WATER RESOURCES RESEARCH, VOL. 42, W05301, doi:10.1029/2005WR004085, 2006

Hydropedology: Synergistic integration of pedology and hydrology
Henry Lin,1 Johan Bouma,2 Yakov Pachepsky,3 Andrew Western,4 James Thompson,5Rien van Genuchten,6 Hans-Jörg Vogel,7 and Allan Lilly8
Received 6 March 2005; revised 26 December 2005; accepted 9 February 2006; published 9 May 2006.

[1] This paper presents a vision that advocates hydropedology as an advantageous

integration of pedology and hydrology for studying the intimate relationships between
soil, landscape, and hydrology. Landscape water flux is suggested as a unifying precept forhydropedology, through which pedologic and hydrologic expertise can be better
integrated. Landscape water flux here encompasses the source, storage, flux, pathway,
residence time, availability, and spatiotemporal distribution of water in the root and deep
vadose zones within the landscape. After illustrating multiple knowledge gaps that can beaddressed by the synergistic integration of pedology and hydrology, we suggest fivescientific hypotheses that are critical to advancing hydropedology and enhancing theprediction of landscape water flux. We then present interlinked strategies for achieving thestated vision. It is our hope that by working together, hydrologists and pedologists, alongwith scientists in related disciplines, can better guide data acquisition, knowledge
integration, and model-based prediction so as to advance the hydrologic sciences in thenext decade and beyond.
Citation: Lin, H., J. Bouma, Y. Pachepsky, A. Western, J. Thompson, R. van Genuchten, H.-J. Vogel, and A. Lilly (2006),
Hydropedology: Synergistic integration of pedology and hydrology, Water Resour. Res., 42, W05301, doi:10.1029/2005WR004085.

1. Introduction[2] It is well recognized that progress in science depends
increasingly on an advanced understanding of the interrelationships among different disciplines and their components [American Association for the Advancement of
Science Council, 2001]. An interdisciplinary systems approach is a proven vehicle for addressing a wide array ofenvironmental, ecological, agricultural, geological, and natural resource issues of societal importance. Over the pastfew decades, there has been a growing interest in the
adoption of a landscape perspective when examining
cross-disciplinary issues such as nonpoint source pollution,watershed management, integrated agricultural systems,
precision farming, sustainable land use, and ecosystem
restoration and preservation. With a landscape perspective
comes the need to address inherent variability in the field
and to transfer knowledge and data across scales from thelaboratory or small plot to the larger field and watershedscales. It also raises the need for field experimental designs
1
Department of Crop and Soil Sciences, Pennsylvania State University,
University Park, Pennsylvania, USA.
2
Laboratory of Soil Science and Geology, Wageningen University,
Wageningen, Netherlands.
3
Environmental Microbial Safety Laboratory, ARS, USDA, Beltsville,
Maryland, USA.
4
Department of Civil and Environmental Engineering, University of
Melbourne, Parkville, Victoria, Australia.
5
Division of Plant and Soil Sciences, West Virginia University,
Morgantown, West Virginia, USA.
6
George E. Brown Jr. Salinity Laboratory, ARS, USDA, Riverside,
California, USA.
7
Center for Environmental Research, Halle, Germany.
8
Macaulay Land Use Research Institute, Craigiebuckler, UK.

Copyright 2006 by the American Geophysical Union.
0043-1397/06/2005WR004085$09.00

and models to take into account the spatial scale triplet(spacing, support, and extent) and the temporal scale triplet(sampling frequency, smoothing or averaging interval, andlength of record) [Blöschl and Grayson, 2000]. The changing factors that control abiotic and biotic processes in thelandscape continuum should also be taken into account foreffective modeling and reliable prediction.
[3] Pedology and hydrology are scientific disciplines
inherently associated with the landscape perspective. Pedology is a branch of soil science that integrates and quantifies
the formation, distribution, morphology, and classificationof soils as natural or anthropogenically modified landscapeentities [Wilding, 2000; Buol et al., 2001], while hydrology
deals with the occurrence, distribution, circulation, andproperties of water on and beneath the Earth’s surface andits relationship with the living and material components ofthe environment [National Research Council (NRC), 1991;
Hornberger et al., 1998]. Soil-water interactions acrossmultiple scales control much of soil development andresulting spatial variability studied by pedologists. These
interactions also control water quantity and quality insurface and groundwater systems studied by hydrologists.
Combining pedologic and hydrologic expertise can beparticularly powerful in addressing complex environmental
issues and policies [European Confederation of Soil Science
Societies, 2004; Bouma, 2006]. Indeed, interactions
between soil and water create the fundamental interface
between the biotic and abiotic and thus function as a critical
determinant of the state of the Earth system. Traditional
solutions and approaches to measuring, modeling, andpredicting water flux in soils and over landscapes (including
the transport of chemicals and energy by flowing water)
have long been plagued by fragmented discipline-limited
efforts and inadequate perceptions among pedologists andhydrologists of the expertise available from each other. For

W05301

1 of 13

W05301

LIN ET AL.: OPINION

example, the following limitations need to be overcome inorder to ally pedology and hydrology [Lin et al., 2005a].
[4] 1. To many hydrologists, pedologists use unfamiliar,
highly structured terminology to describe field soils andmake empirical statements about soil functions based onfield observations that are not necessarily supported bymeasurements. On the other hand, pedologists challenge
the often simplistic representation of field soils that hydrologists frequently assume in their models;[5] 2. Pedology has its roots in soil surveys that consider
soil-landscape relationships and soil structure. These two
aspects are critical for surface and unsaturated zone hydrology in order to improve quantitative characterization of flow
regimes in the field. However, pedologic knowledge is often
conveyed as qualitative or semiquantitative statements.
Pedologists thus can benefit from flow theories in hydrology when transforming qualitative descriptions into quantitative expressions that are increasingly in demand.
[6] Many knowledge gaps can be addressed by thesynergistic integration of pedology and hydrology. The
following five aspects exemplify the point.
[7] 1. Prediction of preferential flow dynamics and pathways at different scales, their interface with the soil matrix,
their residence times, and their significance in different soils
and landscapes remains largely unresolved [Bouma, 1990;
Sposito and Reginato, 1992; NRC, 2001b; Lin, 2003].
Hydrologists may not have a clear picture of flow pathways
in the unsaturated zone before initiating modeling or field
experiments. Pedologists routinely document in situ pedologic features (such as clay films, ped coatings, soil structures, root distributions, macropores, and hydromorphic
features) that are indicative of preferential flow paths andhydrologic regimes. Staining techniques have also beenused to indicate flow patterns and to calculate soil hydraulicconductivity [e.g., Bouma et al., 1979]. While qualitative or
semiquantitative approaches based on whole-soil interpretations have been used successfully [Boorman et al., 1995],
a concerted effort is needed to further quantify the natural‘‘architecture’’ of soil (soil cover, soil structure, and soil
horizonation) in a manner that can be incorporated intomodels of flow and transport.
[8] 2. Where, when, and how water moves throughlandscapes and its impacts on soil processes and subsequently soil spatial patterns needs to be better understood.
Conceptual and mathematical models for water movement
through and over the landscape are key aspects of hydrologic modeling, contaminant transport, and prediction ofterrestrial ecosystem functions. However, many current
hydrologic models do poorly in predicting subsurface lateralflow and the proportion of surface vs. subsurface runoff
inputs into total streamflow [Wood, 1999]. The convergenceof surface and subsurface lateral flows within a landscaperesults in the formation and distribution of wetlands,
streams, and rivers, and contributes to the spatial heterogeneity of soils and vegetation across the landscape. Quantification of soil formation/evolution and soil spatial
distribution, including flow-restricting layers, can enhancehydrologic modeling and forecasting.
[9] 3. Bridging multiple scales remains at the heart ofmany hydrologic and pedologic studies. It is highly desirable to explore quantitative means of bridging scales frommicroscopic (e.g., pores, aggregates) to mesoscopic (e.g.,

W05301pedons, catenas) and to macroscopic (e.g., watersheds,
regional, and global) levels for different hydrologic andpedologic properties and processes. Pedologists study boththe mechanisms and the magnitudes of soil spatial diversity
as a basis for broad generalizations about soil genesis,classification, and mapping, whereas hydrologists havestudied scaling and spatiotemporal variability of hydrologic
processes. However, these two efforts have not converged.
Joint efforts of pedologists and hydrologists thus would
likely shed light on the fundamental processes upon whichscale bridging might be possible.
[10] 4. Hydrologists need soil hydraulic parameters intheir models, as well as information to specify flow paths,
but such data are often lacking or difficult to obtain in large
volumes. At the same time, many national and regional soil
survey databases developed over the last century have been
underused in addressing environmental and ecologicalissues. Improved procedures are needed to extract useful
information from the available databases and to enhancesoil survey interpretations for flow and transport characteristics in different soils and landscapes. Bridging data
gaps through approaches such as pedotransfer functions[Pachepsky and Rawls, 2004] will be continuously indemand. This will enhance the value of soil survey databases
and provide hydrologists with model input parameter
estimates. Toward that end, soil categorizations that differentiate various soil hydrologic units (e.g., in terms of flow
patterns and transport mechanisms), and quantification of soil
morphological features for inferring in situ soil hydraulicproperties and water table dynamics are essential researchareas. The combined effort of pedologists and hydrologists
will provide opportunities for developing integrated databases that are mutually beneficial.
[11] 5. Hydrologists often imply that pedologists view thesoil as a static body as they categorize soil characteristics
into nondynamic entities (such as grouping soil internal
drainage into ‘‘well’’ or ‘‘poorly’’ drained classes). This is amisperception, as pedologists generally have considerable
understanding of the implications of these terms for predicting the depth and duration of waterlogging within soils
as well as temporal changes in soil water regimes in themedium to long term. Unfortunately, this understanding is
not always clearly communicated. Water table fluctuations
in soils influence soil water storage capacity and runoff, andthus impact on such hydrologic responses as flood hydrographs, base flow, and solute concentrations in aquatic
systems. Regular temporal sampling frameworks are being
recognized in pedology, and concerted efforts from pedologists and hydrologists can lead to more complete monitoring data sets that include extreme events (e.g., sedimentor solute concentrations in peak flow or sustained drought).
A move toward continuous sampling or monitoring of thesoil zone will provide better data sets for both pedologic andhydrologic modeling.
[12] The developments in pedology and hydrology are
now converging on multiple fronts, as illustrated in theabove examples. This convergence leads to synergies thatcan be expected from integrating the two disciplines, assuggested in recent literature and professional activities[e.g., Lin, 2003; Lin et al., 2005a; Bouma, 2006; Wilding
and Lin, 2006]. We believe that integrating hydrology andpedology will enhance the understanding and prediction of

2 of 13

W05301

LIN ET AL.: OPINION

W05301Figure 1. Concepts of scales and spatial heterogeneity in pedologic and hydrologic systems:(a) Conceptual integrated-systems model in pedology [from Wilding, 2000]. (b) Scales of representationof drainage systems [from Maidment, 2002]. (c) Variability models in soil and hydrologic systems that
include (1) the classical macroscopic homogeneity (thin gray line), (2) discrete hierarchy (dashed bluelines illustrated at three levels: microscopic, mesoscopic, and macroscopic scales), (3) continuous
hierarchy (dash-dotted line), (4) the classical fractal model (thick orange line), and (5) multifractal model(thick red line) (modified from Vogel and Roth [2003]).
water fluxes and flow pathways in landscapes. Such integration also helps answer fundamental questions in unsaturated zone flow and transport, such as the prediction offlow patterns in heterogeneous and structured in situ soils
across spatial and temporal scales [Lin, 2005]. It is in such acontext that a vision of hydropedology is proposed in thispaper.
[13] Hydropedology is viewed here as an intertwined
branch of soil science and hydrology that studies theintimate relationships between soil, landscape, and hydrology. Landscape water flux is suggested as a unifying
precept for hydropedology, which encompasses the source,
storage, flux, pathway, residence time, availability, andspatiotemporal distribution of water (and the transport ofchemicals and energy by flowing water) in the soil underboth saturated and unsaturated conditions and at a range ofspatial and temporal scales. Its spatial scale ranges frommicroscopy to pedosphere (Figure 1) and its temporal scaleencompasses infinitesimal to geological timescales. Hydropedology emphasizes landscape context and in situ soils thathave distinct characteristics of pedogenic features, struc-tures, and horizons. It uses pedologic data to improve theperformance of process-based hydrologic models, and useshydrologic data to enhance the understanding of soil variability and its interpretations for soil uses or limitations.
Hydropedology may be viewed as a sister discipline ofhydrogeology, with the latter traditionally devoted to saturated systems where geological structures prevail, while theformer investigating the variably saturated soil zone where
soil structures dominate.
[14] The synergistic integration of pedology and hydrology into hydropedology suggests a renewed perspective anda more integrative approach to study landscape-soil-waterinteractions across scales, and their relationships to climate,ecosystem, land use, and contaminant fate. Working together,
we believe, hydrologists and pedologists, along with
scientists from related disciplines (such as soil physicists,hydrogeologists, hydrogeophysicists, ecohydrologists, biogeochemists, and atmospheric scientists), can better guidedata acquisition, knowledge integration, and model prediction. Similar calls for integration that include microbial andgeochemical soil processes could be included but this paper

3 of 13

W05301

LIN ET AL.: OPINION

focuses on the advantages of the integration of pedology
and hydrology.

2. Research Vision: A Framework for
Integrated Hydropedologic Studies[15] Among many unresolved scientific issues in pedology and hydrology, some are fundamental. Resolving thesekey issues can lead to significant improvements in our
understanding, measurement, modeling, and prediction ofwater fluxes across landscapes. Within the context ofhydropedology, we suggest five key issues. We present
these key issues within a holistic conceptual framework,and attempt to distill a testable hypothesis for each of theseissues that will require vigorous testing through concerted
efforts from pedologists, hydrologists, and others.
[16] Our suggested framework focuses on quantitative
relationships between soil and hydrologic structures andfunctions at different scales, which serve as the foundation
for robust models. At each scale, structure reflects spatial
arrangement; function is a result of fluxes or processes. The
two-way connection between structure and function is
dictated by scale, which also determines observable patternsin spatial variability and temporal changes in the system.
The model at each scale (or multiple scales) strives tointegrate structure and function so that the patterns anddynamics of the system can be explained and predicted. The
challenge here is to build bridges that connect different
scales. Anthropogenic forcing (such as land use and land
management) is increasingly recognized as being critical,
thus must be addressed in integrated hydropedologic studies. The intertwined components of our framework guidethe design and implementation of hydropedologic studies,
as further explained in the following.
[17] 1. One component is identification of structures:
System structure creates constraints and conditions in which
processes act and interact. Thus there is a need to identify
the interrelationships between hierarchical structures of soil
and hydrologic systems.
[18] 2. Another is characterization of functions: Acting
and interacting processes create preconditions and feedbacks that modify a system’s structure. Hence studies ofthe functions and structures of soil and hydrologic systems
need to be integrated, including, for example, integral
characterization of land units in terms of landscape waterflux.
[19] 3. Next is bridging of scales: A system’s structureand function interact at a variety of scales that define scalespecific variability and pattern. There is a need to identify
optimal methods for quantifying and communicating
important aspects of soil and hydrologic variability as afunction of scale. Scales and scaling in soils need to becorrelated with scales and scaling in hydrology.
[20] 4. A fourth component is systematic integration:
Models are indispensable tools for integrating the dominant
properties and processes at a given scale (or across multiple
scales). Soil and hydrologic variability and patterns determine the formulation and application of a suitable model.
Pedologic predictive capacity needs to be integrated withhydrologic predictive capacity.
[21] 5. A final component is human impacts: Anthropogenic influences (such as land use and management) on
soils and hydrologic systems are intimately linked. Thus

W05301soil changes and hydrologic alterations under humanimpacts need to be addressed simultaneously and be integrated in the context of the landscape or watershed.
[22] Hydrologists and pedologists use different sets oftechniques to relate structures and functions at different
spatial scales. So far, these techniques have not beencombined systematically between disciplines. In manycases, soil structure has been described in pedology without
measurements of hydrologic parameters such as the hydraulic conductivity or moisture retention functions. Soil hydrologic measurements, in turn, have often been made withlimited attention to soil structure or horizonation. Similarcomments hold for larger scales, such as a field where theprocesses and questions being raised are different. For
example, for a hillslope, a soil scientist may define a soil
sequence (catena) without paying adequate attention to thespatial pattern of hydrologic properties, whereas a hydrologist may measure hydraulic conductivity without paying
sufficient attention to distinctly different soil units. The
watershed scale poses yet another set of conditions where
geomorphologically defined soil-landscape segments, which
may contain one or many soil types and horizons, define acharacteristic structure for this scale that may be delineatedwith geophysical techniques and remote sensing. Systematic
integration of pedologic and hydrologic techniques acrossscales is likely to open new avenues for innovative andcomplementary sampling and measurement techniques thatwould contribute to the quantification of our proposed framework and to facilitate the necessary integration.
2.1. Quantification of Hierarchical Structures ofSoil and Hydrologic Systems[23] A major difficulty in modeling flow and transport insoils, irrespective of the spatial and temporal scale, is thefact that nature is structured at most or all scales. This canbe easily demonstrated by using images of soil structure
obtained at various scales from as small as soil thin sections
to as large as remotely sensed soils information fromsatellites. As a consequence, measurement will depend on
the support scale of the instrument used. To achieve the goalof characterizing and modeling functions of soil and hydrologic systems across scales, we need to find ways toquantify soil-landscape structures at various scales. Probabilistic or fuzzy logic approach could be used to discriminate between fast flow and slow flow domains forming apattern that reflects the observed structures. Identification ofsoil-landscape structures allows enhanced understanding offlow pathways and processes (e.g., preferential flow). Thus
future investments should focus on the spatial structure ofmaterial properties rather than on point measurements. This
perspective is in line with our perceived need for studying
patterns described in the next section.
[24] A relevant hypothesis to be tested is suggested asfollows.
Hypothesis 1. Soil systems and their hydrology exhibit
hierarchical structures (discrete or continuous) that canbe quantified using soil-landscape expertise, coupled with
an appropriate set of measurement techniques (noninvasive
and invasive).
[25] Strategies are needed to quantify spatial structures ofsoil and hydrologic systems at different scales. These

4 of 13

W05301

LIN ET AL.: OPINION

include advanced instruments and measurement techniquesthat are essentially noninvasive (e.g., computed tomography, geophysical tools, and remote sensing). These tools are
all sensitive to some material properties and generate proxymeasurements that can be related to those required byhydrologic models. An essential part of this is to further
develop fundamental insight into such proxy relationships
or pedotransfer functions. One important issue is to identify
critical and/or rare structural information that governs
hydrologic processes at different scales. More specifictechniques are discussed in section 3.2.
[26] Direct detection and quantification of soil andhydrologic spatial structures is typically difficult andexpensive. To make progress, we need to explicitly recognize that the observed structures are not an arbitrary
outcome of unknown random processes but the result ofstructure-forming processes that can be identified andunderstood. Hence an attractive approach is to use allavailable knowledge of soil structure-forming processes(including the distribution of aggregates within a soil andthe distribution of various soils within the landscape). Thisknowledge exists and is continuously being generated andupdated in various branches of soil science, especiallypedology. These disciplines have evolved far too separatelyfrom hydrology. Consequently, joining the soil and hydrologic sciences within a hydropedologic framework would
advance our capability to understand and predict flow andtransport processes and pathways in the field.
[27] The soil structure-forming process is tightly linked topedogenesis, an integrated phenomenon resulting from aseries of physical, chemical, and biological processes.
Pedogenesis provides a holistic view of the processes thathave occurred, or are occurring, in the soil zone in different
geographic regions under the influences of climate, organisms, geology, topography, and time (i.e., the five naturalsoil-forming factors). Besides conceptual understanding ofsoil-forming factors and processes [Jenny, 1941; Simonson,
1959], quantitative models that describe the impact ofenvironmental variables on rock/sediment weathering andsoil-geomorphology evolution over time are critically needed. Because water plays an essential role in soil formationand soil dynamic changes, pedogenesis contains valuable
information regarding hydrologic processes involved insoil-landscape evolution. Adequate understanding of quantitative soil-forming processes can lead to enhanced characterizations of soil and hydrologic hierarchical structures.
2.2. Identification and Prediction of
Functional Patterns[28] Identification and prediction of patterns, or repeatedspatiotemporal organization, across scales is becoming aleading area of research in soil science and hydrology[Grayson and Blöschl, 2000; Lin et al., 2005a]. Patterns
offer rich and comprehensive insight regarding the variability of structures and functions, as well as the underlying
processes controlling hydrologic response [Grayson et al.,
1997; Grayson et al., 2002; Lin et al., 2006]. A number ofrecent catchment-scale hydrologic field investigations havedemonstrated how the understanding and modeling ofhydrologic processes can be improved by the use ofobserved spatial patterns [Grayson and Blöschl, 2000].
Some spatial patterns are temporally persistent, the notion

W05301of ‘‘time stability’’ [Vachaud et al., 1985; Kachanoski andde Jong, 1988; Mohanty and Skaggs, 2001; Lin, 2006],
which may be a function of spatial scale and may varyacross a landscape with different soil types [Kachanoski
and de Jong, 1988; Zhang and Berndtsson, 1991; Lin,2006]. Western and Grayson [2000] found that combining
spatial patterns with temporal responses added value to bothtype of observations in a modeling context, and improved
the confidence with which the spatiotemporal organizationof soil moisture could be predicted.
[29] There is a great need for innovative characterizationand modeling of spatiotemporal patterns at different scales
that are important to pedologic and hydrologic phenomena. Such approaches will likely use a combination ofground-based observations, digital geospatial data layers(e.g., digital elevation models or DEM, surficial geology,
and land cover), noninvasive geophysical/hydrogeophysical methods (e.g., electromagnetic induction, ground-penetrating radar, radiometry), and remote sensing imagery,along with 3-D landscape-scale flow modeling. The optimal combination, integration, and assimilation of these
multiple techniques and data sources, possibly with theuse of inverse methods [Yeh and Šimunek, 2002], will
provide substantially better information regarding spatiotemporal organization of pedologic and hydrologic phenomena across scales. For example, McKenzie and Ryan
[1999] used a variety of data sources including topography, geology, climate, and airborne gamma radiometricdata as predictors of soil properties. Techniques for
combining remote sensing imagery with hydrologic models are also rapidly developing and are enabling better useof remote sensing observations at large (regional to global)
scales. Some progress has been made in using remotesensing of hydrologic response to infer soil properties andvice versa [e.g., Hollenbeck et al., 1996; Jackson and Le
Vine, 1996].
[30] A relevant hypothesis is suggested here.
Hypothesis 2. The storage, flux, pathway, and residence
time of water in the soil-landscape can be used to subdivide
landscapes into similarly functioning hydrologic units.
[31] The functional unit concept based on characterization of 4-D (3-D + time) soil units within fields allows
reliable quantification of fluxes within those fields. Hydrologically similar soil-landscape units exist within watersheds and these can be identified using traditional andnew techniques and data sources. Winter [2001] proposed
the concept of a ‘‘fundamental hydrologic landscape unit’’
as a means to divide a landscape into its most basic forms:
upland and lowland separated by a steeper slope. Each ofthese units has specific characteristics, including land surface form, geology, and climate, which then together control
its hydrology. This concept of fundamental hydrologic
landscape units has been embraced by Reed et al. [2006]
in addressing human and climate impacts on bridging river
basin scales and processes. In the context of hydropedology,soil-landscape relationships and soil hydrologic characteristics are emphasized in defining similarly functioninghydrologic units over a landscape.
[32] Note that some differences within fields, as distinguished by pedologists, do not always correspond with

5 of 13

W05301

LIN ET AL.: OPINION

hydrologic functional differences. Wösten et al. [1985]
transformed soil patterns on detailed soil maps into patternsof ‘‘functional units’’ that each had distinctly different
hydraulic conductivity and moisture retention characteristics. In doing so, the number of spatial units on the map was
reduced by 30%. Breeuwsma et al. [1986] did the same but
for cation exchange capacity and phosphorous adsorption
capacity, resulting in reductions of 20% and 30%, respectively. A more sophisticated procedure was followed by
Bouma et al. [2002] who delineated ‘‘management units’’for precision agriculture on the basis of simulation runs fornitrogen transformations and pesticide leaching for point
data, followed by interpolation. Knowing the internal variability within these ‘‘management units’’ allows estimates
to be made of the variability obtained for simulation runs forthe units.
[33] In identifying and predicting soil hydrologic patterns, soil morphology has a unique role to play. Soilmorphological attributes (such as redoximorphic features,
structure, and ped/void surface features) and their spatial
arrangement over the landscape can be used to aid indetermining dominant flow pathways and fluxes. For
example, frequency and importance of preferential pathways may be inferred using the geometry and distribution ofinterpedal pores, clay films, and worm channels. By using
staining techniques, preferential flow pattern can be further
quantified, linking to soil structure and macropore continuity and connectivity [Booltink and Bouma, 2002]. Because
soil morphology provides clues as to the hydrologic historyof a site by integrating the long-term effects of water flow
and storage in observable features of soil color and otherproperties (e.g., redox features), efforts to interpret andquantify soil morphologic data can elucidate hydrologic
patterns (such as seasonal high water table and soil drainage). It is encouraging that some preliminary attempts havebeen made to hydrologically classify soils and to predictwater movement through different soils and substrates[Quisenberry et al., 1993; Boorman et al., 1995; Lilly etal., 1998]. However, a more comprehensive and quantitative approach to grouping hydrologically similar soil types
across scales is needed.
[34] We realize that predicting preferential flow from soil
morphologic information is still in its infancy, and that
preferential flow may be caused by a multitude of processes,
including some that are not immediately evident fromclassic pedologic studies (e.g., unstable flow). One of the
key issues is the need to quantify soil morphology, including pore structure, in a manner that provides direct information for inclusion in hydrologic models. We envisage theappearance of innovative methods for quantifying in situ
soil morphology and soil structure and then linking suchinformation to hydrologic processes/properties in a quantitative manner.
2.3. Bridging Multiple Scales[35] Translating information about soil and hydrologic
properties and processes across scales has emerged as amajor theme in contemporary soil science and hydrology[Kalma and Sivapalan, 1995; Sposito, 1998; Hoosbeek etal., 1998; Western et al., 2002; Pachepsky et al., 2003]. As
remote sensing techniques for estimating large-area soil andhydrologic properties and in situ measurements for local
areas continue to be developed, bridging multiple scales

W05301

becomes even more essential. At present, no single theory
exists that is suitable for spatial aggregation (or upscaling),
disaggreagation (or downscaling), and temporal inference(or prediction) of soils and hydrologic information. The
major complementary approaches include scaling via defined hierarchies, and continuous models of spatial variation
as described by fractal theory and geostatistics [Lin andRathbun, 2003; Pachepsky et al., 2003]. Further explorationof this topic is critical.
[36] Hierarchical frameworks have been conceptualized
by pedologists as a means for organizing soil systems fromthe soil pore scale to the global pedosphere (Figure 1)[Hoosbeek and Bryant, 1992; Wilding, 2000]. Hierarchicalcomplexity has been studied in pedology, which has longrecognized self-organized complexity in the processes ofsoil formation, with taxonomic frameworks constructed tosummarize that ordering [Buol et al., 2001]. However, aquantitative hierarchy of soil systems that could be integrated into models of flow, scaling, and rate processes isstill lacking. Sommer et al. [2003] recently presented an
integrated method for soil-landscape analysis, in which ahierarchical expert system was developed for multidata
fusion of inquires, relief analysis, geophysical measurements, and remote sensing data, as well as a combinationof the soil-forming factorial model of Jenny [1941] with thescaleway approach of Vogel and Roth [2003] to address soil
variability across scales.
[37] There are several approaches in hydrology and soil
physics to incorporate spatial heterogeneity into flow andtransport modeling, including macroscopic homogeneity,
discrete hierarchy, continuous hierarchy, and fractals(Figure 1). Vogel and Roth [2003] suggested a ‘‘scaleway’’
approach for predictive modeling of flow and transport in
the subsurface at any scale. Their conceptual approach isbased on the explicit consideration of spatial structure that isassumed to be present at any scale of interest, where themicroscopic heterogeneities may be replaced by an averaged, effective description. Some studies [Cushman, 1990;
Bouma, 1992; Vogel et al., 2002] have suggested a discrete
hierarchy of the representative elementary volume (REV),
where the REV is a local property related to a given level ofsoil structural unit. This is consistent with the hierarchicalorganization of soil aggregates that is characteristic of mostsoils [Tisdall and Oades, 1982; Oades and Waters, 1991].
However, quantification of soil structure and its impacts onflow and transport in field soils remains unresolved. A
versatile geometric foundation for representing porous
media (e.g., fractal geometry and percolation theory) is
emerging as one of the possibilities for achieving improvements in media scaling, flow modeling, and soil hydraulicfunction characterization [Crawford et al., 1999; Jury, 1999;
Gerke and van Genuchten, 1996; Hunt, 2005]. Further
progress requires joint efforts of pedologists, hydrologists,
mathematicians, and related discipline scientists.
[38] A hypothesis to be tested is suggested here.
Hypothesis 3. Scale dependence in hydrologic parameters
can be explained using hierarchical structures in soils,
landforms, and land cover.
[39 ] Changing scales in soil and landscape studies
involve changes in the type of information obtained about

6 of 13

W05301

LIN ET AL.: OPINION

the system, in parameters used to characterize the system, inthe system’s variability, and in the observability and predictability of the system. Accordingly, a scale transition
generally includes a change in preferred hydrologic flow
model (e.g., Navier-Stokes ! Richards’ equation ! watermass balance equation) and a change in structure characterization in pedology (e.g., aggregate structure ! profile
structure ! soil-landscape structure). It is the system’s scalethat determines the processes or ‘‘physics’’ involved, andonly when the physics is understood can a suitable flow
model be developed and the relevant material properties be
identified. Faybishenko et al. [2003] suggested a triadic
approach to scaling in which material properties from thefiner scale are used to estimate model parameters forthe scale in question, whereas system properties from thecoarser scale are used to establish constraints for model
behavior. An exhaustive characterization of structure at eachscale should describe rare structural features that, in actuality, may define the hydraulic behavior at the coarser scale[Pachepsky et al., 2004]. Connected macropores that are
rare at the soil horizon scale presents an example of such a
feature since they define soil hydraulic behavior under highwater content at the soil profile scale. Scale-specific delineation of rare structural features and characterization of theirhydrologic role requires a concerted effort from pedologists
and hydrologists.
[40] Hierarchy theory in ecology [O’Neill et al., 1986,
1989] presents some valuable philosophical and operational
concepts pertaining to the quantification of hierarchicalstructures of soil and hydrologic systems [Haigh, 1987;
Wagenet, 1998; Lin et al., 2005a]. If properly constructed, ahierarchy of soil systems should reflect logical links andquantitative relationships among scales. It can be argued,
however, that the hierarchy of scales as used by soil
scientists is often more of an operational or observational
device based on the ability or feasibility to measure theproperties involved, rather than reflecting fundamental
differences in the basic processes [Wagenet, 1998]. Hierarchy theory in ecology defines ‘‘holons,’’ which are nested
spatial units characterized by means of integrated biological, physical, and chemical processes [Haigh, 1987]. In
comparison, soil science uses entities that are generally lesswell defined and procedures that are less well integrated.
Further studies in this area of research are worthwhile.
2.4. Integrated Models and Databases[41] Many current hydrologic models are either ‘‘toogood to be real’’ or ‘‘too real to be good.’’ In the first case,oversimplification compromises the accuracy or generalityof the results. In the second case, the need for detailed input
data renders the model impractical to apply except in a
research setting. Compromises between the quest for perfection and the complex reality, compounded by our limited
knowledge, available modeling technology, and/or suitable
data, plus natural uncertainty, are facts of life. The notionsof multiplicity and site specificity of hydrologic models are
now gaining evidence and acceptance in hydrology [Beven,2000]. It is therefore best to consider a broad range ofreasonable alternative hypotheses and to base the model ona variety of different types of data [NRC, 2001b]. Armedwith advances in categorizing soil-landscape relationships
and cataloging existing structures, pedology has a potential

W05301to contribute substantially to building a range of hypotheses
that should be considered in hydrologic modeling. Needs ofhydrologic modeling, in turn, may catalyze efforts onorganizing available soils information in forms that are
more relevant to hydrologic modeling needs. Pedology has
already provided a spectrum of pedotransfer functions to be
used in hydrologic model parameterizations [Pachepsky andRawls, 2004]. More can be expected as information on soil
structure and landscape features are being incorporated into
pedotransfer functions [Rawls and Pachepsky, 2002; Lillyand Lin, 2004; Lin et al., 2005a]. In addition, as theimportance of prior model parameter estimates along withposterior estimates from calibration becomes recognized,
soil-landscape databases and pedotransfer functions canserve as useful sources of prior estimates. The need to use abroad range of data warrants efforts in developing a quantitative framework for linking soil hydrology to climatic,
pedologic, topographic, and vegetative processes and forlinking data collected at different scales of spatial support.
Data assimilation and data fusion may improve the operational use of hydrologic models at large scales by supporting
model testing, verification, and refinement.
[42] A relevant hypothesis is thus suggested here.
Hypothesis 4. Soil-landscape relationships can improve theaccuracy and the reliability of pedotransfer functions andhydrologic model predictions at the landscape level.
[43] The most important step in any modeling application isto determine what is important to system behavior. In modeling catchment response, determining the dominant processes
and flow pathways that are responsible for controllinghydrologic response at different space and time scales enables
development of appropriate conceptual models that then forma basis for quantitative simulations of system response.
Mixtures of different processes control hydrologic responses
in different landscapes. Hydrologic modelers at present often
struggle to determine what the dominant processes and flow
paths are in a particular landscape, unless these have beenstudied in detail. However, we believe that there is great
potential to improve predictions through more innovative useof soil survey data, and through certain modifications of thebase data being collected during soil surveys. It is important tonote that, as interests shift to issues involving the transport ofsolutes and sediments driven by water flow, the hydrologic
models need to predict first and foremost the flow paths
correctly and then the associated fluxes, i.e., they must be
accurate for the right reasons, something that is not necessarily needed for acceptable predictions of integrated catchment
runoff at the watershed scale.
[44] Another area where modelers are challenged is indeveloping system descriptions that work well across scales[Beven, 2002]. This is partly because different processes
become dominant at different scales, partly because thedetail of information available typically decreases as one
moves up in scale, and partly because the level of detail thatcan be represented reduces at larger scales due to thepragmatic constraint of computing. These effects have anumber of implications that complicate modeling. At microscales, water flow is controlled by capillarity and laminarflow through individual pores and around peds. As scale
increases, flow often becomes controlled by impeding

7 of 13

W05301

LIN ET AL.: OPINION

layers in a soil profile, then accumulation of water downslope leading to surface saturation, and finally routing offlow through a stream network. This change in the dominant
process with scale implies that the model structures changewith the scale of application. Data availability is alsochanging with scales. It is possible to characterize individual pores in a thin section but not yet for an entire soil
profile. Likewise, it is possible to characterize a soil profile
in detail in a soil pit, but it is not yet possible to characterize
the 3-D soil entity to the same level of detail, even for a
first-order catchment. This is fundamental since properties
are spatially variable. This means that some form of averageor statistical representation of small-scale detail may berequired in models, even if the model resolves the systemwith a fine numerical grid in space and time. Although
limits on computing power will become less a concern inthe next decade or so, larger spatial and temporal units are
generally used for larger scale models. An implication ofthis is that clever algorithms are required that capture theeffects of unresolved or unrepresented small-scale processes,
spatiotemporal variability of these processes, and the nonlinearities that typify environmental processes [Vander
Kwaak and Loague, 2001; Panday and Huyakorn, 2004].
[45] Besides the complexity of spatial scale, we alsostress the critical importance of temporal dimension. The
timescales over which soil and hydrologic processes occur
range from milliseconds for soil chemical reactions to
decades or longer for transport of solutes to groundwater,
with some processes occurring only sporadically and alsochanging under different conditions or seasons. In addition,
there is often a disjunction between soil and land use
interactions and the subsequent impacts on aquatic systems.
For example, nitrate is leached from soils in temperate
agricultural systems largely during the winter but the
impacts on aquatic ecology are often seen in the summer
at some distance away from the original source [Ferrier and
Edwards, 2002]. Therefore measurement frequency must bealigned to the temporal variability and the structure (e.g.,
runoff events) inherent in pedologic and hydrologic processes. An adequate understanding and appropriate representation of temporal variability, the scales over which
different processes operate, and the disassociations between
sources and impacts, is vital to the development of robustmodels that can simulate hydropedologic processes, watershed response, and environmental dynamics. Analogous to
the REV, perhaps a concept of ‘‘representative elementary
time step’’ might be explored for characterizing temporalvariability of pedologic and hydrologic phenomena.
[46] Associated with modeling and prediction is theobvious need of integrated databases that are consistentand interoperable. Soil survey databases provide a wealth of
information that hydrologists could utilize for various
applications. These databases can be potentially utilized
in the development of pedotransfer rules and functions,hydrologic grouping or classification of soils, and testingof hydrologic models. However, it is also important torecognize that traditional soil survey databases do notcontain much information on dynamic soil properties
required for deriving reliable pedotransfer functions (except
perhaps for specific retention points such as wilting point
or field capacity). Most data in traditional soil survey
databases have been collected at a window in time. Hence,

W05301to enhance the value of classical soil survey databases andto facilitate the integration of pedology and hydrology,
concerted efforts are needed to develop landscape-based
new generations of pedotransfer functions. We believe thathydropedology offers a useful framework for bridging
traditional soil survey and future databases of dynamic soil
properties through incorporating specific information on
soil structure, horizonation, landform, and land use.
2.5. Human Impacts and the Concepts of Soil‘‘Genoform’’ and ‘‘Phenoform’’[47] With increasing emphasis on human impacts andland management practices, the dynamics of soil andhydrologic properties requires more attention in hydropedology. Anthropogenic influences on soils have resulted in
distinct characteristics that can be used to classify andmodel naturally formed soils under different land management scenarios. The concepts of ‘‘genoform’’ (for genetically defined soil series) and ‘‘phenoform’’ (for soil types
resulting from a particular form of management in a given
genoform) [Droogers and Bouma, 1997] facilitate theincorporation of management effects into pedologic andhydrologic characterizations and could potentially enhancepedotransfer functions that involve soil series and land use
classifications as carriers of soil hydraulic information[Pulleman et al., 2000; Sonneveld et al., 2002]. The
distinction between major soil management types (phenoform) within the same soil series (genoform) separates themorphogenetic properties used in soil taxonomic units fromnear surface temporally dynamic properties used in cartographic units delineating management driven effects.
[48] Grossman et al. [2001] also suggested use-dependant
properties as those soil properties that show change andrespond to soil use and management (such as soil organic
matter levels and aggregate stability), and use-invariant
properties as those soil properties inherent from naturalsoil-forming processes that show little change over timeand are not affected much by soil use and management(such as mineralogy and particle size distribution). Usedependent properties are mostly evident in surface soils.
[49] A possible hypothesis in this area of research is asfollows.
Hypothesis 5. The concepts of ‘‘genoform’’ and ‘‘phenoform’’
combined with pedotransfer functions for separate soil horizons can improve the efficacy of soil series and land use
classifications as carriers of soil hydraulic information under
different human impacts.
[50] Any given soil can be changed significantly by land
use and management practices, even though soil classification remains the same. For example, Droogers and Bouma[1997] studied a prime agricultural soil in the Netherlands
and found that the organic matter content of a conventionally tilled variant had significantly decreased as comparedwith a variant subject to organic farming. Also, grassland
had even higher organic matter contents even though soil
classifications of these three phenoforms were identical.
Pulleman et al. [2000] showed that organic matter contentsof these phenoforms could be predicted by regression
analysis as a function of previous land use. Modeling crop
growth and nitrate leaching to the groundwater yielded

8 of 13

W05301

LIN ET AL.: OPINION

W05301[53] It is also important to recognize that adequate andeffective communication with scientists in other disciplines,
the general public, stakeholders, educationalists, and policy
makers is needed to advance hydropedology and to demonstrate its unique strengths and practical applicability. For
example, early engagement with multidisciplinary scientists
and stakeholders and throughout the length of a project ismore likely to lead to an acceptance and implementation ofproject results. This may be particularly crucial in parts of
the world where there is a stronger affinity with soil andwater resources [Bouma, 2001a, 2001b, 2003, 2005].

Figure 2. Schematic of the iterative loop and interactions
between the strategies for achieving the research vision
proposed in this paper.
significantly different results for these phenoforms. This
concept addresses one of the main limitations of soil mapsthat show genetic and static classification of soils, i.e., soils
with very different properties caused by different management histories are classified as the same soil type on the basisof natural soil-forming factors. Pedotransfer functions often
use organic matter content and bulk density as input parameters, which can vary significantly among different phenoforms. Distinguishing different phenoforms for a given
genoform (or soil series) can refine the dynamic characterization of soils and pedotransfer functions under different
human impacts, which will undoubtedly enhance hydrologic
modeling and prediction. Sonneveld et al. [2002] made suchan analysis for a major sandy soil in the Netherlands.

3. Strategies for Achieving the Vision:
A Global Perspective[51] We suggest some interlinked strategies for achieving
the above stated vision, including (1) design of a set ofscientific experiments to test the proposed hypotheses and(2) use of hydrologic observatories and natural soil laboratories for systematic field data collection and synthesis(Figure 2).
[52] We would like to point out that, while devising moredetailed experimental and modeling work as proposed in thefollowing, a useful first step in hydropedology is to link
existing soil and hydrologic data (such as grouping hydrologically similar soil units and enhancing pedotransfer
functions) to demonstrate the applicability of soils data tohydrologic research. This will be enhanced by case studies
on the use of pedologic data for improved hydrologic
applications (such as knowledge of flow pathways at thepedon and field scales and of hydrologic responses at thewatershed scale) [e.g., Quisenberry et al., 1993; Boorman etal., 1995; Lilly et al., 1998; Dunn and Lilly, 2001]. The
knowledge gaps thus identified can then be used to guidemore detailed fundamental research.

3.1. Design and Implementation of a Systematic Set of
Scientific Experiments to Test the Proposed Hypotheses[54] Designing a set of scientific experiments that can test
the suggested hypotheses is a logical step in achieving the
proposed research vision. The suggested hypotheses can beused to guide the design and implementation of such
experiments. We believe that these experiments are best
conducted in a coordinated fashion among international soil
and hydrologic communities in order to maximize results. A
variety of factors including infrastructure support (such ashydrologic observatories), available funding, cutting-edge
instruments, innovative techniques, integrated databases,
and multidisciplinary communications are all integral partof successful community-based science.
[55] Much progress has been made in 1-, 2-, and 3-Dmodeling, in terms of integrating various processes atdifferent scales and incorporating preferential flow dynamics [van Genuchten and Šimunek, 2004], but much remains
to be done, especially in bridging scales, deriving inputs,
quantifying uncertainties, and integrating processes from asystems perspective. Integrated models are the future, where
overland flow (including rivers and lakes), ecohydrology,
vadose zone flow and transport, and groundwater hydrology
are tightly coupled. Running models of this type for various
applications will help in deciding which hypotheses arefundamental and what data are critical.
[56] To make a quantum leap, we need to embrace a holistic
framework such as the one discussed in section 2 and to findways to build comprehensive data sets for quantifying such aframework, as further explored in section 3.2. Beyond
problem-solving needs, we have to address fundamental
concepts and first principles involved in landscape to river
basin hydrology, and to take full advantage of signatory
information recorded in soils (such as soil hydromorphology
and pedogenesis related to hydrology). One way to follow
such a path is to advance our understanding of a system’s
logical connections such as ‘‘structure-physics-flow model’’and the related five critical issues as discussed in section 2.
3.2. Use of Hydrologic Observatories and Natural
Soil Laboratories for Multiscale, Multidisciplinary,and Long-Term Field Data Collection and Synthesis[57] One critical need for advancing hydropedology is a
network of well-designed and carefully maintained experimental watersheds or natural laboratories across a wide
range of geographic regions for systematic (in both space
and time) field data collection. The soil science and hydrology communities have long recognized the fundamental
need for multiscale, multidisciplinary, and long-term fielddata collection and synthesis, including better archiving andsharing of field data across geographic regions [e.g., NRC,

9 of 13

W05301

LIN ET AL.: OPINION

1999, 2001a]. Although there have been various field
experimental networks in the United States (such as theexperimental watersheds of the USDA Agricultural
Research Services, the large river basin gauging stationsof the U.S. Geological Survey, the pollution monitoringsites of the U.S. Department of Energy, and the experimental stations of many land grant universities), better integration and coordination across agencies, disciplines, andscales is needed to address ‘‘big’’ science questions. In thecontext of hydropedology, we feel the need for better
coordinated and integrated studies to develop principles
governing the relationships between soil, landscape, andhydrology at a range of spatial and temporal scales.
[58] We note that an iterative loop of ‘‘understanding,sampling, and modeling’’ is essential to integrated hydropedologic studies. It is important to point out the need forprocess-based modeling in conjunction with the data gathering. Data are only valuable if they are used, not just by users
or policy makers, but even more so by peer scientists
including those interested in advancing hydrologic modeling.
Future modeling needs could provide both justifications andguidelines for all the measurements to be made, including‘‘what, where, and when’’ data should be collected, at what
resolution, for how long, and for what purpose. Systematicfield data collections must contribute to enhanced understanding at a variety of scales and to the advancement of
quantitative modeling and prediction.
[59] In carrying out field data collection, commonly
accepted and adhered to protocols must be adopted andutilized in hydrologic observatories and natural soil laboratories across geographic regions. Contrasting soil-landscapes and experimental watersheds of different geology,
climate, and land uses need to be considered. Hydropedology can contribute uniquely in this regard in ways that
include (1) the use of the state-of-the-art techniques in soil
mapping, vadose zone monitoring, and variably saturated
modeling and (2) attention to field soil morphology and soil
distribution patterns to guide the selection of monitoringsites, optimal experimental designs, interpretations andsynthesis of experimental data, and flow and transportmodeling. We believe that a minimum set of hydropedologic data should be collected in order to characterize acatchment and its hydrologic flux dynamics. Determinationof such a core data set will be guided by the scientifichypotheses to be tested, interpretation techniques required
for the collected data set, improvements for quantitativemodeling and prediction, as well as the needs for the overall
integrated monitoring network. Research conducted athydrologic observatories and natural soil laboratories will
also allow for the testing and refinement of pedometricapproaches to mapping critical hydropedologic variables
and scaling pedon data to the landscape scale.
[60] Cutting-edge instrumentation and innovative measurement techniques are integral part of hydrologic observatories and natural soil laboratories, such as (1) networked
arrays of state-of-the-art sensors (ground-, air-, and spacebased platforms) for measuring states and fluxes at point,
hillslope, catchment, and regional scales and at critical time
steps (e.g., in situ soil moisture sensors, optical and microwave imagery, radiometry, hyperspectral imagery, andothers); (2) a suite of noninvasive tools (e.g., computed
tomography, geophysical tools, and remote sensing) for

W05301characterizing soil-landscape structures across multiple
scales and for linking such structural information to water
fluxes in a block of land of various sizes; (3) enhanced tools
and methods for measuring and representing landscape-scalesoil hydraulic parameters such as soil hydraulic functions;(4) advanced soil and landscape mapping that captures spatial
and temporal patterns of hydropedologic properties andprocesses; and (5) a set of natural and anthropogenic tracers
for tracking the movement of water through landscapes andriver basins.
[61] To advance hydropedology and hydrologic modeling, we need new ways of mapping soils and landscapefeatures in greater detail and with higher precision. Several
important issues need to be addressed in this regard.
[62] 1. Traditional soil maps have been created using
conceptual models of soil formation modified to suit local
conditions [Dijkerman, 1974; Soil Survey Division Staff,
1993], resulting in qualitative models that discretize the soil
continuum [Hudson, 1992; Cook et al., 1996]. The classicaldistinction of sharply bounded soil units on soil maps is notrealistic as soil boundaries tend to be more gradual. This
realization is essential for flow processes. Even though
work has been done with fuzzy sets to create gradual soil
boundaries, this has yet to be translated into hydrologic
characteristics used for process modeling. Proper use ofexisting soil maps also requires adequate understanding of
map scale and within-map-unit variability. Quantificationof map unit purity for different scales of soil maps and its
applicability for hydrologic modeling is an area needing
improvements in modern soil surveys [Arnold and Wilding,1991; Lin et al., 2005a, 2005b]. With the emergence ofquantitative pedologic measurements and modeling techniques [e.g., McBratney et al., 2000; Heuvelink and Webster,
2001; McBratney et al., 2003], quantitative models of soillandscape relationships are expected to improve hydrologic
modeling. These include environmental correlation modeling [McSweeney et al., 1994; McKenzie and Ryan, 1999;
Park and Vlek, 2002] or landscape-guided soil mapping[Heuvelink and Webster, 2001], and combined use of GIS,
expert knowledge, and fuzzy logic [Zhu et al., 2001].
[63] 2. Soil maps can no longer be static documents.
Rather, derivative maps created for specific purposes orfunctions, and dynamic maps reflecting changes caused byland use and management, must be generated or updated
from original soil maps and tailored to particular applications. Thus pedotransfer functions, in combination withcomputer models and geospatial data layers, need to be
integrated into expert systems to derive such maps. Thusfar, there has been a conspicuous lack of appropriate meansof producing derivative and dynamic soil maps such as soil
hydraulic properties through space and time.
[64] 3. Even though high-resolution DEM are widely
available, they cannot as yet be matched well with theunderlying soil characteristics that determine, together withthe vegetation, infiltration and runoff. A well-defined matchneeds to be established between hydrologic units being
considered and the corresponding soil and landscape data
sets. New techniques using various types of geophysicaltools and remote sensing are needed to fill this gap.
However, although remote sensing techniques offer significant opportunities to infer the state of soils and theirproperties, remote sensing signals are typically only sensi-

10 of 13

W05301

LIN ET AL.: OPINION

tive to a very limited surface depth. This surface information needs to be linked to a reliable model of soil-landscaperelationships to infer subsoil properties and to project point
observations to landscape scales. Extending sensor capabilities to gain more vertical information for describing the 3-D
nature of the soil would be desirable. Pedotransfer functions
utilizing remote sensing and geophysical inputs could also
be used to assist in reducing the degree of freedom inhydrologic models (thus reducing the uncertainty) by providing hydropedologic constraints and patterns that areeither quantitative or qualitative. Correlating geophysicalsignals with soil profile characteristics (such as restricting
layers, soil structures, and soil moisture) as well as soil map
units can greatly enhance the development of proxy relationships needed in hydrogeophysics.
[65] There is a clear need for integrating knowledge,
databases, and models to address forcing, feedbacks andcoupling, and to ensure appropriate spatial coverage, temporal frequency, and data resolution. Besides data models[e.g., Maidment, 2002], an integrated process-based modeling system is also needed that allows systematic examination of various processes at different scales. In doing so,code standards (such as algorithm transferability, modularization, and object-oriented design) and intercode comparison (especially against field data collected in experimental
watersheds and natural soil laboratories) need to be considered [Šimunek et al., 2003]. We recognize the need forbalancing standardizations and innovations in both fielddata collections and modeling system developments so as toprovide common protocols for data gathering and sharing
and model comparison while not constraining new
approaches to data collection and modeling. Innovativesyntheses of hydrologic and pedologic data with newtools/methods for data mining and knowledge discovery
are also important for advancing hydropedology.

4. Conclusion[66] Hydropedology is a timely addition in this exciting
era of interdisciplinary and systems approaches for developing a comprehensive prioritization of science and its
applications in the hydrologic sciences. Hydropedology
proposes to realign geology-rooted classical pedology witha hydrology-driven approach based on a landscape perspective, reflecting the crucial role of water in wide array of
issues. Hydropedology focuses on the interface between thehydrosphere and the pedosphere and emphasizes flow andtransport processes in field soils as they occur in thelandscape. We believe hydropedology is a promising direction for the future of pedology as it adds quantitativehydrologic and soil physical information to classical pedology, including (1) measuring rather than estimating soil
moisture regimes and water fluxes, (2) improving soillandscape modeling and soil mapping through appropriate
attention to landscape water fluxes, (3) quantifying soil
drainage classes and modeling soil dynamic changes under
different land uses and managements, and (4) making soils
databases more relevant and reliable for hydrologic modeling. On the other hand, pedology can make a uniquecontribution to enhance the understanding and predictionof landscape water flux, including (1) providing better dataon soils and water flow pathways (e.g., those related to soillandscape structures, preferential flow, and lateral fluxes overW05301

slowly permeable soil horizons), (2) enhancing the understanding of mechanisms and magnitudes of soil spatiotemporal variability (e.g., soil spatial diversity as a function ofsoil-forming factors and processes), (3) improving the quantification of structural hierarchies and identifying patterns ofsoil and hydrologic systems (e.g., quantitative soil-landscaperelationships and soil hydrologic units as portrayed by soil
maps of various scales), and (4) enhancing model structure
formulation and selection of suitable models for hydrologic
predictions. Integrating pedology and hydrology into hydropedology will make an important contribution to the advancement of the hydrologic sciences as well as soil sciencein the next decade and beyond.
[67] Acknowledgments. Funding provided by the Consortium of
Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI),
through the support from the National Science Foundation, has made thisopinion paper possible. Members of our initial team of Hydropedology
and the Earth’s Critical Zone included: Lawrence Band, Johan Bouma,
Kristofor Brye, Christopher Duffy, James Famiglietti, Rodger Grayson,
Allan Lilly, Henry Lin, Alex McBratney, Yakov Pachepsky, James
Thompson, Scott Tyler, M. T. van Genuchten, Michael Vepraskas, HansJörg Vogel, and Andrew Western. The interest and support of all our initialteam members are greatly appreciated. Valuable review comments provided
by Andrew Binley and two anonymous reviewers significantly enhanced
the quality of this paper.

References
American Association for the Advancement of Science Council (2001),
Report of the 2001 AAAS Council meeting, Science, 292, 2509.
Arnold, R. W., and L. P. Wilding (1991), The need to quantify spatial variability, in Spatial Variabilities of Soils and Landforms, edited by M. J.
Mausbach and L. P. Wilding, Spec. Publ. Soil. Sci. Soc. of Am., 28, 1 – 8.
Beven, K. (2000), Uniqueness of place and nonuniqueness of models inassessive predictive uncertainty, in Computational Methods in Water
Resources XIII, edited by L. R. Bentley et al., pp. 785 – 792, A. A.
Balkema, Brookfield, Vt.
Beven, K. (2002), Towards an alternative blueprint for a physically based
digitally simulated hydrologic response modeling system, Hydrol. Processes, 16, 189 – 206.
Blöschl, G., and R. Grayson (2000), Spatial observations and interpolation,in Spatial Patterns in Catchment Hydrology: Observations and Modeling, edited by R. Grayson and G. Blöschl, pp. 17 – 50, Cambridge Univ.
Press, New York.
Booltink, H. W. G., and J. Bouma (2002), Suction crust infiltrometer andbypass flow, in Methods of Soil Analysis, part 4, Physical Methods,edited by J. H. Dane and G. C. Topp, pp. 812 – 815, Soil Sci. Soc. ofAm., Madison, Wis.
Boorman, D. B., J. M. Hollis, and A. Lilly (1995), Hydrology of soil types:
A hydrologically-based classification of the soils of the United Kingdom,
Rep. 126, Inst. of Hydrol., Wallingford, U. K.
Bouma, J. (1990), Using morphometric expressions for macropores toimprove soil physical analyses of field soils, Geoderma, 46, 3 – 13.
Bouma, J. (1992), Effect of soil structure, tillage and aggregation upon soil
hydraulic properties, in Interacting Processes in Soil Science, edited by
R. S. Wagenet, P. Baveye, and B. A. Stewart, pp. 1 – 37, Lewis, Boca
Raton, Fla.
Bouma, J. (2001a), The role of soil science in the land negotiation process,Soil Use Manage., 17, 1 – 6.
Bouma, J. (2001b), The new role of soil science in a network society, Soil
Sci., 166, 874 – 879.
Bouma, J. (2003), The role of research when implementing European
environmental legislation at the national level, Neth. J. Agric. Sci., 50,
267 – 275.
Bouma, J. (2005), Soil scientists in a changing world, Adv. Agron., 88, 67– 96.
Bouma, J. (2006), Hydropedology as a powerful tool to environmental
policy research, Geoderma, 131, 275 – 286.
Bouma, J., A. Jongerius, and D. Schoonderbeek (1979), Calculation of thesaturated hydraulic conductivity of some pedal clay soils using micromorphometric data, Soil Sci. Soc. Am. J., 43, 261 – 264.
Bouma, J., B. J. van Alphen, and J. J. Stoorvogel (2002), Fine tuning water
quality regulations in agriculture to soil differences, Environ. Sci. Policy,
5, 113 – 120.

11 of 13

W05301

LIN ET AL.: OPINION

Breeuwsma, A., J. H. M. Wösten, J. J. Vleeshouwer, A. M. van Slobbe, andJ. Bouma (1986), Derivation of land qualities to assess environmental
problems from soil surveys, Soil Sci. Soc. Am. J., 50, 186 – 190.
Buol, S. W., R. J. Southard, R. C. Graham, and P. A. McDaniel (2001), Soil
Genesis and Classification, 5th ed., Iowa State Univ. Press, Ames.
Cook, S. E., R. J. Corner, G. Grealish, P. E. Gessler, and C. J. Chartes(1996), A rule-based system to map soil properties, Soil Sci. Soc. Am. J.,
60, 1893 – 1900.
Crawford, J. W., Y. A. Pachepsky, and W. J. Rawls (1999), Integratingprocesses in soils using fractal models, Geoderma, 88, 103 – 109.
Cushman, J. H. (1990), An introduction to hierarchical porous media, inDynamics of Fluids in Hierarchical Porous Media, edited by J. H.
Cushman, pp. 1 – 6, Elsevier, New York.
Dijkerman, J. C. (1974), Pedology as a science: The role of data, models
and theories in the study of natural soil systems, Geoderma, 11, 73 – 93.
Droogers, P., and J. Bouma (1997), Soil survey input in exploratorymodeling of sustainable soil management practices, Soil Sci. Soc. Am.
J., 61, 1704 – 1710.
Dunn, S. M., and A. Lilly (2001), Investigating the relationship between asoils classification and the spatial parameters of a conceptual catchmentscale hydrological model, J. Hydrol., 252, 157 – 173.
European Confederation of Soil Science Societies (2004), Scientific Basisfor the Management of European Soil Resources: Research Agenda,
Guthman-Peterson, Vienna.
Faybishenko, B. A., G. S. Bodvarsson, J. Hinds, and P. A. Witherspoon(2003), Scaling and hierarchy of models for flow processes in unsaturated
fractured rock, in Scaling Methods in Soil Physics, Y. A. Pachepsky,
D. E. Radcliffe, and H. M. Selim, pp. 373 – 417, CRC Press, Boca
Raton, Fla.
Ferrier, R. C., and A. C. Edwards (2002), Sustainability of Scottish water
quality in the early 21st century, Sci. Total Environ., 294, 57 – 71.
Gerke, H. H., and M. T. van Genuchten (1996), Macroscopic representationof structural geometry for simulating water and solute movement in dualporosity media, Adv. Water. Resour., 19, 343 – 357.
Grayson, R., and G. Blöschl (Eds.) (2000), Spatial Pattern in Catchment
Hydrology: Observations and Modeling, Cambridge Univ. Press, New York.
Grayson, R. B., A. W. Western, F. H. S. Chiew, and G. Blöschl (1997),
Preferred states in spatial soil moisture patterns: Local and non-local
controls, Water Resour. Res., 33, 2897 – 2908.
Grayson, R., G. Blöschl, A. W. Western, and T. A. McMahon (2002),
Advances in the use of observed spatial patterns of catchment hydrological response, Adv. Water Res., 25, 1313 – 1334.
Grossman, R. B., D. S. Harms, C. A. Seybold, and J. E. Herrick (2001),
Coupling use-dependent and use-invariant data for soil quality evaluationin the United States, J. Soil Water Conserv., 56, 63 – 68.
Haigh, M. J. (1987), The holon: Hierarchy theory and landscape research,
Catena, 10, 181 – 192.
Heuvelink, G. B. M., and R. Webster (2001), Modelling soil variation: Past,
present, and future, Geoderma, 100, 269 – 301.
Hollenbeck, K. J., T. J. Schmugge, G. M. Hornberger, and J. R. Wang
(1996), Identifying soil hydraulic heterogeneity by detection of relativechange in passive microwave remote sensing observations, Water Resour.
Res., 32, 139 – 148.
Hoosbeek, M. R., and R. B. Bryant (1992), Towards the quantitative modeling of pedogenesis - A review, Geoderma, 55, 183 – 210.
Hoosbeek, M. R., H. M. van Es, and A. Stein (Eds.) (1998), Special issue:
Modeling spatial and temporal variability as a function of scale, Geoderma,
85, 111 – 254.
Hornberger, G. M., J. P. Raffensperger, P. L. Wiberg, and K. N. Eshleman(1998), Elements of Physical Hydrology, Johns Hopkins Univ. Press,
Baltimore, Md.
Hudson, B. D. (1992), The soil survey as paradigm-based science, Soil Sci.
Soc. Am. J., 56, 836 – 841.
Hunt, A. (2005), Percolation Theory for Flow in Porous Media, Springer,
New York.
Jackson, T. J., and D. E. Le Vine (1996), Mapping surface soil moistureusing an aircraft-based passive instrument: Algorithm and example,
J. Hydrol., 18, 85 – 99.
Jenny, H. (1941), Factors of Soil Formation: A System of Quantitative
Pedology, McGraw-Hill, New York.
Jury, W. A. (1999), Present directions and future research in vadose zonehydrology, in Vadose Zone Hydrology: Cutting Across Disciplines, editedby M. B. Parlange and J. W. Hopmans, pp. 433 – 441, Oxford University
Press, New York.
Kachanoski, R. G., and E. de Jong (1988), Scale dependence and thetemporal persistence of spatial patterns of soil water storage, Water
Resour. Res., 24, 85 – 91.

W05301

Kalma, J. D., and M. Sivapalan (Eds.) (1995), Scale Issues in HydrologicalModelling, John Wiley, Hoboken, N. J.
Lilly, A., and H. S. Lin (2004), Using soil morphological attributes and soil
structure in pedotransfer functions, in Development of Pedotransfer
Functions in Soil Hydrology, edited by Y. Pachepsky and W. Rawls,
pp. 115 – 142, Elsevier, New York.
Lilly, A., D. B. Boorman, and J. M. Hollis (1998), The development of ahydrological classification of UK soils and the inherent scale changes,
Nutr. Cycl. Agroecosyst., 50, 299 – 302.
Lin, H. S. (2003), Hydropedology: Bridging disciplines, scales, and data,
Vadose Zone J., 5, 317 – 340.
Lin, H. S. (2005), Letter to the Editor on ‘‘From the Earth’s critical zone toMars exploration: Can soil science enter its golden age?’’, Soil Sci. Soc.
Am. J., 69, 1351 – 1353.
Lin, H. S. (2006), Temporal stability of soil moisture spatial pattern andsubsurface preferential flow pathways in the Shale Hills Catchment,
Vadose Zone J., 5, 317 – 340.
Lin, H. S., and S. Rathbun (2003), Hierarchical frameworks for multiscale
bridging in hydropedology, in Scaling Methods in Soil Physics, edited by
Y. Pachepsky, D. Radcliffe, and H. M. Selim, pp. 353 – 371, CRC Press,
Boca Raton, Fla.
Lin, H. S., J. Bouma, L. Wilding, J. Richardson, M. Kutilek, and D. Nielsen(2005a), Advances in hydropedology, Adv. Agron., 85, 1 – 89.
Lin, H. S., D. Wheeler, J. Bell, and L. Wilding (2005b), Assessment of soil
spatial variability at multiple scales, Ecol. Modell., 182, 271 – 290.
Lin, H. S., W. Kogelmann, C. Walker, and M. A. Bruns (2006), Soil
moisture patterns in a forested catchment: A hydropedological perspective, Geoderma, 131, 345 – 368.
Maidment, D. (Ed.) (2002), Arc Hydro: GIS for Water Resources, ESRI,
Redlands, Calif.
McBratney, A. B., I. O. A. Odeh, T. F. A. Bishop, M. S. Dunbar, and T. M.
Shatar (2000), An overview of pedometric techniques for use in soil
survey, Geoderma, 97, 293 – 327.
McBratney, A. B., M. L. Mendonca Santos, and B. Minasny (2003), On
digital soil mapping, Geoderma, 117, 3 – 52.
McKenzie, N. J., and P. J. Ryan (1999), Spatial prediction of soil properties
using environmental correlation, Geoderma, 89, 67 – 94.
McSweeney, K., P. E. Gessler, B. Slater, R. D. Hammer, J. C. Bell, andG. W. Petersen (1994), Towards a new framework for modeling the soillandscape continuum, in Factors of Soil Formation: A Fiftieth Anniversary Retrospective, edited by R. G. Amundson et al., Spec. Publ. Soil Sci.
Soc. Am., 33, 127 – 145.
Mohanty, B. P., and T. H. Skaggs (2001), Spatio-temporal evolution and timestable characteristics of soil moisture within remote sensing footprints withvarying soil, slope, and vegetation, Adv. Water Resour., 24, 1051 – 1067.
National Research Council (NRC) (1991), Opportunities in the Hydrologic
Sciences, Natl. Acad. Press, Washington, D. C.
National Research Council (NRC) (1999), New Strategies for America’s
Watersheds, Natl. Acad. Press, Washington, D. C.
National Research Council (NRC) (2001a), Basic Research Opportunities
in Earth Science, Natl. Acad. Press, Washington, D. C.
National Research Council (NRC) (2001b), Conceptual Models of Flowand Transport in the Vadose Zone, Natl. Acad. Press, Washington, D. C.
Oades, J. M., and A. G. Waters (1991), Aggregate hierarchy in soils, Aust.
J. Soil Res., 29, 815 – 828.
O’Neill, R. V., D. L. DeAngelis, J. B. Waide, and T. F. H. Allen (1986), A
Hierarchical Concept of Ecosystems, Monogr. Popul. Biol., vol. 23,
Princeton Univ. Press, Princeton, N. J.
O’Neill, R. V., A. R. Johnson, and A. W. King (1989), A hierarchicalframework for the analysis of scale, Landscape Ecol., 3, 193 – 205.
Pachepsky, Y. A., and W. R. Rawls (Eds.) (2004), Development of Pedotransfer Functions in Soil Hydrology, Elsevier, New York.
Pachepsky, Y. A., D. E. Radcliffe, and H. M. Selim (Eds.) (2003), Scaling
Methods in Soil Physics, CRC Press, Boca Raton, Fla.
Pachepsky, Y. A., K. Smettem, J. Vanderborght, M. Herbst, and H. Vereecken(2004), Reality and fiction of models and data in soil hydrology, in
Unsaturated Zone Modeling: Progress, Applications and Challenges, edited by R. A. Feddes and G. de Rooj, pp. 233 – 260, Springer, New York.
Panday, S., and P. S. Huyakorn (2004), A fully coupled physically-based
spatially-distributed model for evaluating surface/subsurface, Adv. Water
Resour., 27, 361 – 382.
Park, S. J., and P. L. G. Vlek (2002), Environmental correlation of threedimensional soil spatial variability: A comparison of three adaptive techniques, Geoderma, 109, 117 – 140.
Pulleman, M. M., J. Bouma, E. A. van Essen, and E. W. Meijles (2000),
Soil organic matter content as a function of different land use history, Soil
Sci. Soc. Am. J., 64, 689 – 694.

12 of 13

W05301

LIN ET AL.: OPINION

Quisenberry, V. L., B. R. Smith, R. E. Philips, H. D. Scott, and S. Nortcliff(1993), A soil classification system for describing water and chemicaltransport, Soil Sci., 156, 306 – 315.
Rawls, W. J., and Y. A. Pachepsky (2002), Soil consistence and structure aspredictors of water retention, Soil Sci. Soc. Am. J., 66, 1115 – 1126.
Reed, P. M., et al. (2006), Bridging river basin scales and processes toassess human-climate impacts and the terrestrial hydrologic system,
Water Resour. Res., doi:10.1029/2005WR004153, in press.
Simonson, R. W. (1959), Outline of a generalized theory of soil genesis,
Soil Sci. Soc. Am. Proc., 23, 152 – 156.
Šimunek, J., N. J. Jarvis, M. T. van Genuchten, and A. Gardenas (2003),
Review and comparison of models for describing non-equilibrium andpreferential flow and transport in the vadose zone, J. Hydrol., 272, 14 – 35.
Soil Survey Division Staff (1993), Soil Survey Manual, Agric. Handb.,
vol. 18, U.S. Gov. Print. Off., Washington, D. C.
Sommer, M., M. Wehrhan, M. Zipprich, U. Weller, W. Zu Castell, S. Ehrich,
B. Tandler, and T. Selige (2003), Hierarchical data fusion for mapping soil
units at field scale, Geoderma, 112, 179 – 196.
Sonneveld, M. P. W., J. Bouma, and A. Veldkamp (2002), Refining soil
survey information for a Dutch soil series using land use history, Soil Use
Manage., 18, 157 – 163.
Sposito, G. (Ed.) (1998), Scale Dependence and Scale Invariance inHydrology, 423 pp., Cambridge Univ. Press, New York.
Sposito, G., and R. J. Reginato (Eds.) (1992), Opportunities in Basic Soil
Science Research, Soil Sci. Soc. of Am., Madison, Wis.
Tisdall, J. M., and J. M. Oades (1982), Organic matter and water-stable
aggregates in soils, J. Soil Sci., 33, 141 – 163.
Vachaud, G., A. De Silans Passerat, P. Balabanis, and M. Vauclin (1985),
Temporal stability of spatially measured soil water probability density
function, Soil Sci. Soc. Am. J., 49, 822 – 827.
VanderKwaak, J. E., and K. Loague (2001), Hydrologic-response simulations for the R-5 catchment with a comprehensive physics-based model,
Water Resour. Res., 37, 999 – 1013.
van Genuchten, M. T., and J. Šimunek (2004), Integrated modeling ofvadose zone flow and transport processes, in Unsaturated Zone Modeling: Progress, Applications and Challenges, edited by R. A. Feddes andG. de Rooj, pp. 37 – 69, Springer, New York.
Vogel, H. J., and K. Roth (2003), Moving through scales of flow andtransport in soil, J. Hydrol., 272, 95 – 106.
Vogel, H. J., I. Cousin, and K. Roth (2002), Quantification of pore structureand gas diffusion as a function of scale, Eur. J. Soil Sci., 53, 465 – 473.
Wagenet, R. J. (1998), Scale issues in agroecological research chains, Nutr.
Cycl. Agroecosyst., 50, 23 – 34.
Western, A. W., and R. B. Grayson (2000), Soil moisture and runoff
processes at Tarrawarra, in Spatial Patterns in Catchment Hydrology:
Observations and Modelling, edited by R. B. Grayson and G. Blöschl,
pp. 209 – 246, Cambridge Univ. Press, New York.

W05301

Western, A. W., R. B. Grayson, and G. Blöschl (2002), Scaling of soil
moisture: A hydrologic perspective, Annu. Rev. Earth Planet. Sci., 205,
20 – 37.
Wilding, L. P. (2000), Pedology, in Handbook of Soil Science, edited by
M. E. Sumner, pp. E-1 – E-4, CRC Press, Boca Raton, Fla.
Wilding, L. P., and H. S. Lin (2006), Advancing the frontiers of soil science
towards a geoscience, Geoderma, 131, 257 – 274.
Winter, T. C. (2001), The concept of hydrologic landscapes, J. Am. Water
Resour. Assoc., 37, 335 – 349.
Wood, E. F. (1999), The role of lateral flow: Over- or underrated, in
Integrating Hydrology, Ecosystem Dynamics, and Biogeochemistry inComplex Landscapes, edited by J. D. Tenhunen and P. Kabat, pp. 197 –
215, John Wiley, Hoboken, N. J.
Wösten, J. H. M., J. Bouma, and G. H. Stoffelsen (1985), The use of soil
survey data for regional soil water simulation models, Soil Sci. Soc. Am.
J., 49, 1238 – 1245.
Yeh, T.-C., and J. Šimunek (2002), Stochastic fusion of information forcharacterizing and monitoring the vadose zone, Vadose Zone J., 1, 207 –221.
Zhang, T., and R. Berndtsson (1991), Analysis of soil water dynamics intime and space by use of pattern recognition, Water Resour. Res., 27,
1623 – 1636.
Zhu, A. X., B. Hudson, J. Burt, K. Lubich, and D. Simonson (2001), Soil
mapping using GIS, expert knowledge, and fuzzy logic, Soil Sci. Soc.
Am. J., 65, 1463 – 1472.




J. Bouma, Soil Science Centre, Wageningen University and ResearchCentre, P.O. Box 47, 6700 AA Wageningen, Netherlands.
A. Lilly, Macaulay Land Use Research Institute, Craigiebuckler AB15
8QH, UK.
H. Lin, Department of Crop and Soil Sciences, Pennsylvania State
University, University Park, PA 16802, USA. (henrylin@psu.edu)
Y. Pachepsky, Environmental Microbial Safety Laboratory, ARS, USDA,
Building 173-BARC-East, Powder Mill Road, Beltsville, MD 20705, USA.
J. Thompson, Division of Plant and Soil Sciences, West Virginia
University, Morgantown, WV 26506, USA.
R. van Genuchten, George E. Brown Jr. Salinity Laboratory, ARS,
USDA, 450 West Big Springs Road, Riverside, CA 92507, USA.
H.-J. Vogel, Department Soil Physics, Center for Environmental
Research, Theodor-Lieser-Straße 4, D-06120 Halle, Germany.
A. Western, Department of Civil and Environmental Engineering,
University of Melbourne, Parkville, Vic 3052, Australia.

13 of 13