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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: henrylin@psu.edu (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

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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

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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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