SSSA 75th Anniversary Paper

Kirkham’s Legacy and Contemporary
Challenges in Soil Physics Research
W. A. Jury*

Dep. of Environmental Sciences
Univ. of California
Riverside, CA 92521

D. Or

Dep. of Environmental Sciences
ETH Zurich,
8092 Zurich, Switzerland

Y. Pachepsky

USDA-ARS
Environmental Microbial andFood Safety Lab.
Beltsville, MD 20705

H. Vereecken

Agrosphere Institute (IBG-3)
Forschungszentrum Jülich GmbHD-52425 Jülich, Germany

J. W. Hopmans

Dep. of Land, Air and Water Resources
Univ. of California
Davis, CA 95618

L. R. Ahuja

USDA-ARS
Agricultural Systems Research Unit
Fort Collins, CO 80526

B. E. Clothier

Systems Modelling
New Zealand Institute for Plant &Food Research Ltd,
Palmerston North 4442, New Zealand

K. L. Bristow

CSIRO Sustainable Agriculture
National Research Flagship
CSIRO Land and Water
PMB Aitkenvale
Townsville, QLD 4814, Australia

G. J. Kluitenberg

Dep. of Agronomy
Kansas State Univ.
Manhattan, KS 66506

P. Moldrup

Dep. of Biotechnology, Chemistry
and Environmental Engineering
Aalborg Univ.
9000 Aalborg, Denmark

J. Šimůnek

Don Kirkham was instrumental in transforming soil physics into a modern scientific discipline by developing theories based on verifiable hypotheses, creating methods to test the hypotheses, and applying the theories to problemsof importance to society. We, the recipients of the Don and Betty Kirkham Award in Soil Physics, show how thislegacy continues to affect soil physics. We describe eight longstanding or emerging research areas in soil physics
that contain key unsolved problems. All are field oriented, with applications to a number of important issues inagriculture and the environment. The first three problems deal with the topic of characterization of field-scale soil
water properties, within which we describe progress on scaling, effective hydraulic properties, and the relationshipbetween soil structure and function. We then move to the description of unstable flow and characterizing water
repellency, and finish with discussions on the effect of plants on transport processes, characterizing soil microbial
diversity, and the importance of soil ecological infrastructure in providing ecosystem services. The challenges we
discuss reflect inherent gaps between the complexity of the soil environment and its biogeochemical function,and the limited measurement and analytical tools at our disposal. Improving our predictive capabilities at relevant
spatial and temporal scales will be necessary to address some of the long-standing problems within agriculture andthe soil environment.

D

on Kirkham was a true pioneer and innovator whose career as a teacher andscientist was instrumental in transforming soil physics into a rigorous discipline with a solid theoretical foundation. Inspired by his example and honored
to have received the Don and Betty Kirkham Award in Soil Physics, we the coauthors found it appropriate to dedicate our contribution for the 75th anniversary
issue of the Soil Science Society of America Journal to Don Kirkham’s legacy, witha theme that he would have appreciated.
We describe research problems that we have worked on during our careers
whose quantification and solution remain elusive. Some have been labored on formany years, while others have only recently emerged. All are field oriented, withapplications to a number of important issues in agriculture and the environment.
The first three problems deal with the topic of characterization of field-scale soil
water properties, within which we describe progress on scaling, effective hydraulic properties, and the relationship between soil structure and function. We thenmove to the description of two important problems, unstable flow and water repellency, whose behavior violates the assumptions of our foundational Richards equation describing water flow in soil. We conclude with discussions of three emerging
areas of research: the effect of plants on transport processes, the characterizationof soil microbial diversity, and the importance of soil ecological infrastructure inproviding ecosystem services.

Dep. of Environmental Sciences
Univ. of California
Riverside, CA 92521

M. Th. van Genuchten

Dep. of Mechanical Engineering
COPPE/LTTC
Federal Univ. of Rio de Janeiro (UFRJ)
Rio de Janeiro, RJ, Brazil

R. Horton

Dep. of Agronomy
Iowa State Univ.
Ames, IA 50011

Soil Sci. Soc. Am. J. 75:1589–1601
Posted online 11 Aug. 2011
doi:10.2136/sssaj2011.0115
Received 29 Mar. 2011.
*Corresponding author (wajury@mail.ucr.edu).
© Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by
any means, electronic or mechanical, including photocopying, recording, or any information storage
and retrieval system, without permission in writing from the publisher. Permission for printing and forreprinting the material contained herein has been obtained by the publisher.

SSSAJ: Volume 75: Number 5 • September–October 20111589

CHARACTERIZING TRANSPORTPROPERTIES IN FIELD SOIL
Problem 1: Development of Scaling Relations
The soil properties that influence the transport and retention of water and chemicals are difficult and time consuming tomeasure. Moreover, they vary considerably in space even acrosssmall distances in the natural environment. Our flow and transport models often require area- or volume-averaged estimates ofthese properties, yet the number of samples necessary to calculate
meaningful averages at the field scale is often prohibitively large.
Scaling relations were developed as a means to circumvent themeasurement problem and to enable comparisons between similar systems by using one or more easily measured or estimatedparameters to describe the differences in soil geometry from one
region of soil to another. These parameters are then related totransport and retention properties through theoretical or empirical arguments, thereby allowing a set of properties measured
at a given location to be extended to other regions via the regions’scaling factors.

Scaling Soil Heterogeneity
The simplest form of scaling is geometric similitude (Miller
and Miller, 1956), in which all regions are regarded as structurally identical magnifications of a reference location, and a single
scaling length λi (m) is all that is required to relate locations toeach other. In this idealized medium, all locations have the same
porosity and the local matric potential hi (m) at water content θ
is related to the reference matric potential href at the same watercontent by the relation

λh i ( θ ) = ¬ ref h ref ( θ )λi[1]Similarly, the hydraulic conductivities Ki (m s-1) of such a medium scale byλ2
K i ( θ ) = ¬ 2i K ref ( θ )
λ ref

[2]

Fig. 1. Conceptual model of evolving soil heterogeneity (after Tyler
and Wheatcraft, 1990).
1590

Thus, if the complete set of hydraulic properties is measured at
the reference location, then only the scaling lengths need to be
known to calculate the hydraulic properties everywhere.
When tested under field conditions, the geometric similitude form of scaling theory proved inadequate at representing theheterogeneity of soil hydraulic properties. When separate scaling
parameters were used for matric potential and for hydraulic conductivity, the variability was greatly reduced but not eliminated(Warrick et al., 1977). Further modifications, such as using relative saturation rather than water content, or using saturated hydraulic conductivity as a third scaling factor and scaling the relative hydraulic conductivity reduced the variability even more butat the price of requiring additional measurements at each location.
Pore and particle size distributions have proven to be usefulfor determining scaling factors. By assuming that the pore size
distribution is lognormally distributed, Kosugi and Hopmans(1998) were able to use a physically based model to scale the soil
water retention and unsaturated hydraulic conductivity functions. The scaled data both allowed the calculation of scaledmean properties representing spatially averaged soil hydraulicfunctions and also characterized the spatial variability of the local properties. Recently, Nasta et al. (2009) successfully used thesoil particle size distribution as the basis for scaling soil hydraulicproperties after first dividing soils into groups of similar soil texture. Other researchers have utilized empirical relations for poresize distribution indexes of soil types to scale hydraulic properties across diverse soil series (Williams and Ahuja, 2003).

Upscaling
Scaling can also be used as a means of extending local measurements to larger soil volumes. An interesting implementation of this principle is the scaleway approach (Vogel and Roth,
2003), where the large-scale soil framework is represented as amultiscale pattern defined by a hierarchical and nested order ofscales (Fig. 1). The structure at any scale is explicitly considered,
while the finer scale heterogeneities are replaced by effective
properties that may be represented statistically. To use the approach, a representation of the structure, a process model at thescale of interest, and the corresponding effective material properties must be known.
Fractal scaling models have been used to represent thesoil water retention curve (Tyler and Wheatcraft, 1990) andto model the hydraulic conductivity (Gimenez et al., 1997),
with the fractal dimension related to the pore size distribution.
Analogous approaches have been developed using percolation
theory (Hunt and Ewing, 2009). Whether these types of scaling would be applicable across a larger extent of scales than thelimited range tested remains an open question.
We continue to be challenged by the task of applying physical, chemical, and biological principles derived or observed atsmall scales to the larger scales where they must be applied. For
example, the Richards equation describing water flow has beentested only at small scales yet is increasingly being used to modelsignificantly larger spatial scales in applications such as coupling
SSSAJ: Volume 75: Number 5 • September–October 2011

unsaturated flow with climate and groundwater models, hydrologic modeling of watersheds, and as a component of ecosystemmodeling. There currently exists, however, no way to measure theboundary conditions, soil hydraulic functions, or vegetation information required to calibrate the model at this scale nor has
it been demonstrated that such functions even exist. As a consequence, less complex conceptual models that do not require
unsaturated soil hydraulic properties may be more appropriate at
those scales, with soil hydraulic information inferred from indirect measurements such as remote sensing.
New instrumentation and techniques for measuring soil
properties in situ are needed. Upscaling approaches are currently
limited by our inability to measure soil hydraulic properties at
a range of spatial scales and by inadequate representation of theheterogeneity of natural soil. Geophysical techniques, such as
electrical resistance tomography and ground penetrating radarthat sense soil dielectric and conductive properties throughout
substantial soil volumes, may prove to be useful for inferring average soil hydraulic properties.
Multifractal modeling of soil structure shows promise in generating realistic hydraulic networks at fine scales, but significant
additional effort is required to develop procedures for statistically characterizing and averaging across those structures to infer
hydraulic properties at coarser scales. Inverse modeling can be applied to determine scale-dependent effective soil hydraulic properties across a wide range of spatial scales for which laboratory (soilcore) and field experiments are available; however, the solutions
obtained by this method may not always be unique. At larger spatial scales, remote sensing may be useful for developing relations
between area-averaged quantities such as storage and drainage.

Problem 2: Defining Effective Properties
An effective property is a functional relationship between
volume-averaged quantities and is scale and model dependent.
Unsaturated hydraulic conductivity, K(h), is an example of aneffective property, defined as the ratio between the flux and thehydraulic potential gradient, all measured at the same scale. It has
been shown to be monotonic and unique when the volume average is performed within a region of homogeneous porous materials. As the scale of averaging becomes larger and the material
more heterogeneous, however, it is not guaranteed that a uniquerepresentation of this function will exist.
Hydraulic functions measured at the core or plot scale arenotoriously variable in natural soil, with properties such as infiltration rate ranging across several orders of magnitude within atypical field (Sharma et al., 1980). Because the information required to create a continuous map of the variability of these properties is unobtainable, plot- and field-scale models of infiltration,soil water movement, and solute transport generally use averageor “effective” soil hydraulic properties to represent the processes.
Bresler and Dagan (1983) found, however, that effective average
hydraulic properties within a heterogeneous volume cannot bederived that predict correct averaged outcomes for all conditions
and processes. Moreover, changing the boundary or initial condi-

SSSAJ: Volume 75: Number 5 • September–October 2011

tions can sometimes produce different effective properties. Thus,
it is important to obtain ways of defining and measuring effective
properties that minimize errors in computing average flow andtransport for a range of initial and boundary conditions.
Several investigators have developed methods for defining
meaningful effective average properties under certain conditions. Feddes et al. (1993a) showed that inverse modeling andsoil water content averaged from discrete measurements could
be used to obtain effective properties that predicted the averagewater movement under dry to medium soil water conditions. In
a companion study, they used area-averaged surface soil moistureand evaporation estimated from remote sensing to calculate theeffective hydraulic properties for a large area by inverse modeling(Feddes et al., 1993b).
Zhu and Mohanty (2002) found that the geometric meanvalue of the Brooks–Corey (Brooks and Corey, 1964) bubblingpressure parameter and the arithmetic mean of the saturated
hydraulic conductivity optimized the prediction of large-scale,steady-state evaporation and infiltration in a soil overlying shallow groundwater. The accuracy of the predictions depended onflow conditions and improved when the parameters were correlated, but the parameters defining the effective properties weredifferent for evaporation and infiltration. Ahuja et al. (2010)
computed the effective hydraulic properties for several different hypothetical soil compositions for rain infiltration and redistribution under 100-kPa initial suction. The effective properties obtained by matching the average early stage ponded-water
infiltration of the component soils gave generally good resultsfor infiltration up to 4 h for four rain intensities but failed todescribe the observed soil water content distribution. To obtainacceptable results for both infiltration and redistribution, the effective saturated hydraulic conductivity obtained by infiltration
matching had to be combined with the arithmetic means of thesoil water retention parameters.

Future Directions
These studies and many others not mentioned show that theconcept of an effective property averaged throughout a heterogeneous soil is a tenuous one that nonetheless may be useful undercertain circumstances. At the present time, however, we are limited by a lack of understanding of a number of important relationships. For example, more data are needed describing the effect ofvarying rainfall intensities on effective hydraulic properties in avariety of heterogeneous soils to determine if a relationship exists
between the effective properties and the average rainfall intensity. Similarly, additional effort is needed to evaluate the effect ofdifferent initial soil water conditions on effective properties for
infiltration and redistribution in various soils and to study the interactions between the initial conditions and rainfall intensities.
We need to extend the concept of effective properties to layered soils having vertical as well as horizontal variability. It may bepossible that in most cases infiltration is restricted to the top 30
cm and that the properties of the topsoil control infiltration andthus alone can define the effective properties. This simplification1591

is unlikely to work for redistribution, however, and it may benecessary to use the arithmetic or geometric mean properties of
the combined layers to define the effective properties.

tent and flow as well as soil composition and structure, inverse
modeling to find both parameters of structure and function fromthe same study is a promising new direction of research.

Problem 3: Relating Structure and Function
Structure at Different Scales

Future Directions

Spatial organization in soils, usually referred to as soil structure, is present at all scales, and its influence on hydrologic processes is also scale dependent. At the pore or aggregate scale, soil
structure is defined by the arrangement of minerals and organic
matter in combination with biological components. At the horizon or pedon scale, soil structure appears as soil horizons andpeds, which influence the retention and transport of infiltratingwater and solutes, especially as preferential flow (Lin et al., 1999).
At the field or hillslope scale, surface cover features control storage and partitioning between runoff and infiltration, thereby
affecting groundwater recharge and ecosystem water (Zehe andFlühler, 2001). Finally, at the watershed or basin scale, the spatialorganization of soil types and associations along with the parent
material provide information on soil cover structure, while soil–
atmosphere interactions and feedbacks characterize the soil hydraulic functions emerging at this scale (Vereecken et al., 2010a).
These features are best characterized using noninvasive or remote sensing methods, for example by computer tomography at
the pore or aggregate scale, electrical resistivity tomography at
the horizon or pedon scale, remote sensing vegetation imaging
or ground penetrating radar at the field or hillslope scale, andtopography-based soil landscape relationships at the watershedor basin scale.
At all scales, the critical unresolved issues are: the selectionof parameters to characterize structure and hydraulic functioning,obtaining information about structure and function to find theseparameters, and relating structure and function in a model using
these parameters. Structural heterogeneity is extremely difficult tomeasure, and it is currently impractical to determine the precise
location and properties of all relevant structural features of a soil atimportant scales of interest in soil management. Many soils possess
discrete structural features such as cracks, channels, or macroporesthat occupy a relatively small part of the soil volume but can have asignificant effect on transport and retention properties.

Structure Characterization Methods
One strategy that has been used to overcome this difficulty is
to assume that the spatial void structure is random, which allows
average water flow and retention properties to be inferred from aknowledge of the effect of structure on water behavior at the local scale, together with a statistical representation of the structure(Pachepsky et al., 2008). Several versions of this approach havebeen proposed for soils and other materials in the vadose zone.
Functional evaluation, in which models whose parameters arecalculated from pedotransfer functions are used to make specificpredictions, has become a powerful tool for improving structure–function relationships (Wösten and van Genuchten, 1988).
Because modern noninvasive methods can sense soil water con1592

Simulations of flow and transport in synthetic structures
having the same statistical properties as natural settings are invaluable for discovering and validating structure–function relationships. These efforts will require new tools such as geostatistical simulations or stochastic imaging to generate the synthetic
structures accurately (Pachepsky and Acock, 1998). The description of hydrologic functioning will need to be advanced before
we can parameterize hydrologic pathways that are opened above
threshold values (Zehe et al., 2007).
Pedotransfer function techniques currently use only soil textural information to model hydraulic properties and would benefit greatly from the addition of structural parameters (Vereecken
et al., 2010b). We need to develop ways of parameterizing structural indices obtained from noninvasive methods, however, because the data obtained from destructive sampling have proven to
be only moderately useful (Rawls and Pachepsky, 2002).
Another possible approach to be developed in the future
is to use structure characterization obtained by methods thatsense the void space. An example is fusing data from field measurements of soil gas diffusivity (representing a soil structural
fingerprint of connected soil pores drained at a given matric potential) and soil air permeability (a fingerprint of the larger, wellconnected soil pores drained at a given matric potential) usingstate-of the-art equipment (Hamamoto et al., 2009).
The commonly used Richards water flow equation andconvective–dispersive solute transport equation models have
limited applicability in structured soils where preferential flow
is predominant, yet by default these models are used to infer soil
hydraulic functioning even where their assumptions are not wellmet. Attempts are underway to develop structure-based screening of soil and soil cover to decide whether these classic models
should be used ( Jarvis et al., 2009).
Very little is known about how structure changes with time.
We have some understanding of the effects of tillage and natural reconsolidation on the changes in soil hydraulic properties(Ahuja et al., 1998); however, we need to know much more
about dynamic changes in void space in various structural units
to be able to interpolate and extrapolate structure–function relationships in soil hydrology with time. Such temporal projections
are needed to understand the changes in soil hydrology due tosoil management and due to changes in ecosystems undergoing
ecological succession or affected by climate change.

DEVIATIONS FROM SURFACETENSION–VISCOUS FLOW
Problem 4: Unstable Water Flow in Soil
Research during the last half century has advanced our understanding of the conditions necessary for the onset of unstableflow in porous media, allowed us to characterize many of its feaSSSAJ: Volume 75: Number 5 • September–October 2011

tures, and enabled the development of specialized models of thephenomenon. There are many conditions in soil that allow instabilities to form and develop, including vertical flow from a finetextured layer into a coarse one, vertical flow into a compressed air
phase, infiltration into water-repellent soil, two-phase flow involving two fluids of contrasting density and viscosity, infiltration into
homogeneous soil at flux rates substantially less than the saturated
hydraulic conductivity, and redistribution following infiltration.

Conditions for Instability
Instabilities form when gravitational forces overcome viscous ones. A common instance where this occurs is when thematric potential decreases toward the surface during a flow event,such as during redistribution following infiltration. Once a finger forms and advances ahead of a wetting front into the dry soil
below, it must be supplied by lateral flow of water from the surrounding wet soil matrix as well as from the region directly above
the finger. In addition, downward flow of water and advanceof the draining front in the matrix zone between fingers must
greatly slow down so that water remains available for the finger.
In order for these effects to occur, the dry soil region below thewetting front must have a threshold water-entry matric potential
hwe, (m) below which the wetting front will not advance.
Figure 2a shows a typical finger pattern observed in thelaboratory, and Fig. 2b illustrates the formation and propagationof a finger during redistribution. During normal drainage, waterpressure at the interface between the wet and dry zones is at thewater-entry potential hwe, which allows water to enter the dry
region below the entire draining front. As a perturbation forms,
the depth of penetration becomes slightly greater at one point
along the front, which shifts the local water pressure distribution
downward and creates a local sink above the advancing finger.
Subsequently, the water pressure in the surrounding matrix decreases, and the pressure at the wetting front may drop below
hwe, thereby detaining the primary front and channeling water
preferentially through propagating fingers until flow stops.

Modeling Unstable Flow Features
Description of the fingering process involves characterizing a number of processes and properties. Various attempts havebeen made to predict the finger diameter from soil properties
and water flow characteristics. They either involve an approximate equation that is a function of the sorptivity (Parlange andHill, 1976) or include a macroscopic surface tension associatedwith the large-scale curvature induced by the radius of the finger(Chuoke et al., 1959). A different approach was taken by Wanget al. (1998), who replaced the effective surface tension with an
expression involving the water-entry pressure head hwe of thewetting curve by using the capillarity equation ΔP = 2σ/R,
where P is pressure and σ is the surface tension of water, anddefining an effective macroscopic curvature R* at the interface.
They used empirical relations derived from laboratory studies forR* and the area fraction. Observations of the fingering process
have repeatedly shown that fingers remain narrow as they propa-

SSSAJ: Volume 75: Number 5 • September–October 2011

Fig. 2. (a) Flow instability observed by light transmission during water
infiltration into layered medium, and (b) development of a fluid
instability during redistribution, when capillary pressure h decreases
toward the surface. When the front advances ahead at one location,
the pressure distribution above it shifts downward, creating a lateralflow gradient from adjacent regions (Jury et al., 2003); hwe is thethreshold water-entry matric potential below which the wetting front
will not advance.

gate and persist for a long time after flow stops, which Glass et al.
(1989a,b) showed was due to hysteresis in the matric potential
water content function.

Predicting Unstable Flow
Although most of the effort to characterize fingered flow
has been experimental, recent attempts have been made to model
the transport process when fingers form and propagate. Because
it has been shown that the classical Richards equation is unconditionally stable, even when hysteresis is included (Eliassi and
Glass, 2001), some other formulation is needed to model unstable flow. The first class of models utilizes a nonequilibrium formof the pressure–water content relation together with hysteresis
to generate unstable fingers that do not dissipate (Nieber et al.,2005). The second type of model is based on a pore network ofpipes of variable diameter in which water propagates via invasion percolation (Flekkøy et al., 2002). Each formulation is capable of reproducing fingers that mirror experimentally observed
behavior. More recently, a third type of model formulation has
appeared in which the Richards equation has been modified toinclude an additional term to account for nonlocal effects associated with the extra energy required to displace air–water interfaces (Cueto-Felgueroso and Juanes, 2009). This new phase-fieldmodel turns the Richards equation into a fourth-order differential equation in space. These efforts represent important progressin extending the theory of water flow in porous media to therealm of unstable flow. Further progress is required to take these
new models to the level where they might assist in assessing thedegree to which flow in a soil with a given set of properties and1593

boundary conditions might exhibit instabilities and how such
instabilities might propagate.

Unstable Flow during Redistribution
Many studies, whether experimental or theoretical, have focused on constant infiltration. From a practical standpoint, however, redistribution following infiltration has a much wider range
of application because water application to the surface of a fieldsoil is always intermittent. Moreover, redistribution is inherently
unstable because it induces a negative pressure gradient toward
the surface. Jury et al. (2003) used a conceptual model of fingering during redistribution that embodied known or postulated
information about finger diameter and flow fraction to generate
the final finger distributions as a function of soil properties. The
principal conclusions drawn from this study were that unstableconditions in finer textured soils generated only wide fingers thatmoved negligible distances ahead of the draining front, whereas
fingers in sandy soils could move substantial distances of morethan a meter downward during redistribution following an infiltration of a few centimeters of water. They also noted thatlaboratory column studies of redistribution produce an artifact
because the width of the column is typically too small to allow
instabilities to form.

Characterizing Unstable Flow in Field Soil
Progress in this field has been rapid in recent years, but muchwork remains before we will have a tool for representing unstableflow in real soils. It will probably never be possible to predict the location where a finger will form because it may arise from small heterogeneities that are incapable of being characterized through priormeasurement. Current theories contain parameters that cannot
be determined experimentally; hence, additional effort is needed
to describe such phenomena as effective surface tension, air-entry
matric potential, hydraulic radius, and nonlocal free energy that appear in current formulations. No theoretical basis exists for predicting the area fraction active in fingering, and it must be estimatedempirically. Finally, we have only a limited understanding of how
important the consequences of unstable flow are. We do not knowwhether they are a near-surface phenomenon or whether instabilities will assist flow for substantial distances beneath the surface. We
know next to nothing about the role of fingering in transporting
solutes or how fingers interact with plant roots.

Problem 5: Characterizing Soil Water Repellency
When water does not bond to soil surfaces, the soil isdeemed to be water repellent or hydrophobic. At the pore scale,water repellency alters the contact angle between the solid andliquid phases, which in turn reduces or eliminates the capillary
forces stabilizing water in the soil. New measurement techniques
have been developed to quantify the degree and persistence ofwater repellency, as well as to indicate its potential formation(Wessolek et al., 2009; Lamparter et al., 2010). These methods
have greatly increased our knowledge of when and how water
repellency occurs in soil. While fire-induced soil water repellen1594

cy has been an area of intense interest for some time (DeBano,
2000), repellency arising from other causes has been shown to be
far more widespread than earlier believed.
We have some understanding of the interacting roles playedby the soil’s ecological infrastructure, that is, its porous architecture, its clays, its organic matter, and its microbial inhabitants.
Water repellency has been found to affect each of the soil’s ecosystem services, most particularly those relating to water and C,
in both destructive and beneficial ways. Whereas Miyata et al.
(2009) found that repellency caused an increase in runoff andsoil erosion, Robinson et al. (2010) observed that the increase in
preferential flow due to water repellency increased water sequestration under woodlands. Water repellency can also impact agriculture via the soil’s provisioning ecosystem services for food andfiber production. For example, repellency-induced dry-patch syndrome in pastures in New Zealand can result in a 20 to 30% reduction in growth (M. Deurer, personal communication, 2010).

Modeling Water Repellency
From our observations of the impacts of repellency on soil
ecosystem services, models of soil water repellency are being developed. These range from the functional (Dekker and Ritsema, 1994)
through to the mechanistically complex (Bachmann et al., 2007).
Despite these tools, however, we remain unable to predict whensoil water repellency will occur, or disappear, and what impact thesechanged states might have on the regulating and provisioning of
ecosystem services that are supported by soil functioning. Moreover,
we do not know the spatial extent and temporal duration of either
potential (measured on oven-dry samples) or actual (measured on
wet samples) soil water repellency, nor why potential repellency is
not always present, and why sometimes it can be “washed out” andnot reappear. Our nescience is primarily because we do not understand the genesis of hydrophobicity at the pore-scale level.

Threshold Behavior
We are beginning to understand that a relatively smallchange in soil water content can have large impacts on soil water repellency. Once the soil becomes hydrophobic, it becomes
highly likely that local-scale runoff and infiltration processeson or near the soil surface will initiate preferential flows in the
soil’s macropores above some critical water content (Dekker andRitsema, 1994). We have limited ability to integrate our understanding of local processes, however, to predict what larger scale
impacts on watershed hydrology might occur at the slope andcatchment scale and on the regulating and provisioning ecosystem services across the landscape (Doerr et al., 2007). As C andwater regulation, plus food provisioning, take on increased importance with climate change and population increases, it will be
important to develop a better understanding of the role of water
repellency in the supporting processes that soils provide.
The study of soil water repellency has primarily been thedomain of soil physicists, (Dekker et al., 2005). Because repellency arises from a complex interaction between porous architecture, clay particles, soil organic matter, and microbial proSSSAJ: Volume 75: Number 5 • September–October 2011

cesses, and because hydrophobicity has landscape-wide impacts
on the regulating and provisioning services supported by soil,
it is imperative that a multidisciplinary approach be adopted.
At the fundamental level, this will require physicists to work
closely with chemists, biologists, and mycologists. At the large
scale, agronomists, ecologists, and resource economists needto integrate this better understanding of water repellency into
farm-scale and landscape modeling.

Measurement Needs
Despite recent progress, we are still in urgent need of bettermeasurement techniques. We need better information on the active parts of the soil’s organic matter, on the coating mechanismsthat create hydrophobicity on mineral surfaces, and how thesemicro-effects scale up to the aggregates that comprise the soil’s
porous matrix. New scanning technologies such as nuclear magnetic resonance and near-infrared spectroscopy will probably
help us in this quest, as will investigations using different liquids
to assess the degree of hydrophobicity. Presently, our standard
methods to quantify water repellency only work within a limited
range of contact angles, from about 50 to 110°. New tomographic scanning technologies, along with proximal and remote sensing techniques, will allow us to observe the impact of repellency
at both the local and larger scales of soil functioning.
We must improve the modeling of water-repellency dynamics, as well as its impact on the soil’s ecosystem services.
Our models are either too simple to handle complexity (Dekker
and Ritsema, 1994) or too complex to be useful (Deurer andBachmann, 2007). With better knowledge and improved prediction tools, we can develop strategies to reduce the ecosystem disservices generated by soil water repellency while enhancing the
supporting soil services that are reliant on hydrophobicity. This
could involve modifying the soil’s ecological infrastructure toachieve the sought-after ecosystem services through vegetationand grazing management, by manipulating the soil’s microbiology, or by land imprinting, as well as through the addition of soil
amendments and the use of natural surfactants.

INCORPORATING THE BIOLOGICALCOMPONENTS OF SOIL
Problem 6: Flow and Transport in the
Soil–Plant Continuum
Our understanding of dynamic processes in the soil–plantcontinuum has greatly progressed in recent years due to the development of novel measurement technologies combined withincreasing capabilities and progress in numerical modeling. For
all of this progress, however, we are still at the early stages of understanding how to describe the many processes influencing flow
and transport when plants are present.

Modeling Root Water Uptake
It is now generally accepted that water uptake by roots canbe described by a composite transport model consisting of threemajor pathways: (i) the apoplastic path, which is the free diffu-

SSSAJ: Volume 75: Number 5 • September–October 2011

sional space outside the plasma membrane; (ii) the symplastic
path, which is the inner side of the plasma membrane in whichwater and low-molecular-weight solutes can freely diffuse; and(iii) the transcellular path, which is movement through cells(Steudle and Peterson, 1998). In addition, water uptake can be
regulated through the existence of water channels or aquaporins(Steudle, 2000). These various pathways are typically lumped together in flow models using an approach based on Ohm’s law,consisting of an effective root resistance and a pressure head gradient between the soil and the root compartment.
Current water uptake models either describe the process
explicitly at the scale of a single root or use a large-scale sinkterm. Microscopic-scale models typically calculate water andnutrient fluxes from the soil into the root system based on local
flow laws. Root representation has evolved from simple root
geometry models involving root length density toward more
complex models considering three-dimensional dynamic rootarchitectures embedded in heterogeneous, three-dimensional,
variably saturated soil systems that solve for both the root andsoil water potentials.
Macroscopic-scale models assume that root water uptake is
proportional to the root length density and that uptake is locallyreduced depending on the soil saturation or salinity (Šimůnek
and Hopmans, 2009). This approach does not require information about root geometry and flow paths because it represents
the water uptake process in a single macroscopic term that isincluded in the soil water balance. Macroscopic models are designed to be applied at the field and plot scales, but identificationof their parameters from experiments has proven to be difficult if
not impossible (Vrugt et al., 2001). Moreover, their parameters
are site specific, and models calibrated at one location cannot
be used elsewhere. At the present time, microscopic models canonly be applied at the scale of one or a few plants due to the large
data requirements and computational costs in the numerical solution of the governing equations.
Most of the macroscopic modeling approaches rely on onedimensional root density profiles, neglecting the complete rootarchitecture. Water and nutrient uptake depend on the location
within the root system, however, and may vary in space and timeby an order of magnitude (Pierret et al., 2007). Roots may develop root hairs to increase the area of soil exploitable by the plant
and also to increase plant stability (Gilroy and Jones, 2000).
Large changes in physiological properties may occur along roots
as a result of aging and differentiation (Hodge et al., 2009).

Nutrient Uptake by Plant Roots
The driving force for most solute uptake is the electrochemical gradient across the root plasma membrane, a major portion ofwhich is generated by H-ATPase (Glass, 2009). Some nutrients
may diffuse passively into the root system via this electrochemical gradient (e.g., Ca2+), whereas an uptake mechanism requiring the activation of specific transport pathways is required forother nutrients (e.g., NO3− or PO43−). The transport pathways
used by nutrients therefore depend on the type of nutrient, the1595soil solution conditions, and environmental factors controlling
the plant demand for nutrients and water. Because active uptake
processes may differ significantly among ions and plant species, current model approaches that are based on supply-driven
Michaelis–Menten kinetics do not capture the full complexityof nutrient uptake processes, in particular the dependence of affinity as a function of soil ion concentration (Roose et al., 2001).
More sophisticated approaches that consider the dynamic
character of nutrient uptake parameters and more complex uptake kinetics need to be developed. Some studies have suggested
that plant regulation is the dominant factor in nutrient uptake
and that uptake is demand driven (de Willigen, 1987). The interaction between the above- and belowground parts of the plantsystem and their relation to root dynamics and water and nutrient uptake processes has been neglected (Hodge et al., 2009) andis critical to the further development of our understanding ofwater and nutrient uptake. This implies that the nutrient requirements of the plant and its distribution between root and shootmay need to be modeled as well as conditions within the soil.
Nutrient uptake may also be facilitated by the release of water byroots in dry zones based on the concept of hydraulic lift used byplants to transport water from deeper soil layers to upper drier
layers during the night (Caldwell et al., 1998).
The interaction between water uptake and nutrient uptake
also requires further research because the flow of each can alter theforces driving the other across root surfaces. At present, root waterand nutrient uptake models do not consider interactions between
the two processes. These interactions might be very important in
the case of the adaptation of plants to stresses such as drought, salinity, and toxic substances (Hopmans and Bristow, 2002).

Root–Soil Coupling and Feedback Mechanisms
There are complex interactions and feedback mechanisms
between roots and soil, which typically take place in the rhizosphere. Roots release chemicals such as organic anions, protons,
or enzymes to facilitate the acquisition of nutrients by changing
the pH or redox conditions. Mucilage exuded by roots can modify water retention in the rhizosphere compared with that of thesurrounding bulk soil water (Carminati et al., 2010). Accounting
for numerous chemically induced processes would require theintegration of biogeochemical reaction processes into soil–plantinteraction models (Hinsinger et al., 2009). Up to now, only a
few models have considered the effect of roots on the surrounding soil environment (e.g., Szegedi et al., 2008).
Root architecture development is determined by both biotic and abiotic factors in the soil profile and the aboveground partof the plant (Pierret et al., 2007). For example, there is evidence
that a heterogeneous distribution of nutrients may lead to theproliferation of roots into zones of higher nutrient concentration. The roots of vines and citrus trees, through hormonal signaling from dry soil root zone regions to wet root zone regions,can enable the plant to adopt water conservation strategies that
reduce the amount of irrigation water needed (Dirksen et al.,1979). Stress conditions, such as a lack of nutrients and water
1596

shortage, lead to reallocation of assimilates from the shoot to theroot to optimize the acquisition of water and nutrients by growing more roots in the wet or nutrient-rich areas of the soil. This
indicates that there are complex feedback mechanisms acting inthe soil–root–plant system that are controlled by external conditions requiring further research before models of the process canbe developed (Teuling et al., 2006).
Root conductivity may vary in response to external stress orinternal factors such as the nutritional state and water status of
the plant (Steudle, 2000). Measured uptake rates of NO3− andwater suggest that only 10 and 30%, respectively, of the total root
length system is involved in both processes (Hodge et al., 2009).
In addition, root systems may react to the soil environment by
proliferating into nutrient-rich patches or by retreating from regions of the soil that are devoid of water or nutrients by letting
the root mass die off. Research is needed to improve our understanding of xylem hydraulics, including the extent and mechanisms of hysteresis in xylem transport due to cavitation and amechanistic understanding of the regulation of the plant water
status due to stomatal control. An improved understanding of
xylem dynamics is needed to obtain a mechanistic link between
soil water availability and canopy water use (Sperry et al., 2003).

Large-Scale Modeling of Root Water Uptake
At larger scales, most of the root water uptake models usedin land surface descriptions are empirical and are not able to describe important processes such as the effect of various stress factors on water and nutrient uptake. Recently, Ostle et al. (2009)
pointed out that predictions of the biogeochemical cycles of water, N, and C made with various dynamic global vegetation models may contrast considerably due to differing assumptions about
key soil–plant interaction processes at the local scale. Improved
description of local-scale soil–plant interactions in combination
with identification of the key processes and feedback mechanisms may therefore contribute to an improved prediction of
global biogeochemical cycles.

New Research Opportunities
Improving our understanding of soil–plant interactions will
require well-designed experiments at the laboratory, plot, andfield scales. Early stress recognition of plant stands has become
possible due to the development of novel measurement techniques such as hyperspectral sensors operated at the plant stand,fluorescence, and isotopic techniques. Imaging methods basedon magnetic resonance imaging (MRI), neutron tomography(NT), and electrical resistance tomography (ERT) have allowed
more precise laboratory- and lysimeter-scale experimental studies of soil–root water uptake processes to be conducted, resolving
for the first time the spatial and temporal dynamics of the soil
water distribution from the micrometer scale (MRI and NT) up
to the 0.1-m scale (ERT combined with lysimeter experiments)(see Fig. 3). In addition, new tensiometer systems will allow measurements of pressure head values in regions beyond the permanent wilting point.
SSSAJ: Volume 75: Number 5 • September–October 2011

Problem 7: Physical and Ecological Origins ofSoil Microbial Diversity
Factors Affecting Diversity
By some accounts, exploring the soil microbial diversity represents a scientific frontier at a scope similar to that of space exploration. Curtis and Sloan (2005) stated, “...there are 109 times
more bacteria on Earth than there are stars in the Universe …
an immense and unexplored frontier in science of astronomical
dimensions and of astonishing complexity.” The high degree of
microbial diversity found in soil is a consequence of the many
complex pore surfaces and spaces housing dynamic aqueous andchemical microenvironments that may separate bacteria spatially,physiologically, or genetically. Because soil is far from being well
mixed, spatial heterogeneity in aquatic habitats and the availability of nutrients such as C can arise and persist. Plant-derived
material, the main source of soil C, is transported from the soil
surface to deeper layers via the action of soil macro- and microbiota and, most importantly, by transport with water. Water flow
in soil is not homogeneous, and preferential flow pathways may
form hot spots characterized by a higher water-soluble C concentration than the adjacent soil matrix (Bundt et al., 2001).
The dynamics and spatial arrangement of water are particularly important for soil bacteria. Temporal and spatial variationsin the amount and configuration of water in soil pores results
in a flickering aqueous network that shapes diffusional pathways
for nutrients and promotes or suppresses mobility and connections between soil microbial communities even across very shortdistances (Mills, 2003; Or et al., 2007). Although heterogeneityand microhabitat fragmentation are often cited as factors promoting the immense soil microbial diversity, the study and modeling of the key factors sustaining diversity are in their infancy.

Diversity and Scale
The physical processes that control soil microbial habitats,community diversity, and activity may vary with spatial scales.
Diffusional limitations may dominate at the pore scale, limiting
interaction among microbial colonies with length scales of theorder of 10 to 1000 μm. Even at this microscale, heterogeneous
diffusional pathways and aquatic habitat fragmentation may
support microbial coexistence and lead to the microbial diversity observed in soils (Torsvik and Ovreas, 2008). At the sample
scale of 10 to 100 mm, convective transport becomes prominent,
forming nutrient gradients and supporting “hot spots,” or regionswith elevated microbial activity such as the rhizosphere (Bundt
et al., 2001). Continuum representation of gaseous fluxes and exchange with the atmosphere becomes meaningful at the sample
scale (Skopp et al., 1990). Quantitative models for microbial
transport through soils focus on processes at the sample and profile scale, often combining convective–dispersive transport withelements of filtration theory to describe interactions with soil
surfaces (e.g., Hornberger et al., 1992).
Soil formation processes result in differences in soil materials that in turn affect transport properties and microbial activity
at scales of soil layers (0.1 m) to the soil profile or pedon scaleSSSAJ: Volume 75: Number 5 • September–October 2011

Fig. 3. Three-dimensional root architecture (brown) and water content
changes, Δθ, of a Ricinus plant grown in sandy soil during a periodof 20 d after initial saturation. Original resolution of the magnetic
resonance image is 0.6 mm for the root system and 6.3 mm for thewater content (from Pohlmeier et al., 2008).

(?10 m). Differences in soil texture and organic C content between layers affect long-term wetness and aeration conditions aswell as nutrient and gaseous fluxes, resulting in gradients in microbial abundance and composition (Kreft et al., 1998). Nutrient
diffusion and microbial migration typically do not exceed thepedon scale, giving rise to noninteracting microbial populations.
The primary interactions at these scales are via convective transport pathways (soil macropores and fractures) and plant roots.
Wang and Or (2010) used a model to demonstrate how capillarity and water films constrain bacterial motility and colony
growth on partially hydrated rough surfaces (Fig. 4). Subsequent
simulations and experiments on rough (capillary) surfaces demonstrated that bacterial motility confers ecological advantage
only within a surprisingly narrow range of hydration conditions
and that there is no difference in the expansion rates of motile
and nonmotile bacteria at matric potential values lower than −5
kPa. Subsequent studies illustrated that drier and more heterogeneous rough surfaces promote and prolong the coexistence of
two competing bacterial species (Zhou et al., 2002).

1597

ditions, the primary survival strategy is for microorganisms tocompletely abolish their metabolism and switch into a dormant
state (Torsvik and Ovreas, 2008). Accounting for these limitations is vital to improving the modeling of bacterial transport inunsaturated soil and to understanding the links between microhydrology dynamics and the functionality of species having different survival and reactivation strategies.
Microbes respond to local hydration fluctuations by biosynthesizing extracellular polymeric substances (EPS) surrounding
their cells, which also facilitate colonies sticking to solid surfaces.
Aggregation and pooling of resources as a defense against variations
in hydration status and in nutrient availability enhance cooperative genetic and metabolic exchanges. The ubiquity of microbiallyexcreted EPS across many different environmental conditions andhabitats is attributed to its key role in environmental adaptation, inparticular anchoring, nutrient entrapment, and the maintenanceof favorable hydration conditions (Roberson and Firestone, 1992).
The EPS support higher water retention and consequently higher
nutrient diffusion rates within EPS-rich microenvironments relative to the surrounding soil under dry conditions.
The need to formulate quantitative links between hydrologic processes and microbial life in the soil is motivated bothby fundamental ecological questions related to diversity and its
maintenance as well as by practical environmental, agronomical, and engineering questions, for example, issues related to theintroduction and stimulation of bacteria for remediation activities in the soil or the prediction of bacterially mediated nutrient
cycles and gaseous fluxes at all scales. The environmental impactof the ongoing molecular revolution with rapid advances in theidentification and unraveling of complex functions of microbial
populations would be significantly enhanced when placed in theproper hydrologic and porous media context. That is an interdisciplinary frontier where soil physics can play an important role.
Fig. 4. Effects of hydration status (matric potential) on bacteria
motility and colony expansion on simulated rough surface: (a)simulated bacterial cell velocities as a function of matric potential
with V-shaped bonds with two spanning angles and similar bond depth(100 mm), where the dashed line marks critical water film thickness(equal cell diameter) with Fo, Fλ, and Fc the viscous drag force
opposing motion in bulk water, the cell-surface interactions force,and the capillary pinning force, respectively; (b) simulated microbialcolony patterns 25 h after inoculation on “wet” and “dry” roughsurfaces; and (c) comparison of numerical and analytical models withmeasurements (Dechesne et al., 2008) of colony expansion rates as afunction of matric potential (Wang and Or, 2010).

Microhydrology and Bacterial Survival Strategies
The narrow range of hydration conditions sustaining bacterial motility in soil pores suggests that colonization of new surfaces and dispersion of soil microbial populations is limited toshort time windows when the soil water content is near saturation. The range of water potentials (and relative humidity, RH)
supporting the growth and activity of microbial life is also relatively narrow; at 99% RH, microbial growth becomes limited
and at a water potential of −5 MPa (RH ?96%) bacterial respiration ceases (Potts, 1994). Under extreme desiccation con1598

SOILS AS A COMPONENT OF ECOSYSTEMS
Problem 8: Soils, Ecological Infrastructure,and Ecosystem Services
Soils are often mismanaged because we pay little attention
to their role as key components of “ecological infrastructure.”
This neglect is due to the fact that we have no adequate meansof properly quantifying the natural capital value of soil. Natural
capital comprises the stocks of natural materials and energy(Costanza et al., 1997). Ecological infrastructure (EI) (Bristow
et al., 2010) can be broadly defined as the underlying frameworkof natural elements, ecosystems, and functions and processes thatare spatially and temporally interconnected to maintain the continued regeneration and evolution of life on Earth. Explicitly, the
EI is how natural capital stocks are organized to produce ecosystem goods and services.
Ecosystem goods, such as minerals, foods, fibers, and fuels, canbe easily valued because they provide quantifiable benefits in termsof economic markets. As yet, however, we do not have a complete
understanding of the value of all natural processes, including thosenecessary for human life. Soils provide many raw materials, includSSSAJ: Volume 75: Number 5 • September–October 2011

ing the provisioning of food, fuel, and fiber for humans, plus habitat and refugia for flora and fauna. Soils play a vital role in filtering
nutrients and contaminants, detoxification and decomposition ofwastes, storage of C, water, and N, plus the control and regulationof pests and diseases (Dominati et al., 2010).
In a landmark study, Costanza et al. (1997) estimated theglobal value of ecosystem services to be $US33 trillion. Attempts
have been made to value the services provided by soils (e.g.,
Clothier et al., 2008), but we are still struggling to develop aframework that would enable the total value of the soil’s natural
capital to be accurately determined.
Despite its increasing adoption in science and policy, theecosystems services approach cannot cope with the complexity
and interconnectedness of natural systems because it is very difficult to isolate, quantify, and thus value all the natural processes
involved in the provision of a given service. It is important to
identify and value various ecosystem services before we inadvertently, or even knowingly, lose them, but there is an even greaterand more urgent need to understand and invest in the EI that
underpins all natural processes (Bristow et al., 2010). One of
the key elements of EI is the soil, which provides a great numberof goods and services (Clothier et al., 2008). While the natureand values of some soil services are still unclear, what is becoming clearer is that there needs to be more comprehensive investment in both understanding and maintaining the integrity of theunderlying soil infrastructure that delivers these critical services(Bristow et al., 2010).
Land and water management provides opportunities, andrisks, to change certain soil properties, and thereby exerts influence on various soil processes such as hydrophobicity. This thenaffects the soil’s service delivery for the filtering and buffering ofwater and food provisioning, either from a service perspective(Robinson et al., 2010) or via disservices (Deurer and Bachmann,2007) (see water repellency discussion above).
Soil scientists and economists currently have no common
language with which to communicate, and this has inhibited
their mutual engagement with decision makers and the public(Dominati et al., 2010; Robinson and Lebron, 2010). The concepts of EI and ecosystem services lay the foundation for a solution to this dilemma.
We cannot spatially, or temporally, isolate the value of soils
and their natural processes from the EI because soils are the substrate on which plant and animal ecosystems thrive. They are thematrix through which water, energy, gases, and chemicals flow.
There is an urgent need to increase our understanding of (i) theconnectivity of the EI elements and the ecosystems that comprise
the EI, (ii) the impacts of land uses on EI and how they affect theprovision of ecosystem services, (iii) the connection between EI
and built infrastructure (BI), and (iv) how to better design andoperate the BI so it is more in tune with the EI.
Soils should be treated as a key component of an interconnected EI and linked to the BI. Just like our continuing investment in the BI, which includes roads, power lines, and dams,
we need to ensure investment in restoring, maintaining, andSSSAJ: Volume 75: Number 5 • September–October 2011

enhancing the integrity and functioning of the EI, including thesoil infrastructure. This will require coordinated approaches andwill involve a wide range of scientific disciplines, from soil physicists through to ecologists and economists.
It will also require an “ecological focus” as opposed to simply an “economic focus,” and the development of knowledge that
accounts for the interconnectivity and overall functioning of the
EI. This is not possible using the existing suite of economic andnonmarket valuations and scientific tools. Until a truly transdisciplinary engagement occurs, we will continue to treat soil likedirt and future generations will pay the price for a lack of investment in EI.

SUMMARY AND OUTLOOK
Any discussion of the key challenges facing contemporary(and future) soil physics research would be incomplete without
addressing links with key societal issues dominated by globalchange, food security, and diminishing soil and water resources.
The challenges discussed here reflect inherent gaps between thecomplexity of the soil environment and its biogeochemical function, and the limited measurement and analytical tools at our
disposal. Improving our predictive capabilities at relevant spatial
and temporal scales is necessary to address some of the longstanding problems within agriculture and the soil environment.
Nevertheless, as members of the soil and earth science communities, we need to become more engaged in seeking solutions to
global issues of resource scarcity and environmental degradationthat require multidisciplinary effort. We have listed some of theresearch areas impacting global issues where soil physics (and soil
science) could make important contributions, such as characterizing plant–soil interactions in the hydrologic cycle and foodproduction, and valuing and preserving the soil’s ecological infrastructure and associated ecosystem services. The future visibility
and vitality of our discipline will be greatly enhanced if we play amore prominent role in solving the big problems impacting theplanet by providing input to climate modelers, resource planners,
and policymakers.
For motivation, Don Kirkham kept a note taped to his deskthat read ‘keep moving’ on the challenging problems identified
in this paper.

ACKNOWLEDGMENTS
Input from the following people is gratefully acknowledged: Estelle
Dominati, Markus Deurer, Alec Mackay, and Steve Marchant.

REFERENCES
Ahuja, L.R., F. Fiedler, G.H. Dunn, J.G. Benjamin, and A. Garrison. 1998.
Changes in soil water retention curves due to tillage and natural
reconsolidation. Soil Sci. Soc. Am. J. 62:1228–1233. doi:10.2136/
sssaj1998.03615995006200050011x
Ahuja, L.R., L. Ma, and T.R. Green. 2010. Effective properties of heterogeneous
areas for modeling infiltration and redistribution. Soil Sci. Soc. Am. J.
74:1469–1482. doi:10.2136/sssaj2010.0073
Bachmann, J., M. Deurer, and G. Arye. 2007. Modeling water movement inheterogeneous water-repellent soil: 1. Development of a contact angle-dependent
water-retention model. Vadose Zone J. 6:436–445. doi:10.2136/vzj2006.0060
Bresler, E., and G. Dagan. 1983. Unsaturated flow in spatially variable fields: 2.

1599

Application of water flow to various fields. Water Resour. Res. 19:421–
428. doi:10.1029/WR019i002p00421
Bristow, K.L., S.M. Marchant, M. Deurer, and B.E. Clothier. 2010. Enhancing theecological infrastructure of soils. In R.J. Gilkes and N. Prakongkep (ed.) Soilsolutions for a changing world, Proc. World Congr. of Soil Sci., 19th, Brisbane,
QLD, Australia [CD-ROM]. 1–6 Aug. 2010. Int. Union of Soil Sci.
Brooks, R.H., and A.T. Corey. 1964. Hydraulic properties of porous media.
Hydrol. Pap. 3. Colorado State Univ., Fort Collins.
Bundt, M., F. Widmer, M. Pesaro, J. Zeyer, and P. Blaser. 2001. Preferential flow
paths: Biological ‘hot spots’ in soils. Soil Biol. Biochem. 33:729–738.
doi:10.1016/S0038-0717(00)00218-2
Caldwell, M.M., T.E. Dawson, and J.H. Richards. 1998. Hydraulic lift:
Consequences of water efflux from the roots of plants. Oecologia
113:151–161. doi:10.1007/s004420050363
Carminati, A., A.B. Moradi, D. Vetterlein, P. Vontobel, E. Lehmann, U. Weller,
H.J. Vogel, and S.E. Oswald. 2010. Dynamics of soil water content in therhizosphere. Plant Soil 332:163–176. doi:10.1007/s11104-010-0283-8
Chuoke, R.L., P. van Meurs, and C. van der Poel. 1959. The instability of slow,
immiscible, viscous liquid–liquid displacements in permeable media.
Trans. Am. Inst. Min. Metall. Pet. Eng. 216:188–194.
Clothier, B.E., S.R. Green, and M. Deurer. 2008. Preferential flow and transport
in soil: Progress and prognosis. Eur. J. Soil Sci. 59:2–13. doi:10.1111/
j.1365-2389.2007.00991.x
Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, K.
Limburg, S. Naeem, R.V. O’Neill, J. Paruelo, R.G. Raskin, P. Sutton, andM. van den Belt. 1997. The value of the world’s ecosystem services andnatural capital. Nature 387:253–260. doi:10.1038/387253a0
Cueto-Felgueroso, L., and R. Juanes. 2009. Stability analysis of a phase fieldmodel of gravity-driven unsaturated flow through porous media. Phys.
Rev. E 79:036301. doi:10.1103/PhysRevE.79.036301
Curtis, T.P., and W.T. Sloan. 2005. Exploring microbial diversity—a vast below.
Science 309:1331–1333. doi:10.1126/science.1118176
DeBano, L.F. 2000. The role of fire and soil heating on water repellency
in wildland environments: A review. J. Hydrol. 231–232:195–206.
doi:10.1016/S0022-1694(00)00194-3
Dechesne, A., D. Or, G. Gülez, and B.F. Smets. 2008. The porous surface model:
A novel experimental system for quantitative observation of microbial
growth under unsaturated conditions. Appl. Environ. Microbiol.
74:5195–5200. doi:10.1128/AEM.00313-08
Dekker, L.W., K. Oostindie, and C.J. Ritsema. 2005. Exponential increase inpublications related to soil water repellency. Aust. J. Soil Res. 43:403–441.
doi:10.1071/SR05007
Dekker, L.W., and C.J. Ritsema. 1994. How water moves in a water repellent soil.
I. Potential and actual water repellency. Water Resour. Res. 30:2507–2517.
doi:10.1029/94WR00749
Deurer, M., and J. Bachmann. 2007. Modeling water movement in heterogeneous
water-repellent soil: 2. A conceptual numerical simulation. Vadose Zone J.
6:446–457. doi:10.2136/vzj2006.0061
de Willigen, P. 1987. Uptake potential of non-regularly distributed roots. J. Plant
Nutr. 10:1273–1280. doi:10.1080/01904168709363656
Dirksen, C., J.D. Oster, and P.A.C. Raats. 1979. Water and salt transport,
water uptake, and leaf water potentials during regular and suspended
high frequency irrigation of citrus. Agric. Water Manage. 2:241–256.
doi:10.1016/0378-3774(79)90007-6
Doerr, S.H., C.J. Ritsema, L.W. Dekker, D.F. Scott, and D. Carter. 2007. Water
repellence of soils: New insights and emerging research needs. Hydrol.
Processes 21:2223–2228. doi:10.1002/hyp.6762
Dominati, E.J., M.G. Patterson, and A.D. Mackay. 2010. A framework forclassifying and quantifying the natural capital and ecosystem services ofsoils. Ecol. Econ. 69:1858–1868. doi:10.1016/j.ecolecon.2010.05.002
Eliassi, M., and R.J. Glass. 2001. On the continuum-scale modeling of gravitydriven fingers in unsaturated porous media: The inadequacy of the
Richards equation with standard monotonic constitutive relations
and hysteretic equations of state. Water Resour. Res. 37:2019–2035.
doi:10.1029/2000WR900403
Feddes, R.A., D.H. de Rooij, J.C. van Dam, P. Kabat, P. Droogers, and J.N.M.
Stricker. 1993a. Estimation of regional effective soil hydraulic parameters
by inverse modeling. p. 211–231. In D. Russo and G. Dagan (ed.) Water
flow and solute transport in soils. Springer-Verlag, Berlin.
Feddes, R.A., M. Monenti, P. Kabat, and W.G.M. Bastiaanssen. 1993b. Is large-

1600

scale inverse modelling of unsaturated flow with aerial average evaporationand surface soil moisture as estimated from remote sensing feasible? J.
Hydrol. 143:125–152. doi:10.1016/0022-1694(93)90092-N
Flekkøy, E.G., J. Schmittbuhl, K.J. Maløy, F. Løvholt, U. Oxaal, and P. Aagaard.
2002. Flow paths in wetting unsaturated flows: Experiments andsimulations. Phys. Rev. E 65:036312. doi:10.1103/PhysRevE.65.036312
Gilroy, S., and D.L. Jones. 2000. Through form to function: Root hairdevelopment and nutrient uptake. Trends Plant Sci. 5:56–60. doi:10.1016/
S1360-1385(99)01551-4
Gimenez, D., E. Perfect, W.J. Rawls, and Y. Pachepsky. 1997. Fractal models
for predicting soil hydraulic properties. Rev. Eng. Geol. 48:161–183.
doi:10.1016/S0013-7952(97)00038-0
Glass, A.D.M. 2009. Nitrate uptake by plant roots. Botany 87:659–667.
doi:10.1139/B09-014
Glass, R.J., J.-Y. Parlange, and T.S. Steenhuis. 1989a. Wetting front instability:
1. Theoretical discussion and dimensional analysis. Water Resour. Res.
25:1187–1194. doi:10.1029/WR025i006p01187
Glass, R.J., T.S. Steenhuis, and J.-Y. Parlange. 1989b. Wetting front instability:
2. Experimental determination of relationships between systemparameters and two dimensional unstable flow field behavior in initiallydry porous media. Water Resour. Res. 25:1195–1207. doi:10.1029/
WR025i006p01195
Hamamoto, S., P. Moldrup, K. Kawamoto, T. Komatsu, and D.E. Rolston.
2009. Unified measurement system for the gas dispersion coefficient, airpermeability, and diffusion coefficient in variably saturated soil. Soil Sci.
Soc. Am. J. 73:1921–1930. doi:10.2136/sssaj2009.0012
Hinsinger, P., A.G. Bengough, D. Vetterlein, and I.M. Young. 2009. Rhizosphere:
Biophysics, biogeochemistry and ecological relevance. Plant Soil 321:117–
152. doi:10.1007/s11104-008-9885-9
Hodge, A., G. Berta, C. Doussan, F. Merchan, and M. Crespi. 2009. Plant root
growth, architecture and function. Plant Soil 321:153–187. doi:10.1007/
s11104-009-9929-9
Hopmans, J.W., and K.L. Bristow. 2002. Current capabilities and future needsof root water and nutrient uptake modeling. Adv. Agron. 77:103–183.
doi:10.1016/S0065-2113(02)77014-4
Hornberger, G.M., A.L. Mills, and J.S. Herman. 1992. Bacterial transport inporous media: Evaluation of a model using laboratory observations. Water
Resour. Res. 28:915–938. doi:10.1029/91WR02980
Hunt, A.G., and R.P. Ewing. 2009. Percolation theory for flow in porous media.
2nd ed. Lect. Notes Phys. 771. Springer-Verlag, Berlin.
Jarvis, N.J., J. Moeys, J.M. Hollis, S. Reichenberger, A.M.L. Lindahl, and I.G.
Dubus. 2009. A conceptual model of soil susceptibility to macropore flow.
Vadose Zone J. 8:902–910. doi:10.2136/vzj2008.0137
Jury, W.A., Z. Wang, and A. Tuli. 2003. A conceptual model of unstable flow inunsaturated soil during redistribution. Vadose Zone J. 17:61–67.
Kosugi, K., and J.W. Hopmans. 1998. Scaling water retention curves for soils
with lognormal pore-size distribution. Soil Sci. Soc. Am. J. 62:1496–1505.
doi:10.2136/sssaj1998.03615995006200060004x
Kreft, J.-U., G. Booth, and J.W.T. Wimpenny. 1998. BacSim, a simulator forindividual-based modelling of bacterial colony growth. Microbiology
144:3275–3287. doi:10.1099/00221287-144-12-3275
Lamparter, A., J. Bachmann, M. Deurer, and S.K. Woche. 2010. Applicabilityof ethanol for measuring intrinsic hydraulic properties of sand withvarious water repellency levels. Vadose Zone J. 9:445–450. doi:10.2136/
vzj2009.0079
Lin, H., K.J. McInnes, L.P. Wilding, and C.T. Hallmark. 1999. Effects of soil
morphology on hydraulic properties. Soil Sci. Soc. Am. J. 63:948–961.
doi:10.2136/sssaj1999.634948x
Miller, E.E., and R.D. Miller. 1956. Physical theory for capillary flow phenomena.
J. Appl. Phys. 27:324–332. doi:10.1063/1.1722370
Mills, A.L. 2003. Keeping in touch: Microbial life on soil particle surfaces. Adv.
Agron. 78:1–43. doi:10.1016/S0065-2113(02)78001-2
Miyata, S., K. Kosugi, T. Gomi, and T. Mizuyama. 2009. Effects of forest
floor coverage on overland flow and soil erosion in hillslopes inJapanese cypress plantation forests. Water Resour. Res. 45:W06402.
doi:10.1029/2008WR007270
Nasta, P., T. Kamai, G.B. Chirico, J.W. Hopmans, and N. Romano. 2009. Scalingsoil water retention functions using particle-size distribution. J. Hydrol.
374:223–234. doi:10.1016/j.jhydrol.2009.06.007
Nieber, J.L., R. Dautov, A.G. Egerov, and A.Y. Sheshukov. 2005. Dynamic

SSSAJ: Volume 75: Number 5 • September–October 2011

capillary pressure mechanism for instability in gravity-driven flows: Review
and extension to very dry conditions. Transp. Porous Media 58:147–172.
doi:10.1007/s11242-004-5473-5
Or, D., B.F. Smets, J.M. Wraith, A. Dechesne, and S.P. Friedman. 2007. Physicalconstraints affecting bacterial habitats and activity in unsaturated porous
media: A review. Adv. Water Resour. 30:1505–1527. doi:10.1016/j.
advwatres.2006.05.025
Ostle, N.J., P. Smith, R. Fisher, F.I. Woodward, J.B. Fisher, J.U. Smith, D.
Galbraith, P. Levy, P. Meir, N.P. McNamara, and R.D. Bardgett. 2009.
Integrating plant–soil interactions into global carbon cycle models. J. Ecol.
97:851–863. doi:10.1111/j.1365-2745.2009.01547.x
Pachepsky, Y.A., and B. Acock. 1998. Stochastic imaging of soil parameters
to assess variability and uncertainty of crop yield estimates. Geoderma
85:213–229. doi:10.1016/S0016-7061(98)00021-4
Pachepsky, Y.A., D. Gimenez, A. Lilly, and A. Nemes. 2008. Promises ofhydropedology. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Resour.
2008(3):040.
Parlange, J.-Y., and D.E. Hill. 1976. Theoretical analysis of wetting front
instability in soils. Soil Sci. 122:236–239. doi:10.1097/00010694197610000-00008
Pierret, A., C. Doussan, Y. Capowiez, F. Bastardie, and L. Pages. 2007. Root
functional architecture: A framework for modeling the interplay between
roots and soil. Vadose Zone J. 6:269–281. doi:10.2136/vzj2006.0067
Pohlmeier, A., A. Oros-Peusquens, M. Javaux, M. I. Menzel, J. Vanderborght,
J. Kaffanke, S. Romanzetti, J. Lindenmair, H. Vereecken, and N. J. Shah.
2008. Changes in soil water content resulting from Ricinus root uptake
monitored by magnetic resonance imaging. Vadose Zone J. 7:1010–1017.
doi:10.2136/vzj2007.0110
Potts, M. 1994. Desiccation tolerance of prokaryotes. Microbiol. Rev. 58:755–805.
Rawls, W.J., and Y.A. Pachepsky. 2002. Soil consistence and structure aspredictors of water retention. Soil Sci. Soc. Am. J. 66:1115–1126.
doi:10.2136/sssaj2002.1115
Roberson, E.B., and M.K. Firestone. 1992. Relationship between desiccationand exopolysaccharide production in a soil Pseudomonas sp. Appl. Environ.
Microbiol. 58:1284–1291.
Robinson, D.A., and I. Lebron. 2010. On the natural capital and ecosystem services
of soils. Ecol. Econ. 70:137–138. doi:10.1016/j.ecolecon.2010.08.012
Robinson, D.A., I. Lebron, R.J. Ryel, and S.B. Jones. 2010. Soil water repellency:
A method of soil moisture sequestration in pinyon–juniper woodland. Soil
Sci. Soc. Am. J. 74:624–634. doi:10.2136/sssaj2009.0208
Roose, T., A.C. Fowler, and P.R. Darrah. 2001. A mathematical model of plant
nutrient uptake. J. Math. Biol. 42:347–360. doi:10.1007/s002850000075
Sharma, M.L., G.A. Gander, and C.G. Hunt. 1980. Spatial variability of
infiltration in a watershed. J. Hydrol. 45:101–122. doi:10.1016/00221694(80)90008-6
Šimůnek, J., and J.W. Hopmans. 2009. Modeling compensated root waterand nutrient uptake. Ecol. Modell. 220:505–521. doi:10.1016/j.
ecolmodel.2008.11.004
Skopp, J., M.D. Jawson, and J.W. Doran. 1990. Steady-state aerobic microbialactivity as a function of soil water content. Soil Sci. Soc. Am. J. 54:1619–
1625. doi:10.2136/sssaj1990.03615995005400060018x
Sperry, J.S., V. Stiller, and U.G. Hauck. 2003. Xylem hydraulics and the soil–
plant–atmosphere continuum: Opportunities and unresolved issues.
Agron. J. 95:1362–1370. doi:10.2134/agronj2003.1362
Steudle, E. 2000. Water uptake by plant roots: An integration of views. Plant Soil
226:45–56. doi:10.1023/A:1026439226716
Steudle, E., and C.A. Peterson. 1998. How does water get through roots? J. Exp.

SSSAJ: Volume 75: Number 5 • September–October 2011

Bot. 49:775–788. doi:10.1093/jexbot/49.322.775
Szegedi, K., D. Vetterlein, H. Nietfeld, R. Jahn, and H.U. Neue. 2008. New
tool RhizoMath for modeling coupled transport and speciation in therhizosphere. Vadose Zone J. 7:712–720. doi:10.2136/vzj2007.0064
Teuling, A.J., R. Uijlenhoet, F. Hupet, and P.A. Troch. 2006. Impact of plant water
uptake strategy on soil moisture and evapotranspiration dynamics during
drydown. Geophys. Res. Lett. 33:L03401. doi:10.1029/2005GL025019
Torsvik, V., and L. Ovreas. 2008. Microbial diversity, life strategies, andadaptation to life in extreme soils, p. 15–43. In P. Dion and C.S. Nautiyal(ed.) Microbiology of extreme soils. Springer-Verlag, Berlin.
Tyler, S.W., and S.W. Wheatcraft. 1990. The consequences of fractal scaling inheterogeneous soils and porous media. p. 109–122. In D. Hillel and D.E.
Elrick (ed.) Scaling in soil physics: Principles and applications. SSSA Spec.
Publ. 25. SSSA, Madison, WI.
Vereecken, H., S. Kollet, and C. Simmer. 2010a. Patterns in soil–vegetation–
atmosphere systems: Monitoring, modeling, and data assimilation. Vadose
Zone J. 9:821–827. doi:10.2136/vzj2010.0122
Vereecken, H., M. Weynants, M. Javaux, Y. Pachepsky, M.G. Schaap, and M.Th.
van Genuchten. 2010b. Using pedotransfer functions to estimate the van
Genuchten–Mualem soil hydraulic properties: A review. Vadose Zone J.
9:795–820. doi:10.2136/vzj2010.0045
Vogel, H.-J., and K. Roth. 2003. Moving through scales of flow and transport. J.
Hydrol. 272:95–106. doi:10.1016/S0022-1694(02)00257-3
Vrugt, J.A., M.T. van Wijk, J.W. Hopmans, and J. Šimůnek. 2001. One-, two-,and three-dimensional root water uptake functions for transient modeling.
Water Resour. Res. 37:2457–2470. doi:10.1029/2000WR000027
Wang, G., and D. Or. 2010. Aqueous films limit bacterial cell motility and colony
expansion on partially saturated rough surfaces. Environ. Microbiol.
12:1363–1373. doi:10.1111/j.1462-2920.2010.02180.x
Wang, Z., J. Feyen, and D.E. Elrick. 1998. Prediction of fingering in porous
media. Water Resour. Res. 34:2183–2190. doi:10.1029/98WR01472
Warrick, A.W., G.J. Mullen, and D.R. Nielsen. 1977. Scaling field-measured soil
hydraulic properties using a similar media concept. Water Resour. Res.
13:355–362. doi:10.1029/WR013i002p00355
Wessolek, G., H. Stoffregen, and K. Täumer. 2009. Persistency of flow patterns
in a water repellent sandy soil: Conclusions of TDR readings and a timedelayed double tracer experiment. J. Hydrol. 375:524–535. doi:10.1016/j.
jhydrol.2009.07.003
Williams, R.D., and L.R. Ahuja. 2003. Scaling and estimating the soil water
characteristic using a one-parameter model. p. 35–48. In Y. Pachepsky etal. (ed.) Scaling methods in soil physics. CRC Press, Boca Raton, FL.
Wösten, J.H.M., and M.Th. van Genuchten. 1988. Using texture and other soil
properties to predict the unsaturated soil hydraulic functions. Soil Sci. Soc.
Am. J. 52:1762–1770. doi:10.2136/sssaj1988.03615995005200060045x
Zehe, E., H. Elsenbeer, F. Lindenmaier, K. Schulz, and G. Blöschl. 2007. Patterns
of predictability in hydrological threshold systems. Water Resour. Res.
43:W07434. doi:10.1029/2006WR005589
Zehe, E., and H. Flühler. 2001. Slope scale variation of flow patterns in soil
profiles. J. Hydrol. 247:116–132. doi:10.1016/S0022-1694(01)00371-7
Zhou, J.Z., B.C. Xia, D.S. Treves, L.Y. Wu, T.L. Marsh, R.V. O’Neill, A.V.
Palumbo, and J.M. Tiedje. 2002. Spatial and resource factors influencing
high microbial diversity in soil. Appl. Environ. Microbiol. 68:326–334.
doi:10.1128/AEM.68.1.326-334.2002
Zhu, J., and B.P. Mohanty. 2002. Upscaling of soil hydraulic properties forsteady state evaporation and infiltration. Water Resour. Res. 38:1178.
doi:10.1029/2001WR000704

1601