Geomorphology 116 (2010) 206–217
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Geomorphology
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e o m o r p h
Geomorphology and vegetation on hillslopes: Interactions, dependencies, and
feedback loops
Richard A. Marston ⁎
Department of Geography, Kansas State University, Manhattan, KS 66506-2904, USA
a r t i c l e
i n f o
Article history:
Received 4 June 2009
Accepted 3 September 2009
Available online 6 October 2009
Keywords:
Hillslopes
Mass movement
Vegetation
Biogeomorphology
a b s t r a c t
The linkages between vegetation and hillslope geomorphology have been the subject of serious study for
years, but traditionally, ecologists and geomorphologists have viewed these interactions as unidirectional.
On the one hand, botanists and landscape ecologists have examined the effects of hillslope features,
processes, and materials on vegetation structure, composition, and dynamics. Focus has been placed on the
effects of topography (elevation, slope angle, slope aspect), edaphic factors, rock type, and geomorphic
disturbance (mass movement, snow avalanches, land surface erosion). On the other hand, geomorphologists
have traditionally treated vegetation as an independent variable that affects landforms and sediment routing
at limited spatial–temporal scales. Hillslope vegetation and landforms, however, co-evolve. One key is to
understand the role of time, disturbances, and feedbacks that link vegetation and geomorphology on
hillslopes. The effects of vegetation on mass movement and landscape evolution are being studied in new
ways. Many regional studies claim that vegetation becomes less relevant as one moves to larger and larger
watershed scales, but ecoregion analysis offers a contrasting view. Whereas these efforts have produced
vehicles of understanding that are simple, ordered, unified, and harmonious, they often do not reflect the
complexity that leads to multiple possible outcomes—place-dependent results. Recent perspectives focus on
the two-way interplay between vegetation and hillslope geomorphology, where establishing cause-andeffect linkages is made difficult by confounding factors (spatial–temporal scale, location, convergence,
divergence, nonlinearity, thresholds, feedbacks). Vegetation and geomorphology interactions are controlled
by a combination of global factors (independent of time and place) and the local environmental history.
Continued refinement of fine-scale deterministic models should be encouraged, but the ability to translate
these results to larger scales needs to be explored. At large scales, future research, especially those with
predictive modeling as the goal, should concentrate on how to increase the generality of concepts and
models and should seek to reduce the number of variables and factors considered.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
Interest in the interactions between vegetation and geomorphology
can be traced through the history of science. Systematic study of the
processes involved in linking upland vegetation and geomorphology, however, is relatively recent. The purpose of this paper will
be threefold. First, past research will be highlighted that has been
especially influential in our thinking about linkages between vegetation
and geomorphology in upland areas before 1950 and since 1950.
Second, recent breakthroughs in conceptual and methodological
understanding will be presented on specific themes in hillslope studies.
Third, the paper will examine directions for future research, particularly
those highlighted by the 2008 Meeting of Young Researchers in Earth
Science (MYRES III) on the theme Dynamic Interactions of Life and
its Landscape (Reinhardt et al., 2010). This paper will not discuss
⁎ Tel.: +1 785 532 6727; fax: +1 785 532 7310.
E-mail address: Rmarston@ksu.edu.
0169-555X/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.geomorph.2009.09.028
paleoenvironment reconstruction, the role of vegetation in coupled
hillslope-channel systems, or linkages in aeolian systems.
The interest in linkages between vegetation and geomorphology
(phytogeomorphology, or geobotany) on hillslopes has, until very
recently, been unidirectional (Viles, 1988). On the one hand, botanists
and landscape ecologists have examined the effects of hillslope
features, processes, and materials on vegetation structure, composition, and dynamics. Focus has been placed on the effects of topography
(elevation, slope angle, slope aspect), edaphic factors, rock type, and
geomorphic disturbance (mass movement, snow avalanches, land
surface erosion). In this line of research, at least until recently, vegetation has been considered as responding to static geomorphic
variables (e.g., Whittaker, 1970). Reinhardt et al. (2010, p. 9) claim
that “…one could likely pick up any introductory textbook [in
ecology] and find dozens, if not hundreds, of examples where the
organization of biological communities is presumed to be the outcome
of physical processes that drive the formation of landscapes.” A recent,
comprehensive review of this research direction is presented by
Kruckeberg (2002). On the other hand, geomorphologists have been
R.A. Marston / Geomorphology 116 (2010) 206–217
207
Table 1
Key geomorphic functions created by vegetation on hillslopes.
Process
Selected examples
Modify soil moisture, through interception loss and transpiration, controlling mass movement
Leaves and litter intercept raindrops, dissipating erosive energy
(Haneberg, 1991; Harden, 2006)
(Walsh and Voight, 1977; Parsons et al., 1996;
Marston and Dolan, 1999; Keim and Skaugset, 2003)
Bryan (2000)
Organic matter in the soil increases water storage, infiltration, and percolation thereby promoting
vegetation growth and inhibiting erosion
Roots bind soil against piping, land surface erosion, and shallow mass movement
Aboveground biomass creates microtopography on land surface that affects overland flow;
roughness in the profile direction (upslope–downslope) slows overland flow;
roughness along the contour concentrates sheetflow into rillflow, rills into gullies
Aboveground biomass creates hydraulic roughness against overland flow
Treefall exposes soil for erosion
equally guilty, traditionally treating vegetation as a static independent
variable that affects landforms and sediment routing at limited spatial–
temporal scales (Renschler et al., 2007). The key geomorphic functions
of vegetation in upland settings are summarized in Table 1. Publications
by Viles (1988, 1990), Thornes (1990a,b), Hupp et al. (1995), and Collins
et al. (2004) provided new comprehensive overviews of this research
direction. In a search of the GeoRef database using keywords “vegetation
and geomorphology and hillslopes,” a total of 2073 entries in English (or
with English translations) were identified, excluding abstracts (Fig. 1).
Sixty percent of the entries have been published since the year 2000,
which reflects the remarkable increase in scholarship on the topic. Many
papers in recent years are now treating geomorphic and vegetative
variables as dynamic, but attention on feedbacks is still needed
(Table 2).
2. Foundations (pre-1950) in understanding
vegetation–geomorphology linkages
2.1. Early studies of geomorphic influences on vegetation distributions
Natural historians in China, Persia, and the ancient Greco-Roman
world (especially Theophrastus in Greece) were involved in identifying and classifying plants, but most were either oblivious or
uninterested in the oftimes striking relation between vegetation and
landforms, processes, and earth materials in the Mediterranean
(Kruckeberg, 2002). Alexander von Humboldt (1769–1859) was one
of the first to systematically observe the effects of elevation, climate,
and slope on vegetation and land use in his exploration of the Andes in
Colombia, Ecuador, and Peru (Martin and James, 1993). Although he
climbed most of the volcanoes in Ecuador, he compiled only brief notes
on the effect of substrate and lithology on vegetation. Kruckeberg
(2002 pp. 23–25) credits Franz Unger (1836) with a “full-blown
conceptualization of the connection between geomorphology and
Abrahams et al. (1994, 1995)
Gabet et al. (2003)
vegetation,” but vegetation is considered as dependent on geomorphology and not the reciprocal. Curiously, Charles Darwin recognized
that historical changes in plant distributions could be attributed to
geologic events (e.g., volcanism), but he rarely mentioned how plant
distributions at any point in time were affected by geological attributes
(Kruckeberg, 2002). He stated that climate change exerts a greater
influence on vegetation than do landforms, processes, and earth
materials. This view was further strengthened by the benchmark
ecological works by Clements (1928) and Cain (1944) in North America
and by Braun-Blanquet (1932) in Europe. These scholars claimed
climate was the primary variable affecting plant distributions, with
edaphic and topographic variables exerting a secondary influence.
2.2. Early studies of vegetation influences on landforms, geomorphic
processes, and earth materials
The lack of attention to vegetation among early geomorphologists
is evident as one reviews the history of geomorphology (e.g., Chorley
et al., 1964, 1973; Schumm, 1972, 1977; King, 1976; Laronne and
Mosley, 1982; Tinkler, 1985, 1989; Beckinsale and Chorley, 1991).
During and after the Renaissance (fourteenth to sixteenth centuries),
Leonardo da Vinci, Buffon, and Desmarest (among others) contributed
important findings to studies of erosion, but vegetation was rarely
considered. During the seventeenth and eighteenth centuries, theories
of landform development focused on catastrophic processes of
origin, including sea level change (e.g., Werner), floods (e.g., Buckland,
Sedgwick), vulcanism (e.g., de Saussure), and endogenic processes
(e.g., John Phillips) (Chorley et al., 1964). With the focus on the origin
and long-term denudation history of landforms at that time, scant
Table 2
Modes of vegetation disturbance that affect geomorphology in upland environments
[Trimble (1988) discusses most of the processes].
Process
Wildfires
Fig. 1. Number of refereed articles, books, and reports cited in GeoRef for 5-year periods,
using “vegetation and geomorphology and hillslopes.”
(Greenway, 1987; Wu et al., 1988; Riestenberg, 1994;
Schmidt et al., 2001; Roering et al., 2003)
(Parsons et al., 1992; Abrahams et al., 1995;
Wainwright et al., 2000; Stavi et al., 2009)
Selected examples
(Swanson, 1981; Marston and Haire, 1990;
Johansen et al., 2001; Gabet and Dunne, 2003;
Roering and Gerber, 2005; Moody et al., 2008;
Gabet and Sternberg, 2008; Blake et al., 2009)
Grazing
(Trimble and Mendel, 1995; Marston and
Dolan, 1999; Descroix et al., 2008)
Vegetation conversion to (DeGraff, 1979; Gabet and Dunne, 2002)
improve rangeland
Cropland agriculture
Trimble (1988)
Cropland abandonment Harden (1996)
Trails and roads
(Harden, 1992; Wallin and Harden, 1996)
Deforestation
(Swanston and Swanson, 1976; Sidle, 1992; Reid, 1993;
Marston et al., 1998; Lancaster et al., 2003)
Reforestation
(Liebault et al., 2002; Harden, 2002;
Marston et al., 2003; Keesstra et al., 2009)
Mined land reclamation (Toy and Hadley, 1987; Marston and Furin, 2004)
Military maneuver
(Marston, 1986; Grantham et al., 2001)
impacts
Climate change
(Lavee et al., 1999; Houben et al., 2008)
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R.A. Marston / Geomorphology 116 (2010) 206–217
attention was devoted to vegetation. The advocates of gradualism
who followed—Hutton, Playfair, Lyell—did not discuss vegetation–
landform relationships, and the topic was largely ignored by Powell,
Dutton, and Davis in the United States as well as by Walther Penck
in Germany and L.C. King in South Africa. Davis was influenced by
Darwinian ideas, so it is all the more surprising that he did not
incorporate vegetation into the Davisian geographical cycle. Gilbert
(1877) in his classic Report on the Geology of the Henry Mountains
devotes one-half page to the multifaceted roles that vegetation plays in
weathering, modifying the local hydrologic cycle, as well as in
retarding erosion by rainsplash and overland flow. Even Gilbert did
not conduct systematic studies of vegetative influences on rates of
erosion or landform development. The impact of human actions on the
removal of vegetation and the consequences for mass movement and
land surface erosion were recognized by Marsh (1864) and Glacken
(1956) in two publications that had widespread influence. In the
late nineteenth century, French geomorphologists De La Noë and De
Margerie (1888) described the role of vegetation in controlling
hillslope angles. In the twentieth century, French geomorphologists
Tricart, Cailleux, and Birot stressed the influence of tectonics and
climate oscillations in geomorphology, as did Büdel in Germany
and Fournier and Peltier in the United States. Tricart and Cailleux
(1969, 1974) described how vegetation and soils control geomorphic
processes by affecting weathering. All realized that climate acts on
landforms and geomorphic processes by acting on soil, vegetation,
weathering, and runoff.
3. Developments since 1950 in understanding
vegetation–geomorphology linkages
3.1. Time, disturbances, and feedbacks involving vegetation and
geomorphology on hillslopes
Many geomorphologists are familiar with the classic article by
Schumm and Lichty (1965) that outlined how the influence of
vegetation (along with other variables) changes with temporal scale
(Table 3). Vegetation is viewed by Schumm and Lichty (1965) as a
dependent variable over “cyclic (or geologic) time” (thousands to
millions of years in duration), while acting as an independent variable
over “graded (or modern) time” (centuries to millennia) and “steadystate (or present) time” (one year or less). To be sure, over cyclic time
vegetation is controlled by patterns in elevation, parent material, slope
angle, and aspect, all of which adjust over cyclic time. Vegetation
patchiness, however, also depends on geomorphic disturbances that
operate over shorter timescales (Swanson et al., 1988). The rigidity of
the Schumm and Lichty (1965) three divisions of time notwithstanding, the article opened the doors to explain the changing importance of
variables as a function of the timescale considered.
The 1960 paper by Hack and Goodlett, “Geomorphology and forest
ecology of a mountain region in the central Appalachians,” constituted
a benchmark effort to relate geomorphology and vegetation (and
hydrology). This effort was especially noteworthy because equal
attention was devoted to vegetation and geomorphology. Hack and
Goodlett (1960) demonstrated that groundwater flow paths affected
moisture availability (convergent flow lines = wet; divergent flow
lines = dry; parallel flow lines = mesic) and corresponded closely with
forests dominated by northern hardwood (basswood, sugar maple,
yellow and black birch), yellow pine (pitch pine, table mountain pine),
and oak (red oak, chestnut oak, black birch), respectively. The groundwater flow paths corresponded with hillslope shapes termed cones,
noses, and side (straight) slopes. Interestingly, Osterkamp et al. (1995)
revisited the study sites almost four decades after the work by
Hack and Goodlett (1960). They found the relationship had persisted
between topographic position, moisture regime, and prevailing forest
type.
Knox (1972) produced a widely cited paper that conceptualized
vegetation response to abrupt climate change in SW Wisconsin, along
with the subsequent response in hillslope erosion and sediment yield
(Fig. 2). Hascheburger and Souch (2004) pointed out that Knox had
since conceded that watersheds can respond in more than one way
to perturbations in the watershed—a reflection of the confounding
problem of complexity elucidated by Schumm (1991). A variety of
responses are shown in Fig. 3. Although this figure was prepared
for stream reaches, one can use it to conceptualize the response of
Table 3
(A) The status of river variables during time spans of decreasing duration (after Schumm and Lichty, 1965)
Status of variables during designated time spans
River variables
Geological
Modern
Present
1. Time
2. Geology
3. Climate
4. Vegetation (type and density)
5. Relief
6. Palaeohydrology (long-term discharge of water and sediment)
7. Valley dimensions (width, depth, slope)
8. Mean discharge of water and sediment
9. Channel morphology (width, depth, slope, shape, pattern)
10. Observed discharge of water and sediment
11. Observed flow characteristics (depth, velocity, turbulence, etc.)
Independent
Independent
Independent
Dependent
Dependent
Dependent
Dependent
Indeterminate
Indeterminate
Indeterminate
Indeterminate
Not relevant
Independent
Independent
Independent
Independent
Independent
Independent
Independent
Dependent
Indeterminate
Indeterminate
Not relevant
Independent
Independent
Independent
Independent
Independent
Independent
Independent
Independent
Dependent
Dependent
(B) The status of drainage basin variables during timespans of decreasing duration
Status of variables during designated timespans
Drainage basin variables
Cyclic
Graded
Steady
1. Time
2. Initial relief
3. Geology
4. Climate
5. Vegetation (type and density)
6. Relief or volume or system above base level
7. Hydrology (runoff and sediment yield per unit area within the system)
8. Drainage network morphology
9. Hillslope morphology
10. Hydrology (discharge of water and sediment from system)
Independent
Independent
Independent
Independent
Dependent
Dependent
Dependent
Dependent
Dependent
Dependent
Not relevant
Not relevant
Independent
Independent
Independent
Independent
Independent
Dependent
Dependent
Dependent
Not relevant
Not relevant
Independent
Independent
Independent
Independent
Independent
Independent
Independent
Dependent
R.A. Marston / Geomorphology 116 (2010) 206–217
Fig. 2. Response of vegetation, hillslope erosion, and sediment yield to abrupt changes
in climate, as proposed by Knox (1972). Curves (A) and (B) were modified by Knox
from Bryson and Wendland (1967), while curves (C) and (D) were derived by Knox.
Source: Hascheburger and Souch (2004).
209
hillslope erosion (via mass movement or land surface erosion) to
vegetation disturbance. In case A, the hillslope adjusts to a new
dynamic equilibrium. In case B, the hillslope is resilient and does not
respond. In case C, the hillslope experiences an initial response to the
disturbance followed by a period of recovery. Nevertheless, the Knox
(1972) article (and Fig. 2 in particular) drew attention to the role of
thresholds and response time (= reaction time + relaxation time) in
understanding vegetation–hillslope–valley geomorphology linkages
on a watershed scale. The nonlinear and complex nature of linkages
between climate, vegetation, and geomorphic response creates
challenges to reconstructing environmental change using this model.
The response of hillslopes to disturbance can vary dramatically
from hillslope to hillslope in the same watershed. For instance,
Marston and Haire (1990) found that that runoff and soil loss were
insignificant following the 1988 Yellowstone fires in areas where fire
resistant needles in Douglas-fir forests provided a dense, post-fire
cover of litter. In contrast, needles from lodgepole pine were easily
consumed by crown fires, eliminating a source of litter to protect the
soil after the fires (Fig. 4).
Landforms influence the frequency and spatial pattern of nongeomorphically induced disturbance by agents such as fire, floods,
drought, wind, and grazing (Swanson et al., 1988). A rich literature
has developed regarding the effect of vegetation disturbance on
hillslope erosion through wildfires, grazing, cropland agriculture,
deforestation and afforestation, urbanization, mined land reclamation, and maneuver impacts (Table 2). In turn, ecosystem processes
and patterns are also affected by the disturbances that are induced
directly by geomorphology, including the full range of mass movements, and snow avalanches in particular. Reice (1994, p. 424) stated
“biological communities are always recovering from the last disturbance. Disturbance and heterogeneity, not equilibrium, generate
biodiversity.” Geomorphic disturbances create patches (Pickett and
White, 1985; Zeng and Malanson, 2006) and corridors (Butler, 2001).
Geomorphic (and other types of) disturbances on hillslopes create
opportunities for recolonization and heterogeneity in the landscape,
which encourages greater diversity (Reice, 1994).
Geertsema and Pojar (2007) summarized the effects of mass
movement on biodiversity, citing a number of studies from around the
world but also adding their own experience from British Columbia.
The mesoscale topography created by mass movement (e.g., cliffs,
Fig. 3. Potential responses of erosional hillslopes to vegetation disturbance. Sensitive hillslopes will experience a period of instability in response to vegetation disturbance before
attaining a new equilibrium state (A). Resilient systems may show little response to vegetation disturbance (B) or may experience a period of instability before recovering to its
previous equilibrium state (C).
Source: Bakke (2009), as modified from Graf (1977).
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R.A. Marston / Geomorphology 116 (2010) 206–217
perature, create shade, provide shelter from wind, and serve as seed
traps. The microhabitat can make the difference in where conifers can
establish and persist. As climate warms, conifers are moving upslope
in many alpine settings, but not in an even pattern. Because feedbacks
and time lags are system responses, vegetation and climate may be
only loosely coupled at alpine treeline, rendering the geomorphic
influence of greater importance. As pointed out by Malanson et al.
(2007, p. 378),
mechanistic processes that shape the ecotone—seed rain, seed
germination, seedling establishment and subsequent tree growth
form, or, conversely tree dieback—depend on microsite patterns.
Growth forms affect wind and snow, and so develop positive and
negative feedback loops that create these microsites. As a result,
complex landscape patterns are generated at multiple spatial scales.
The disturbance caused by mass movement is the basis for dendrogeomorphology. Individual geomorphic events can be reflected in the
annual growth rings in a tree (Alestalo, 1971; Shroder, 1980). As
pointed out by Giardino et al. (1984, p. 303), “other factors such as
insects, climate, or other ecological variations can also initiate changes
within the tree and, therefore, samples must be screened to rule out
irrelevant events.” Dendrogeomorphology has been used to determine
relative rates of movement over multiple centuries for rock glaciers
(Giardino et al., 1984; Shroder and Giardino, 1987).
A potentially useful perspective on geomorphic disturbance and
vegetation response was suggested by Phillips (1995). He pointed out
that Brunsden and Thornes (1979) examined the sensitivity of landforms to events causing geomorphic change. They introduced the
transient form ratio:
TFr = ta = tf
ð1Þ
Where ta and tf are the mean relaxation and recurrence times,
respectively (Fig. 3). A ratio greater than unity (TFr > 1) indicates that
the transient forms will prevail because more time is needed for a
landform to attain a new equilibrium state than the time between
major geomorphic disturbances. On the other hand, a ratio less than
unity (TFr < 1) indicates the landform can attain a characteristic, stable
form. Phillips (1995) went on to suggest that one could compute a
transient form ratio for a particular ecosystem or plant community
(using the subscript v to indicate vegetation):
TFr;v = ta;v = tf;v
Fig. 4. Ground litter after 1988 Yellowstone fires in a Douglas-fir forest (A) and in a
lodgepole pine forest (B). In both cases, photos taken by the author in 1989 after a onehour, 75 mm artificial rainstorm had been applied with a rainfall simulator.
hummocks) adds complexity to the landscape. As one example, Smith
et al. (1986) found eight vegetation communities on 49 debris slides
and flows on the Queen Charlotte Islands. Species composition and
percent cover changed depending on the time since slope failure.
A noteworthy body of literature has developed in the last decade
on geomorphic effects on the spatial pattern of alpine treeline (Butler
et al., 2004, 2007; Resler et al., 2005). Microtopography is created in
the form of nivation hollows, terraced risers, patterned ground, snow
avalanche chutes, and debris flow deposits. Even single boulders,
termed “nurse rocks” by Resler et al. (2005), can moderate the tem-
ð2Þ
where ta,v is the mean time it takes for succession to restore the
predisturbance vegetation community, and tf,v is the recurrence
interval of geomorphic disturbances. By computing ratios for landforms and vegetation, Phillips reasoned, one might be able to identify
domains where vegetation and geomorphic change are codependent
(TFr and TFr,v are both less than or greater than unity) or at least
partially independent (one ratio > 1; the other less than unity). In the
case of the North Carolina Coastal Plain, tabular data were presented
from which the reader could make preliminary calculations of transient form ratios. The complexity comes in integrating the transient
form ratios for multiple types of geomorphic and vegetative disturbances. It is not surprising, therefore, that few researchers have
pursued this line of inquiry. One noteworthy effort by Moody and
Martin (2001) revealed that the relaxation time for wildfire in
watersheds forested by ponderosa pine-Douglas fir in the Colorado
Front Range was much less than fire recurrence intervals, suggesting
that fire-related geomorphic response may disappear before the next
fire disturbance. On the other hand, the residence time of sediment
eroded following the fire is more than 300 years, much greater than
the fire recurrence interval. Therefore, erosional and depositional
features may become persistent legacies from the wildfire. Research
focusing on transient form ratios should be encouraged as a way of
R.A. Marston / Geomorphology 116 (2010) 206–217
identifying the intertwined resilience of ecological and geomorphic
systems. With respect to mass movement, Wolman and Gerson
(1978) surveyed the literature and found measured recovery times
varying from less than a decade for some tropical regions to decades
or more in temperate regions. Recurrence intervals of high magnitude
storms that trigger mass movement range from 1 to 2 years in some
tropical areas, to 3 or 4 per hundred years in some areas of seasonal
rainfall and to 100 or more years in some temperate regions.
3.2. Modeling effects of vegetation on mass movement and landscape
evolution
A key development in the studies of mass movement was the
report prepared for the National Academy of Sciences by Varnes
(1978), which was also the most cited individual article or report in
Geomorphology between 1995 and 2004 (Doyle and Julian, 2005).
An updated version of this report, published by Cruden and Varnes
(1996), presented the most widely accepted classification of mass
movement. Detailed, quantitative sections are devoted to field
investigation and instrumentation, strength properties of earth
materials, methods of stability analysis, and design and construction
of slopes. Vegetation, however, is mentioned only in passing.
The effects of woody vegetation on mass movement was discussed
by Greenway (1987) and nicely summarized by Sidle et al. (1985) and
Sidle and Ochiai (2006). Details of how vegetation affects slope
stability are outlined in Table 4.
A group of researchers has focused on mechanistic modeling of
vegetation–geomorphic linkages affecting landscape evolution in the
Oregon Coast Range. Roering (1999) and Roering et al. (2001)
developed a physically based equation linking biotic–geomorphic
coupled transport. Testing the hypothesis that root strength is greater
on ridges than in hollows, hillslope convexity can be shown to vary
exponentially with erosion rate (Roering, 1999). Collins et al. (2004)
and Istanbulluoglu and Bras (2005) investigated the effects of vegetation on thresholds for channel initiation and landform evolution
using analytical and numerical approaches. The approach was
validated in describing how vegetation provides roughness to flow
hydraulics, slope stabilization via root reinforcement, shear strength
to resist erosion by overland flow, and protection against rainsplash
erosion. Vegetation was allowed to vary with colonization and
mortality rates, decays following fire or timber harvest, or mortality
via geomorphic disturbances. Gabet et al. (2003) developed the first
sediment flux equations for tree throw and root growth and decay.
When a tree is uprooted and falls over, the root mass that binds the
soil is rotated up, leaving a pit. During root growth, soil is pushed in a
211
direction normal to the soil surface. After the root dies, the soil
collapses vertically into the root hole. Widespread tree-throw can
disrupt soils to the point where soil characteristics are not correlated
with slope gradient, slope aspect, or rates of bedrock weathering
(Phillips and Marion, 2004). Subsequent tree location and density are
affected in a self reinforcing feedback.
Considerable effort has been invested in developing models to
assess landslide hazards. SHALSTAB is a physically based, deterministic model that combines an infinite slope stability model and steadystate hydrologic model to predict the potential for shallow landsliding
controlled by topography and pore water pressure (Montgomery and
Dietrich, 1994; Dietrich et al., 2001). The model assumes that soils are
cohesionless and root strength is neglected because it is highly
variable over space and time and difficult to quantify. A basic version
of SHALSTAB has been shown to reliably delineate areas prone to
shallow landsliding in parts of the Coast Ranges of northern California,
Oregon, and Washington (Montgomery et al., 1998; Dietrich et al.,
2001). The model does not predict the location of deep-seated
instability nor instability associated with steep, planar slopes typical
of inner gorges. Soil thickness strongly affects relative slope stability
by supporting vegetation that increases root strength and by
influencing the role of subsurface to overland flow. Soils are typically
thinnest on ridges and sideslopes and thickest in unchanneled valleys,
but the spatial variation in soil thickness is rarely incorporated into
deterministic hillslope stability models because it is highly variable
and impractical to measure over large areas. Dietrich et al. (1995)
developed a variation of the basic SHALSTAB model, SHALSTAB.V,
which incorporates greater parameterization, especially the spatial
variability in soil depth.
Probabilistic Infinite Slope Analysis (PISA) is a physically based,
probabilistic model that predicts spatially distributed static and
seismic shallow slope stability for topography obtained from a digital
elevation model and geotechnical information (Haneberg, 2004,
2005). Geotechnical information includes shear strength parameters,
phreatic surface height, and root strength and surcharge.
In a study prepared for the California North Coast Regional Water
Quality Control Board (Stillwater Sciences, 2007), the success of
SHALSTAB, SHALSTAB.V, and PISA in predicting landslides was tested
against independent data sets in three geologic types of terrain in the
Elk River basin in NW California. Parameters were determined for all
models. The tests demonstrated that differences between SHALSTAB.V
and PISA were typically small (within 3%), correctly predicting slope
instability (for shallow landslides) at 82% of the sample points. All
models produced better predictions than if slope gradient was used
alone.
Table 4
Relative influences of woody vegetation on slope stability [source: Sidle and Ochiai (2006), as modified from Greenway (1987)].
Influences on types of landslides
Mechanisms
Shallow, rapid
Deep-seated
Hydrological mechanisms
1. Interception of rainfall and snow by canopies of vegetation, thus promoting evaporation and reducing water available for infiltration
2. Root systems extract water from the soil for physiological purposes (via transpiration), leading to lower soil moisture levels
3. Roots, stems, and organic litter increase ground surface roughness and soil's infiltration capacity
4. Depletion of soil moisture may cause desiccation cracks, resulting in higher infiltration capacity and short-circuiting of
infiltrating water to a deeper failure plane
B
B
MA
MA
B
B
MA
MA
B
B
B
B
MA/MB
A
MB
B
B
MB
MA/MB
MA
Mechanical mechanisms
5. Individual strong woody roots anchor the lower soil mantle into the more stable substrate
6. Strong roots tie across planes of weakness along the flanks of potential landslides
7. Roots provide a membrane of reinforcement to the soil mantle, increasing soil shear strength
8. Roots of woody vegetation anchor into firm strata, providing support to the upslope soil mantle through buttressing and arching
9. Weight of trees (surcharge) increases the normal and downhill force components
10. Wind transmits dynamic forces to the soil mantle via the tree bole
A = mechanism adverse to stability; MA = marginally adverse mechanisms; MB = marginally beneficial mechanisms; B = beneficial mechanisms.
212
R.A. Marston / Geomorphology 116 (2010) 206–217
3.3. The role of vegetation in watershed production of sediment
Many have asserted that vegetation exerts a strong control on
production of sediment for small-sized watersheds, but the influence
of vegetation declines as one moves downstream to progressively
larger sized watersheds. Lane et al. (1997) demonstrated that vegetation canopy cover and surface ground cover were important controls on sediment yield in the semiarid Walnut Gulch watershed near
Tombstone, Arizona, at the scale of plots to hillslope scales (areas <
0.02 km2). At the subwatershed scale (0.02 to 10 km2), the type of
vegetation remained important; but at the larger watershed scale (10
to 100 km2), rainfall, channel variables, and soils became dominant
variables. Trimble (1988) discussed vegetation effects on sediment
yield at the contrasting spatial scales of the hillslope, river basin, and
climate–vegetation region. He concluded that while generalizing the
relationships between vegetation and sediment yield at the scale of
vegetation–climate regions and river basins is possible, the confounding effect of other variables renders prediction as untenable at those
scales. Prediction at large-scale/small areas of hillslopes has been
more reliable, and Trimble (1988) presented a comprehensive survey
of vegetation effects on erosion at that scale. In yet another case, work
in the Himalaya has demonstrated that changes in forest cover in the
mountain regions cannot explain trends in catastrophic flooding and
sedimentation downstream in the Ganges River Plain of India and the
delta region of Bangladesh (Hamilton, 1987; Hofer, 1993; Ives, 2006)
or even within the Himalayan Range (Marston et al., 1996). Slope
failures in undisturbed forests in the Middle Mountains of central
Nepal was found to be proportionately more frequent than slope
failures on hillslopes disturbed by deforestation or terraced agriculture (Fig. 5).
One of the great challenges in understanding linkages between
vegetation and geomorphology will continue to be spatial scale.
Large-scale (small area) patterns of vegetation can be affected by
Fig. 5. Slope failures in a forest undisturbed by human activities along an unnamed
stream in Middle Mountains of central Nepal. All slope failures in this image, taken by the
author in 1984, can be attributed to stream undercutting of the hillslope as the stream
aggrades, a vivid example of a positive feedback.
small-scale (large area) geomorphic processes such as tectonics,
vulcanism or glaciation. Urban and Daniels (2006, p. 204) point out
While fine resolution investigations may be of critical importance
to the understanding of isolated geomorphic function, many
important ecological processes operate primarily at landscape
scales (Dunning et al., 1992; Taylor et al, 1993). The incorporation
of ecological questions into research investigations and methodological designs should lead us to question more critically the
appropriateness of scale and resolution at which data should
be collected and processed. Another challenge is to increase the
portability of research results and explanations from one spatial
scale to another.
The role of vegetation in geomorphology was highlighted in the
classic work of Langbein and Schumm (1958). They related annual
sediment yield to effective precipitation (adjusted in concept for
evapotranspiration, but estimated by a graphical relation between
precipitation and runoff) (Fig. 6). Annual sediment loads for “about
100 stations” were used, “…giving preference to smaller drainage
areas” (Langbein and Schumm, 1958, p. 1076). The 100 data points
were averaged for each of six precipitation groups, which explain the
often misunderstood presence of only six data points displayed on the
graph. A separate graphical relation was developed using sediment
accumulation data for 163 reservoirs that drained areas between 2.6
and 12.9 km2. In both studies, no attempt was made to control for
effects of land use or topography, an oversight corrected by Kirkby
(1980). The two Langbein and Schumm (1958) curves are similar,
identifying a peak in sediment yield in regions with effective annual
precipitation of ~ 305 mm (12 in.). This precipitation regime is
associated with semiarid vegetation, a finding confirmed by Collins
and Bras (2008). In arid regions with lower mean effective annual
precipitation, vegetation is sparse or absent; but runoff is insufficient
to produce high sediment yields. In more humid regions, they claimed
grass and forest cover bind soil against fluvial action. In the steep
mountains of the humid tropics (e.g., Japan and New Zealand) affected
by tropical cyclones, the highest sediment yields in the world occur
under conditions of thick forest cover (Ohmuri, 1983). Moreover, the
Langbein and Schumm (1958) graph included solid load only; solutes
are not considered. In the final analysis, the work by Langbein and
Schumm (1958) drew attention to the nonlinear role of vegetation in a
quantitative, systematic way that was simple, ordered, unified, and
harmonious—and, therefore, appealing. This classic study inspired a
new generation of studies that employed multivariate analyses and
more careful consideration of mechanics. For instance, a numerical
modeling study by Istanbulluoglu and Bras (2006) examined the
dynamics of soil moisture, vegetation and erosion with respect to
Fig. 6. Relationship of sediment yield to effective precipitation using sediment stations and
reservoir data.
Source: redrawn from Langbein and Schumm (1958, pp. 1077–1078).
R.A. Marston / Geomorphology 116 (2010) 206–217
climate change. Their efforts reproduced the non linear relationship
between mean annual sediment yield and precipitation.
Three types of models have been developed to describe the erosion
of the land surface: empirical, conceptual, and physically based (Lane
et al., 1988). Models vary in how they treat vegetation effects on land
surface erosion. The Universal Soil Loss Equation (USLE) is an example
of empirical models, developed by Wischmeier and Smith (1978),
which has been used worldwide. The USLE was developed during the
period 1954–1965, modifying earlier work on soil erosion in the Corn
Belt of the United States with plot data from natural storms and
rainfall simulator experiments. The form of the USLE equation is (Lane
et al., 1988)
A = RKLSCP
ð1Þ
213
combine in repeated patterns. Ecoregions also vary in sensitivity to
human disturbance. The level III ecoregions developed by Omernik
(1995) are particularly well suited for examining regional differences in
sediment yield. Simon et al. (2004) compiled a database of suspended
sediment concentrations from 2900 sites in the United States. The
suspended sediment concentrations were normalized by the 1.5-year
recurrence interval flows for a valid comparison between ecoregions.
These studies revealed that the highest suspended sediment concentrations occur in semiarid environments (southwest Tablelands, Arizona/
New Mexico Plateau, Mohave Basin and Range) (Fig. 7). The highest
sediment yields occur in the following ecoregions: Mississippi valley
loess plains, Coast Range (northern California, Oregon, Washington),
Flint Hills of Kansas, and northern Piedmont.
4. Future directions: MYRES III
where
A
R
K
LS
C
P
computed soil loss per unit area (tons per acre-foot),
rainfall–runoff factor (hundreds of foot-tons per in? per
acre-h-y),
soil erodibility factor (tons-acre-hr per hundreds of acrefoot-tons-in),
slope length-steepness factor (1.0 on uniform 72.6-footlong slopes at 9% gradient),
crop management factor (1.0 for tilled, continuous fallow),
and
erosion control practice factor (1.0 for upslope–downslope
tillage).
Tests of the USLE revealed only partial explanation of field-measured soil erosion and did not account for redeposition of mobilized
soil. A Modified Soil Loss Equation (MSLE) was developed to account
for natural vegetation (Warrington et al., 1980). The C and P factors in
the USLE were replaced by a vegetation-management factor, VM, in
the MSLE. Neither the USLE nor the MSLE measured gully erosion or
mass movement. A Revised Universal Soil Loss Equation (RUSLE)
incorporated modifications to the LS and C factors and utilized a more
deterministic approach to the P factor. These changes did little to
improve prediction (Tiwari et al., 2000).
In 1985, the USDA initiated the Water Erosion Prediction Project
(WEPP), a process-based simulation model that predicts soil loss and
deposition. Vegetation parameters were incorporated into the soils
component of WEPP, which considers biomass aboveground and
belowground for the entire plant community. In WEPP, living and
dead organic matter affect runoff, shear stress, and flow sediment
transport capacity. The model can accommodate spatial and temporal
variability in topography, soil properties, cropping and management,
and sediment detachment and deposition. WEPP calculates erosion
from the rill and interrill areas on a per rill area basis (Tiwari et al.,
2000). WEPP has been selected by the USDA-NRCS and EPA for
designation as the primary assessment tool for soil erosion that will be
used in the future to support regulatory requirements (Renschler and
Harbor, 2002). In a comparison test by Tiwari et al. (2000), WEPP
performed as well or better than USLE and RUSLE on 85% of 1600
plots. In a study by Wisleder (2000) in the Washita River basin of
western Oklahoma–Texas panhandle, WEPP derived estimates of
sediment yield upstream of 19 dams and were compared to the
sediment deposition in those reservoirs. WEPP severely underestimated sediment yield, but 77% of the residuals between predicted
and observed sedimentation could be explained by accounting for
unpaved section line roads in the watersheds.
Ecoregion analysis is a brilliant solution to the problem of forming
and testing hypotheses about phenomena that cannot easily be reduced
to deterministic terms. Ecoregions comprise distinct and mappable
geographic units in which biophysical forms, processes and materials
(vegetation/land use, geology, geomorphology, soils, and hydrology)
The lack of collaborative research between ecologists and geomorphologists led the National Research Council to select 77 early-career
researchers and convene the 2008 Meeting of Young Researchers in
Earth Science (MYRES III) on the theme Dynamic Interactions of Life and
its Landscape (Reinhardt et al., 2010). They identified two broad
themes: 1) co-evolution of landforms and biological communities; and
2) humans as modifiers of the landscape, through direct and indirect
actions. They discussed the state-of-the-art on these themes, along
with knowledge gaps, and suggested ways to move forward. Some of
the main points can be summarized as follows (MYRES phraseology
shown in italics below).
(1) Analytical tools: the expanded capability of remote sensing
(ground-based, airborne, spaceborne) is underutilized for
assessing landscape change over a range of spatial and temporal
scales. Numerical dating of bedrock and sediment, and hence
physical landscape evolution, is now possible over a wide
range of temporal scales using stable and radioactive isotopes.
Concordant understanding of paleoenvironment dynamics is
possible with paleoenvironment dynamics can be studied with
plant macrofossils, pollen and stable isotopes.
(2) Development and validation of landscape evolution models that
enable feedbacks between biotic and physical processes. Vegetation needs to be treated as a more dynamic element (cover,
seasonality, function) in the development of mechanistic
models to describe rates of sediment transport, thresholds of
erosion, slope stability, and hydraulic roughness. More attention needs to be devoted to modeling linkages between
vegetation and geomorphic on a decadal time scale, focusing
on feedbacks between physical and biological processes.
(3) Physical modeling—motivating and constraining bio-physical
experiments: The full breadth of biotic–abiotic linkages should
be explored in light of hypotheses that many linkages could be
scale-independent. This would provide new impetus to labscale experimentation on vegetation–geomorphic linkages.
(4) Co-evolution of landforms and biological communities: More
work is needed, including field-based and model testing, to
document at what spatial and temporal scales biotic factors
affect the physical landscape.
(5) To what extent does biodiversity influence the evolution of
landscapes? What level of biological variation matters? Functional
groups of species exist, defined by their signature on the physical
environment. How many different species or functional groups
need to be considered? What is the link between biodiversity and
landscape stability?
(6) The impact of climate change on a landscape: the key factor
mediating landscape response to climate change is variability in
biological and physical processes. Prediction will be improved by
understanding landscape sensitivity (thresholds involving vegetation) and paleorecords of landscape response to climate change.
214
R.A. Marston / Geomorphology 116 (2010) 206–217
Fig. 7. (A) Omernik Level III ecoregions for the conterminous USA. (B) Median suspended sediment concentrations (in mg/l) for ecoregions.
Source: Simon et al. (2004).
R.A. Marston / Geomorphology 116 (2010) 206–217
(7) Long-term sustainability of human-influenced and human-occupied
environments: agent-based modeling should be encouraged that
link agent decisions to land use/land cover change. The models
should be process-based and include human interactions and
natural landscape dynamics.
(8) Ecological management and landscape restoration: many failures
in landscape restoration result from unknown or poorly
understood feedbacks between physical and biological processes. Post-project monitoring is needed of the physical and
biological environment.
The MYRES III effort and a survey of recent advances in hillslope
studies illustrate that focus needs to be placed on identifying and
measuring the feedbacks between biological and geomorphological
processes. Nonlinear feedbacks, if recursive, may confer a degree of
persistence to a particular ecosystem (Stallins, 2006). The MYRES III
findings are credible. Research priority should be focused on
understanding the feedbacks between biotic and physical processes
and the co-evolution of landforms and vegetation. Advances gained on
those two themes promise to yield benefits in the other six thematic
areas.
5. Summary and conclusions
Scientific discourse on the linkages between vegetation and geomorphology focused on mere description before 1950. Important
conceptual advances began about 1950, thanks to the emergence of
landscape ecology and field measurement of geomorphic processes.
Much excitement has been generated in the last two decades by recent
advances in measurement/explanation and now prediction. The
strategies for prediction of land surface erosion on hillslopes, mass
movement, and landscape evolution range from empirical studies to
physically based mechanistic models. Research on the full range of
techniques should be encouraged until we reach the day when we can
successfully upscale the linkages from mechanistic modeling on small
plots and individual organisms to the landscape scale. Our understanding of the feedbacks between geomorphology and vegetation is
incomplete. Linkages abound between vegetation and geomorphology
on the scale of hillslopes, but the link between cause-and-effect is
confounded by many factors. A need exists, however, to construct,
calibrate, and test the relationships over the full range of geographic
conditions. Place matters…on an individual hillslope (ridge top vs.
colluvial hollow), from hillslope to hillslope in the same watershed
when vegetation differs, and when comparing linkages among different ecoregions. Vegetation and landforms co-evolve, but the feedbacks
are just beginning to be understood and quantified. We are looking for
new metrics to characterize these interactions and model them at a
variety of spatial and temporal scales. The need exists to build stronger
linkages between spatial analysis and GIS in the study of hillslope
systems. For instance, GIS can be used to study connectivity, distance–
decay functions, shape analysis, edge roughness, and the association
between patterns. The impact of human activities on these linkages
will continue to garner attention as a result of accelerated rates of
direct and indirect vegetation change, including the effects of climate
change on biogeomorphic form and processes.
Whereas these efforts have produced vehicles of understanding that
are simple, ordered, unified, and harmonious, they often do not reflect
the complexity that leads to multiple possible outcomes—placedependent results. Recent perspectives focus on the two-way interplay
between vegetation and hillslope geomorphology, where establishing
cause-and-effect linkages is made difficult by confounding factors
(spatial–temporal scale, location, convergence, divergence, nonlinearity, thresholds, feedbacks). Vegetation and geomorphology interactions
are controlled by a combination of global factors (independent of time
and place) and the local environmental history—the perfect landscape
of Phillips (2007). Each landscape has an inherited history…from
215
biophysical and human influences…which will almost certainly vary
from place to place. Perhaps no clear time scale exists for understanding
the linkages between vegetation and geomorphology on hillslopes
because of the inherent coupling of biological and physical processes.
The historical legacy of local disturbances leads to increased divergence,
while global controls lead to convergence. The key, therefore, is to
increase the generality of our models, concepts, and research and to
reduce the number of variables and factors considered, rather than seek
deterministic models to describe landscapes in all of their complexity.
Continued refinement of fine-scale deterministic models should be
encouraged, but the obstacles to translating these results to different
scales need to be explored. At coarse scales, future research, especially
those with predictive modeling as the goal, should concentrate on how
to increase the generality of concepts and models and should seek to
reduce the number of variables and factors considered.
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