterns
and in understanding hydrological processes, the
issues of within-map-unit variability and fuzzy soil
boundaries need to be better addressed in order to
further enhance the accuracy of soil map applications
in landscape hydrology.
Despite the leaf litter layer throughout the forest
catchment, surface and subsurface soil moisture at the
Shale Hills responded to the general pattern of
H.S. Lin et al. / Geoderma 131 (2006) 345–368 367
precipitation events, particularly high rainfalls. Tem-
poral patterns in soil moisture also corresponded with
stream discharge. The quick stream flow response to
the climatic forcing in the catchment indicates the
rapid movement of water within the catchment into
the stream channel. This is consistent with the
observed soil morphological features and the steep
slopes in the V-shaped catchment. The swales in the
catchment also facilitate the concentration of down-
slope water movement into the stream.
The proposed conceptual model of the hillslope
hydrological processes elaborates on the patterns of
soil moisture distribution along the hillslope and
within the soil profiles, as well as their relations to
the soil series and the four main flow pathways
downslope (i.e., subsurface macropore flow, subsur-
face lateral flow at A–B horizon interface, return flow
at footslope and toeslope, and flow at the soil–bedrock
interface). Further testing of this conceptual model
would lead to enhanced understanding and modeling
of preferential flow dynamics at the small watershed
scale, particularly in relation to the role of soil
distribution and lateral flow.
Acknowledgements
This study is sponsored by the USDA National
Research Initiative (grant #2002-35102-12547). We
are grateful to Dr. Christopher Duffy for supplying the
stream discharge data and to Dr. James Lynch for
providing the precipitation data used in this study. We
extend our thanks to Jim Doolittle for collaboration on
the GPR investigations and to Jake Eckenrode for
assistance in the detailed soil survey. Assistance in
field data collections from Brad Georgic and Michael
Kochuba is also acknowledged. We also wish to
acknowledge the insightful review comments of Drs.
Andrew Western and Anthony O’Geen.
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