An Eco-hydro-geomorphic Perspective to Modeling the Role of … · 2018-08-27 · quantitative process-based and theoretical methods, however, these climatic geomorphology ideas have

© 2009 The AuthorJournal Compilation © 2009 Blackwell Publishing Ltd

Geography Compass 3/3 (2009): 1151–1175, 10.1111/j.1749-8198.2009.00229.x

Blackwell Publishing LtdOxford, UKGECOGeography Compass1749-81981749-8198© 2009 The AuthorJournal Compilation © 2009 Blackwell Publishing Ltd22910.1111/j.1749-8198.2009.00229.xFebruary 2009001151???1175???Original ArticleEco-hydro-geomorphic perspective to catchment evolutionEco-hydro-geomorphic perspective to catchment evolution

An Eco-hydro-geomorphic Perspective to Modeling the Role of Climate in Catchment Evolution

Erkan Istanbulluoglu*School of Natural Resources, Biological Systems Engineering, University of Nebraska-Lincoln

AbstractClimate is the energizer of the landscape system. Geomorphic response of acatchment integrates a wide range of climate-driven coupled biotic-abioticprocesses along topographic flow paths. As such, modeling the impacts of climatein landscape evolution requires a holistic approach, involving basin ecological andhydrological states, primitive in existing numerical models. To facilitate futuremodel developments, a conceptual diagram of a holistic approach for modelingthe role of climate in landscape evolution is presented. We identify hydrology andvegetation dynamics as critical components of future landscape evolution modelsand discuss how different runoff generation and simple vegetation growth-deathprocesses impact modeled catchment morphology and sediment yields. We illustratefundamental differences observed in eco-hydro-geomorphic processes acrossclimates using examples from the literature, and emphasize the importance ofthese differences on catchment evolution. The article concludes with the opinionthat simplistic modeling approaches that capture the salient aspects of dominantclimate-soil-vegetation-erosion interactions observed in different climates should beadopted in landscape evolution models. This dominant-process approach wouldalso stimulate extensive process-based field research, necessary for the merger offield and modeling perspectives in geomorphology.

Introduction

A process-based understanding of the role of climate in landscape evolutionis crucial for both predicting the integrated impacts of the future climatechange, and interpreting the geologic records of the past climates. Therelation between regional climate, sediment yields, and catchmentmorphology involves the interaction of many physical and biologicalprocesses. Recognizing the strong control of climate in cathcment evolution,a group of geomorphologists hypothesized that climate manifests itselfdistinctly on landscape morphology, and used basic climate variables todistinguish landscape morphology (Ritter et al. 2006; Wilson 1968). Lacking

1152 Eco-hydro-geomorphic perspective to catchment evolution

© 2009 The Author Geography Compass 3/3 (2009): 1151–1175, 10.1111/j.1749-8198.2009.00229.xJournal Compilation © 2009 Blackwell Publishing Ltd

quantitative process-based and theoretical methods, however, these climaticgeomorphology ideas have only been applicable in extreme conditions(arid, periglacial and glacial systems) with characteristic geomorphicphenomena (Twidale and Lageat 1994; Wilson 1968).

Efforts trying to relate drainage basin response to climate have pre-dominantly focused on deciphering the Quaternary stratigraphic records(Anders et al. 2005; Brakenridge 1980; Bull 1991; Hall 1990; Meyer et al.1992; Pierce et al. 2004; Waters and Haynes 2001), while contributingrelatively little to mechanistic understanding of how the complex andcoupled landscape system responds to climate (Tucker and Slingerland1997). Fluvial channel incision and hillslope mass movements sculptlandscapes in most climates. Topographic signatures of these commonforms of processes have been identified using landscape evolution models(LEMs) forced by constant climate (Tucker and Bras 1998, 2000; Tuckerand Whipple 2002; Veneziano and Niemann 2000). Statistical propertiesof such process signatures have been related to landscape surfaceproperties, including erodibility (Gasparini et al. 2004; Moglen andBras 1995a,b), geology, soils (Cohen et al. 2008; Hancock 2005), andvegetation (Collins et al. 2004; Istanbulluoglu and Bras 2005; Istanbulluogluet al. 2008).

Only a few LEMs have addressed the impacts of climate change. Rinaldoet al. (1995) examined the topographic implications of a cyclic climatechange using a detachment-limited erosion model. In their model, dryperiods were represented with a high and wet periods with a low erosionthreshold following a sinusoidal function. Their model predicted channelnetwork contraction and valley aggradation during wet-to-dry climatetransitions. Although this contradicts with empirical data suggesting thereverse (Bull 1997; McFadden and McAuliffe 1997; Waters and Haynes2001), the Rinaldo et al. (1995) model showed that climate states leavetheir geomorphic signatures only when there is no active uplift (i.e.,topographic decline); when there is uplift, topography reflects the signatureof the current climate, and any relict features on the landscape reflectlow-threshold conditions of the past (Rinaldo et al. 1995).

In a more comprehensive modeling study, Tucker and Slingerland(1997) combined transport- and detachment-limited fluvial erosion andhillslope diffusion laws to study abrupt and gradual changes in climate ina moderate-relief catchment. Their model predicted: (1) a higher sensitivityto increase in runoff rate and decrease in runoff erosion threshold than tothe reverse; (2) sequential aggradation and incision following both cyclic runoffvariations and a single climate perturbation; and (3) valley incision duringperiods of growing runoff intensity and/or decreasing vegetation cover.

In these earlier models, climate change was simulated implicitly by varyinga surface property (e.g., erosion threshold) and/or a hydrological quantity(e.g., runoff rate) in time. In reality, climate change triggers many complexresponses and feedbacks in the internal biotic (terrestrial flora and fauna)


Eco-hydro-geomorphic perspective to catchment evolution 1153

and abiotic (soil and sediment) landscape variables. In the complex landscapesystem, the relation between a cause and effect could sometimes be counter-intuitive and difficult to predict especially during transitional phases(Chappell 1983; Schumm 1974; Schumm et al. 1987; Tucker et al. 2006).For example, runoff may or may not increase when climate shifts from adry to a wet state as a result of altered ecosystem response (e.g., Berts et al.2007; Scanlon et al. 2005), or could become more erosive due to amodulated hydrological behavior (Kirkby et al. 1990; Molnar 2001;Molnar et al. 2006) and weakened vegetation resistance (Istanbulluogluand Bras 2006; Tucker et al. 2006). The challenge here is how to representthe impact of climate on the dynamic landscape properties, such as biota,and their interactions with geomorphic processes. This will require anintegrated ecologic, hydrologic, and geomorphic (eco-hydro-geomorphic)approach forced by climate in landscape evolution modeling.

Landscape Eco-Hydro-Geomorphic Response to Climate

Vegetation is the center piece of landscape eco-hydro-geomorphic response(e.g., Kirkby 1995). Many field observations worldwide link catastrophicerosion and erosion hazards to natural and anthropogenic vegetationdisturbances (e.g., Greenway 1987; Sidle et al. 1985). Vegetation in con-nection with soil development supports habitat for invertebrates (e.g.,earthwarms, beetle, and mites) and burrowing mammals (e.g., moles,rodents, and gophers), which have a tremendous impact on downslopesediment transport (Black and Montgomery 1991; Gabet et al. 2003; Yooet al. 2005).

Vegetation response to changes in past climates has been documentedbased on paleobotanical evidences at local, continental (Hughen et al. 2004;Muller et al. 2003; Sisk 1998), and global (e.g., Ceding et al. 1997) scales.Geomorphologists have shown clear examples of how climate-vegetation-erosion linkages influence basin sediment yields (Douglas 1967; Langbeinand Schumm 1958; Walling and Kleo 1979; Zhang et al. 2001a,b), drainagedensity, channel head positions (Abrahams 1984; Istanbulluoglu et al. 2002;Melton 1957; Moglen et al. 1998; Montgomery and Dietrich 1994a,b),and landscape morphology (Hack and Goodlett 1960).

To facilitate discussions toward an eco-hydro-geomorphic LEM relevantfor examining climate change impacts, a conceptual model diagram isproposed in Figure 1. In the diagram, landscape is conceptualized as acollection of four local state variables: elevation, soil (e.g., depth, hydrologicproperties), biomass, and burrowing animal density, which co-evolve,through local interactions (both in the vertical and horizontal direction)via coupled processes. Researchers have illustrated the emergence ofglobal patterns in the organization of elevation in modeled river basins as aresult of geomorphic transport (Rodriguez-Iturbe and Rinaldo 1997;Willgoose et al. 1991); and eco-hydrological organization of vegetation



functional types within a drainage network (Caylor et al. 2005; Ivanovet al. 2008). However, the two-way interactions between biologicaland physical processes, that is, the role of biota on hydrology andgeomorphic transport, and the impact of geomorphic transport on theestablishment and resilience of biota over geomorphically significant timescales have remained largely unexplored.

Modeling landscape evolution under a varying climate would requiretying these processes to climatic variables such that climate signals couldpenetrate into the various landscape state variables. Below I attempt todistill the linkages in Figure 1 into the conservation of mass equation forlandscape elevation by writing a generic total sediment flux (soil wash, massmovements) term in pseudo functional form of discharge, biomass, animalpopulation density and climate variables as:

(1)

(2)

where ∂z/∂ t is rate of change in landscape elevation; U, tectonic upliftrate; qs, volume of sediment flux; q, surface/subsurface flow discharge;

Fig. 1. A conceptual diagram depicting the interactions among biological and physical Earthsurface processes in landscape evolution. Dashed lines represent the two-way interactionsbetween landscape variables and processes.

∂∂z

tU qs= − �

q fq P ET P E T V Sl V ET T

A V Sl Tsa e B m B a

p D B m∝ , ( , , , , , ) ( , )

( , , , )( )⎧

⎨−

λλ

**⎩⎩

⎫⎬⎭

> −, ( )F F Vt p B



P, precipitation rate; ETa, actual evapotranspiration rate; Ee, availableevaporative energy; T, temperature; VB, vegetation biomass; Sl, soilproperties; λm, landscape morphology; Ap–D, animal population density;F, erosive forces; and Ft, erosion threshold induced by vegetation.

Here, biomass is used to characterize the state of vegetation, because itmay be utilized in geomorphic transport laws, potentially in the form ofa dynamic erosion threshold (e.g., Graf 1979), and modeled based onecological theory (e.g., Montaldo et al. 2005). When life is considered asa dynamic state variable, how precipitation is partitioned into runoff andevapotranspiration becomes a focal point in landscape evolution modeling(Figure 1). As such, runoff discharge should integrate the physical controlsof basin morphology and soils, with biotic controls through evapotranspira-tion. Animal population density may be written tentatively as a functionof vegetation biomass, soil properties, and temperature. As one couldnotice, there are a number of parameters in equation (2), which do nottypically appear in abiotic landscape evolution models. To identify researchand model development needs necessary for a holistic eco-hydro-geomorphiclandscape evolution model, below I review the existing literature abouthydrology and vegetation modeling in LEMs as well as geomorphictransport equations that explicitly consider biotic effects.

Impacts of Hydrology in Landscape Evolution

RECENT ADVANCES IN MODELING

Basin hydrologic response and morphology co-evolve. Hydrologists haverelated runoff generation (Beighley et al. 2005; Dunne and Black 1970;Grayson et al. 2002) and streamflow response (Howard 1990; Marani et al.2001; Rodriguez-Iturbe and Valdez 1979) to basin geomorphic structure,while geomorphologists have identified strong controls of dominanttype of runoff on landscape morphology, including drainage density andwatershed hypsometry (Laity and Malin 1985; Luo 2000; Ritter andGardner 1993).

Traditionally in LEMs runoff is assumed to be event-based, Hortonian(infiltration excess), steady-state, and uniform across the landscape. Thisassumption is implicit in the derivation of fluvial geomorphic transportlaws (Tucker and Whipple 2002). Solyom and Tucker (2004) incorporateda non-steady flow routing by relating the peak discharge of runoffhydrograph to runoff rate, a storm duration number (SDN), basin length,and a non-dimensional hydrograph shape factor. The SDN was definedas storm duration to sum of storm duration and basin travel time ratio. Ahigh (low) SDN suggests a long-duration (short-duration) storm. Theythen turned to the CHILD landscape evolution model to explore thetheory with progressively decreasing SDNs (implying shorter stormdurations) (Figure 2). In the simulated basins using the detachment-limited



erosion law, downstream reduction in runoff erosion with SDN<1resulted in steeper channel slopes, lower channel concavity, and smallervalley density (Figure 2). Lower than typical channel concavity valuesobserved in some semiarid basins partly corroborate these results (e.g.Howard 1980; Tarboton et al. 1991; Tucker et al. 2006). In a recentarticle, Solyum and Tucker (2007) have extended this theory for stormsizes less than catchment area, which remains to be explored in an LEM.

Hortonian runoff is typically observed in semiarid climates with lowsoil infiltration rates. In humid environments, however, runoff is oftengenerated over saturated portions of the landscape (Beven and Kirkby1979; Dunne and Black 1970). Introducing a topographic saturationthreshold in LEMs based on the TOPMODEL conceptualization of Bevenand Kirkby (1979) have led to longer and more convex hillslopes and alower drainage density in modeled catchments (Ijjasz-Vasquez et al.1992; Tucker and Bras 1998). This was related to the dominance ofslope-dependent sediment transport in unsaturated landscape areas, illus-trating well-defined signatures of runoff type on landscape morphology.

Climate change may cause shifts in the dominant form of runoffgeneration. Bogaart et al. (2003) examined the impacts of alternatingclimate-related phases of permafrost and non-permafrost on catchmentevolution by representing the former with a low and the latter with a highvalue of soil transmissivity (measure of how much water can be transmittedhorizontally in the subsurface). As a result, during periods of permafrost(non-permafrost), their model simulated infiltration excess (saturation

Fig. 2. Equilibrium topographies presented by Solyom and Tucker (2004) with (a) steady-staterunoff; (b) non-steady runoff, SDN = 1/3 in the side length of the simulated field; and (c) non-steady runoff, SDN = 1/26 in the side length of the simulated field. Simulations were con-ducted on an 80 m by 80 m lattice with a corner outlet using the CHILD landscape evolutionmodel. Figure from Solyom and Tucker (2004). Reproduced by permission of American Geo-physical Union.



excess) runoff. These process shifts have triggered marked differences ingeomorphic response. Channel network expanded and sediment yieldpeaked during permafrost. Channel network contracted and sedimentyield plummeted during non-permafrost (Bogaart et al. 2003).

Huang and Niemann (2006, 2008) have recognized the role ofgroundwater in their model by incorporating a dynamic two-dimensionalunconfined aquifer flow algorithm along with saturation and infiltrationexcess runoff generation mechanisms and evaporation from groundwater.The model was used to examine differences in modeled topography, basinhypsometry, and slope-area relations between surface runoff and baseflowdominated steady-state landforms (Figure 3). In close agreement withearlier models, Huang and Niemann (2008) predicted higher drainagedensity, smoother hillslope-valley transition, and lower landscape relief inthe surface runoff basin (Figure 3a,b). Deeply incised channels in themodeled topography were identified as a distinct topographic outcome ofdirect groundwater flow, which also manifested itself in basin hypsometry

Fig. 3. Steady-state topographies modeled by (a) Hortonian runoff, (b) groundwater discharge,and their (c) hypsometric curves and (d) slope-area relations. In the figure caption F representsinfiltration capacity. Figure from Huang and Niemann (2008), reproduced by permission ofWiley-Blackwell.



(Figure 3c). Earlier studies have reported higher values of hypsometricintegrals for groundwater-dominated basins (e.g., Laity and Malin 1985;Luo 2000). Interestingly, the Huang and Niemann (2008) model does notsupport this idea at erosional equilibrium, but instead offers theoreticalexplanations when such differences may be observed. The slope-are relationsof the two modeled basins exhibited notable difference in relation to theirhydrological response to rainfall under low and high infiltration conditions(Figure 3b) (Huang and Niemann 2008).

CLIMATE AND DOMINANT HYDROLOGICAL PROCESSES

Hydrology is fundamentally different in a range of climates. In humidvegetated landscapes where subsurface flow forms due to high infiltrationrates, the strong link between hydrology and landsliding have motivatedresearchers to monitor several zero-order basins in the Oregon CoastRange (Ebel et al. 2007a; Montgomery and Dietrich 2002; Montgomeryet al. 2002; Torres et al. 1998) and develop models to identify dominantprocesses (Casadei et al. 2003; Ebel et al. 2007b). These studies haveemphasized the critical role of antecedent soil moisture, rapid unsaturatedflow response, and shallow fracture flow in landsliding of steep forestedhillslopes; and pointed out the limitations of the steady-state topographydriven flow assumptions used in LEMs. Significance of pipe flow, subsurfacesaturation in bedrock depressions, and connectivity of flow paths in the soilhave also been identified in humid hillslopes (Tromp-van Meerveld andMcDonnell 2006a,b). In a recent study, Tarolli et al. (2008) argues thatlandslide initiation can be highly sensitive to variable soil thickness andbedrock outcrops in altering saturation levels in colluvial hollows. In light ofthese field observations, the steady-state subsurface flow hydrology approachesused in LEMs reviewed above may be improved and tested against data.

In arid and semiarid regions hydrology is strongly dictated by spatialvegetation patterns and connectivity between bare and vegetated patches(e.g., Mayor et al. 2008). For example, in shrublands (Figure 4a), runoffand erosion is higher because of interconnected bare soil patches withlow soil infiltration (Abrahams et al. 1998; Neave and Abrahams 2002;Wainwright et al. 2000), than in savanna ecosystems, such as the Juniperpine-grass savanna in Figure 4b with dense grass cover, where vegetationretains moisture and nutrients within the hillslope. With vegetation lossas a result of droughts or human disturbances net hilslope runoff, erosion,and nutrient losses increase dramatically (Davenport et al. 1998; Reidet al. 1999; Wilcox et al. 2003). In a semiarid headwater basin in MewMexico, USA, Gutiérrez-Jurado et al. (2007) illustrates how ecosystemand soil differences in opposing north- and south-facing slopes alter soilmoisture along a hillslope and cause differential hillslope erosion response.These fundamental differences in hydrology among different climatespresent challenges for the development of climate-sensitive LEMs.



A direct impact of vegetation on catchment geomorphic response canbe discussed within the context of the Huang and Niemann (2008)model. A change in the rate of infiltration from a lower to a higher value(for example, with denser vegetation growth as climate gets wetter) would

Fig. 4. Vegetation patterns in a typical semiarid ecosystem in central New Mexico, USA: (a)south-facing shrubland. Frequent runoff on interconnected bare soil patches lead to extensivehollow development; (b) North-facing juniper pine-grass savanna (see ~1.8 m-tall people forscale). Runoff is observed rarely only during high-intensity events. Water is retained in the soilduring the rainy season for evapotranspiration.



stop runoff on hillslopes, promote baseflow, saturation excess runofferosion, and alter flow hydrograph characteristics. In a coupled holisticmodel (Figure 1), such changes in hydrology would propagate into thestate of many coupled biotic and abiotic variables.

Impacts of Vegetation in Landscape Evolution

A considerable body of initial numerical modeling work has explored theimplications of forest disturbances by wildfire and harvest on sedimentyields over millennial time scales (Benda and Dunne 1997; Gabbet andDunne 2003; Istanbulluoglu et al. 2004; Lancaster et al. 2003). In thesemodels, forest enhances slope stability by providing root strength charac-terized by root cohesion. The majority of those models operated on fixedDigital Elevation Models (DEMs). Kirkby and coworkers incorporatedvegetation to their one-dimensional hillslope profile evolution model, inwhich vegetation cover influenced erosion indirectly by reducing surfacerunoff (Kirkby 1995; Kirkby et al. 1990). The model was used to forecastimpacts of climate change on hillslope evolution in a Mediterraneanclimate (Kirkby 1995), and evaluate aspect control on valley asymmetry(Kirkby et al. 1990). Tucker and Bras (1999) recognized the two-waycoupling between vegetation and runoff erosion, and developed a coupleddynamical model using the detachment-limited erosion law based on thefollowing three critical ways of interactions: (1) vegetation increases runofferosion threshold; (2) erosive floods disrupt vegetation; and (3) vegetationgrows back after disruption:

Local erosion: (3)

Erosion threshold: (4)

Vegetation disruption-growth (5)

where ∂z/∂ t is the rate of change of elevation, τb is boundary shear stress,τc is critical shear stress for detachment (erosion threshold), and kes and pe

are empirical parameters. The erosion threshold under the influence ofvegetation (equation 4) was conceptualized as a linear function of vegetationcover, where τc,s is critical shear stress due to soil resistance, τc,V is additivevegetation threshold imparted by full vegetation cover, and V is surfacecover fraction (0 ≤ V ≤ 1). Tucker et al. (2006) reported critical thresholdshear stresses gathered from the literature for bare and grass-covered soil.In the vegetation disruption-growth function (equation 5), kv is anhypothesized vegetation destruction coefficient by fluvial incision, and

∂∂z

tk Ve b c

pe= − −[ ( )] ,τ τ

τ τ τc c s c VV V( ) ,, ,= +

dV

dt

k V V

TV

v b c

v

=− −

−

⎧⎨⎪

⎩⎪

[ ( )]

( )

τ τ1

1



Tv is vegetation growth time-scale. This growth function is based on apopulation growth model commonly used in ecology (e.g., Tilman 1994).In the strict sense, the vegetation-erosion coupling model summarizedabove is applicable to grasslands in well-watered conditions.

Incorporating this theory in the CHILD LEM, Collins et al. (2004)explored the topographic implications of grass vegetation over time scalesof catchment evolution. As a consequence of having a larger erosionthreshold, the vegetated basin developed steeper slopes in both hillslopesand channels (to balance uplift with denudation), higher relief, and a lowerdrainage density as compared to its bare counterpart. A highly episodicerosion regime was also among the characteristics of vegetation-erosioncoupling identified by Collins et al. (2004) in their numerical experiments.

Istanbulluoglu and Bras (2005) extended the model of vegetation-erosion interactions by relating hillslope diffusivity negatively to surfacevegetation cover; incorporating a critical slope threshold for landslidingderived from the infinite slope stability model as a function of treeroot cohesion; and introducing different growth time scales for trees andunderstory cover. Vegetation destruction by landsliding was modeled byremoving the vegetation cover in the model domain where a landslideoccurred. Istanbulluoglu and Bras (2005) also modified and used a shearstress partitioning model advocated by Foster et al. (1980), by relatingoverland flow friction to vegetation cover.

Using uplift and climate forcing representative of the current conditionsin the Oregon Coast Range, Istanbulluoglu and Bras (2005) conducted aseries of numerical modeling experiments with the CHILD model.Figure 5 shows equilibrium basins modeled for bare soil, static forestvegetation, and dynamic forest vegetation conditions. High drainagedensity and low relief in the bare basin (Figure 5a) resembles semiariddrainage networks devoid of vegetation cover. Steeper slopes, a less dis-sected drainage network, and increased relief, are three notable differencesbetween the bare and fully vegetated landscapes (Figure 5a and 5b). Asfluvial incision becomes negligible under dense vegetation cover in themodel, hillslopes steepen with continuing uplift until a critical slopethreshold for landsliding is exceeded. This uplift-erosion interplay,modulated by vegetation, leads to a switch in the dominant form ofhollow erosion from runoff to landslides. This interesting model outcomeillustrates the implications of a one-way interaction (vegetation enhanceerosion threshold) on landscape morphology.

When an abiotic-biotic feedback is introduced – floods and landslideslocally kill vegetation and vegetation grows back – the model outcome wasa lower-relief, and a more dissected topography than the static vegetationcase, with smaller landslide-dominated hollows entering a more tortuousfluvial network (Figure 5c). When random wildfires were included in themodel, the drainage density further increased and relief declined (Figurenot included). The dynamic vegetation simulations demonstrated how a



Fig. 5. Modeled topographies using CHILD in a 700 m by 700 m model domain for (a) baresoil; (b) static vegetation cover; (c) dynamic vegetation cover. Figure from Istanbulluoglu andBras (2005). Reproduced by the permission of American Geophysical Union.



window of opportunity for erosion to attack the barren landscape duringvegetation recovery impacts morphology; and underscored the importanceof local vegetation disturbance-growth dynamics on large-scale landscapeorganization. DEM analysis of landscape morphology at least partly corroboratesthese model results (Istanbulluoglu et al. 2008; Tucker et al. 2006).

The absence of an ecohydrological link between climate and vegetationlimits the use of the aforementioned models for studying climate changeimpacts explicitely. Istanbulluoglu and Bras (2006) developed a stochasticmodel of one-dimensional ecohydrological water balance with soil moisture-dependent grass growth, and examined the relation between sedimentyield (represented by an index) and climate for different soil types. Themodel reproduced the general shape of the Langbein and Schumm (1958)curve which shows a peak in the semiarid precipitation range with decayingtrends on both sides of the peak as precipitation gets lower and higher.The peak sediment yield in the intermediate climate range was attributedto ecosystem transition form shrubland to grassland. Multiple peaks werealso reported in much wetter regions with mean annual precipitations upto 2500 mm (Walling and Kleo 1979).

In their model, the regime shift in sediment yield occurs with significantgrowth of grass cover. Interestingly, in the modeled curves, location of thepeak varies with soil texture (Figure 6). Coarser soils are more favorablefor vegetation growth in water-limited ecosystems (Laio et al. 2001;

Fig. 6. Modeled sediment transport potential index (STI) as a function of mean annual precipita-tion for clay, loam and loamy sand, normalized by the mean STI of loam; and the Langbeinand Schumm (1958) curve normalized by its mean. Figure from Istanbulluoglu and Bras (2006).Reproduced by the permission of American Geophysical Union.



Rodriguez-Iturbe and Porporato 2004). As such, grass cover establishes andsediment transport begins to drop for loamy sand first, followed by loamand clay textural types as mean annual precipitation increases. These resultsillustrate the ecohydrological significance of soil texture on sediment trans-port. Istanbulluoglu and Bras (2006) pointed out that while humid regionscould be more sensitive to climate change impacts, the vegetation-erosioninteractions are far more tightly coupled in semi-arid to sub-humid climates.Building on this idea, Collins and Bras (2008) examined the impacts ofshort-term climate (50 years) and land cover changes on landscape responseusing simulated equilibrium catchments as initial conditions.

Biotic Processes and Hillslope Sediment Transport

The recent modeling of vegetation-erosion interactions summarized aboveis limited to fluvial processes and partly landsliding. Denudation rate andhilltop morphology of soil-mantled hillslopes, however, are predominantlycontrolled by soil production by bedrock weathering and transport by soilcreep. Recent research further demonstrates strong connections betweenthese two processes moderated by biota (Roering 2008; Yoo et al. 2005).Soil development over rock is a precondition for the establishment of soilflora and fauna where climate permits. Once established, life shifts theform of dominant soil creep mechanisms from physical disturbances(Gabet 2003) to biotic disturbances (e.g., Gabet 2000; Gabet et al. 2003).

In arid and semiarid climates with bare or sparsely vegetated soils,hillslope diffusion is predominantly controlled by rainsplash, expansionand contraction of sediments due to freeze-thaw, wet-dry cycles, and dryravel (e.g., Gabet 2003; Hanks 2000). Over decadal time scales some fieldobservations show up to an order of magnitude higher soil creep rates inbare and loosely vegetated soils compared to those vegetated (Carson andKirkby 1972; Jahn 1981, 1989; Selby 1993; Young 1972). In vegetatedlandscapes bioturbation (tree throw, root growth-decay, burrowinganimals) dominate soil creep and efficiently cause soil mixing and turnover(e.g., Gabet et al. 2003; Roering et al. 2000). The non-linear form ofhillslope diffusion model that may be used both for steep and gentle slopesis (Roering et al. 1999):

(6)

where Kd is hillslope diffusivity constant and Sc is the critical hillslopegradient for threshold slopes. Both parameters of this model exhibitconsiderable variations relative to climate and vegetation. Higher Kd valueswere reported for forested humid basins undergoing bioturbation (Blackand Montgomery 1991; Nash 1994; Roering et al. 2000) compared tothose of sparsely vegetated arid environments (see, for example, Hanks

qK S

S Ssd

d

c

=−1 2( / )

,



2000; Martin 2000). Roering et al. (2001) reported Sc = 0.56 ± 0.2 for anexperimental sand pile, while forested hillslopes of the Oregon CoastRange took a significantly higher range Sc = 1.27 ± 0.16 (Roering et al.1999). Increase in Sc with vegetation can be attributed to the long-termbinding effects of roots holding the soil against sliding as well as thesheltering effects of vegetation canopy against rainsplash. Recent datafrom the Oregon Coast Range indicate that on average approximately4 m3 and as high as ~16 m3 soil is displaced by each tree-throw event(Mort 2003; Roering et al. 2004). This episodic nature of tree throw mayexplain why over short time scales abiotic creep could be larger than bioticcreep, as reported in earlier studies. Short-term variations in vegetationcover also result in marked differences in the parameters of equation (6).For example, following wildfires Roering and Gerber (2005) reportedclose to a factor of four increase in Kd and as high as 20% decrease inSc. Their study underscores the dramatic landscape response to suddenvegetation loss in threshold or near-threshold vegetated slopes.

In an attempt to explicitly model the mechanistic influence of biota onsoil creep, Yoo et al. (2005) related the rate of downslope creep transportto burrowing animals’ energy expenditure on soil disturbance. In the Yooet al. (2005) model, gopher population density increases with soil depthuntil a finite depth. This provides a link between creep rates and soilproduction, introducing a temporal dimension to modeling depth-dependentsoil creep (Braun et al. 2001; Furbish and Fagherazzi 2001; Heimsathet al. 2005). Recognizing that soil-disturbing agents’ activity (e.g., rootgrowth-death, population of gophers) diminish systemically with soil depth(e.g., Roering et al. 2000), Roering (2008) proposed a depth-dependentexpression for Kd, relating depth-integrated energy expenditure by rootdensity to downslope sediment flux. This new model closely predicts theobserved hillslope morphology in the Oregon Coast Range.

During climate transitions in the past many landscapes have alternatedbetween grass, shrub, and forest ecosystems. Studies focusing on the lastglacial-interglacial transition in New Zealand estimated hillslope diffusioncoefficients (from gully deposits) for the Holocene forests much larger thanthose of the late Pleistocene grasslands (Roering et al. 2000, 2004). Formodeling the impacts of climate and landuse change, field based studiesthat explicitly relate sediment transport to life (vegetation and animals) willguide the development of future geomorphic transport laws. Currently,biotic and other abiotic mechanistic models, such as those for dry ravel (Gabet2003), and frost cracking of bedrock (Anderson 2002; Hales and Roering2007) have not been coupled for holistic landscape evolution modeling.

Discussions and Outlook

In this article, we argued the need for a holistic eco-hydro-geomorphicapproach for modeling the role of climate on catchment sediment yields



and geomorphic evolution. The conceptual diagram in Figure 1 demon-strates the author’s opinion of the processes and landscape state variablesthat should be represented in such an LEM. We discussed the currenttheory and models quantifying the interactions between biotic and physicalprocesses. Among the landscape state variables in Figure 1, relativelylittle is known about the function of soils in catchment evolution. Soil playsa fundamental role in the establishment of life, landscape-ecosystemcoupling, and geomorphic transport (Black and Montgomery 1991; Gabetet al. 2003; Graf 1979; Roering 2008; Yoo et al. 2005), and regulatesurface and subsurface hydrology, and the partitioning of energy and wateron complex terrain (Bertoldi et al. 2006). Numerous feedback mechanismsamong soils, vegetation dynamics, and topography lead to changes in thephysical properties of soils over time (Eagleson 1982; Lohse and Dietrich2005; Shafer et al. 2007), which may have propagating consequences onthe whole eco-hydro-geomorphic system behavior, including the productivityand spatial dominance of vegetation functional types (Caylor et al. 2005;Ivanov et al. 2008). The one-dimensional model results presented inFigure 6 suggest a strong ecohydrological control of soil texture in therelation between sediment yield and mean annual precipitation. In additionto the mechanistic properties of soils that interact with geomorphic trans-port laws (Dietrich et al. 2003; Selby 1993), there is immediate need torecognize their ecohydrological significance in geomorphic modeling.

In Figure 1, biomass, a variable often simulated in ecological models, isused as the vegetation state variable because other vegetation propertiessuch as the Leaf Area Index and surface cover fraction (Hanson et al.1988; Montaldo et al. 2005), root cohesion (Lancaster et al. 2003; Sakalsand Sidle 2004) and wash erosion thresholds (Graf 1979) may be relatedto root and shoot biomass. The predictions of Istanbulluoglu and Bras(2005) demonstrate a strong landscape-scale geomorphic signature of localvegetation disturbances in headwater catchment evolution (Figure 5). Thisunderscores the importance of the variability of plant properties in finespecial and temporal resolutions on landscape structure. The dominantclimate-ecology-landscape interactions controlling space-time dynamics ofplants during long, high-amplitude climate fluctuations (e.g., Pleistocene-Holocene climate transition) can be fundamentally different than thoseconcerning the present global warming trends. Therefore, developingmodels to serve for both long- and short-term climate change purposeswill be challenging. In the case of the former, with strong trends in themean properties of the climate and their geographic distribution, equilibriumbiogeography models may be adapted and used in LEMs. These modelswere used to simulate global vegetation patterns of past climates withinthe Paleoclimate Modelling Intercomparison Project (e.g., Harrison et al.2002). Equilibrium biogeography models require basic climate inputs suchas seasonal temperatures and precipitation (Harrison et al. 1998; Peng2000; Wohlfahrt et al. 2004; Wu et al. 2007).



Subtle changes in annual temperatures, large alterations in storm inter-mittency and interannual variability, growing number of dry spells andflash floods typically characterize the nature of today’s climate change(e.g., Easterling et al. 2000; Groisman et al. 2004). Ecosystem and hydrologyresponse to growing climate variability are often predicted by DynamicVegetation Models (DVMs) that explicitly simulate water balance in thevertical dimension and detailed ecophysiological processes (Arora 2002;Cramer et al. 2001). These models have been traditionally used withinregional or global climate models at coarse spatial resolutions to examinelarge-scale consequences of climate change. Although simplified versions ofDVMs have shown good comparisons with field observations (e.g., Montaldoet al. 2005; Williams and Albertson 2005), these models require a largenumber of input parameters and often work at a minute to hourly timesteps, making them less attractive for geomorphic modeling purposes.

Another shortcoming of biogeography models and DVMs are that theydo not allow cell-by-cell interactions. Yet, spatial ecohydrological interactionsare critical for both dry and wet climates. By mediating plant migrationand establishment, and lateral soil moisture and runoff redistribution insemiarid climates, these interactions are responsible for the emergence of awide range of spatial vegetation and hydrological patterns (Gutiérrez-Juradoet al. 2007; Ivanov et al. 2008; Thompson and Katul 2008; van Wijk andRodriguez-Iturbe 2002). In humid climates lateral subsurface flow,following a variety of different flow paths, and its interaction with bedrocksignificantly contributes to geomorphic transport. In order to capture thesesalient aspects of eco-hydro-geomorphology in different climates spatialecohydrological interactions will need to be accounted for in LEMs.

Clearly, there is no off-the-shelf ecology model to directly couple withan LEM, but yet the literature offers a rich set of models and basicecological understanding to geomorphologists’ advantage. Parsimoniousvegetation growth models, similar in spirit to the geomorphic transportlaws, a compromise between mechanistic DVMs and heuristic colonization-mortality models (e.g., Tilman 1994) can be developed. In that regard,growing work in ecohydrological modeling offers promising opportunities.For example, the van Wijk and Rodriguez-Iturbe (2002) tree-grass com-petition model simulates spatial organization of savanna ecosystems basedon a ‘bucket’ representation of local hydrology and lateral seed dispersal.The simplicity of this model with low number of parameters allowslong-term spatial simulations. The van Wijk and Rodriguez-Iturbe (2002)model and other more generic ecological cellular-automata models (e.g.,Scanlon et al. 2007) may be used in LEMs with minor modifications. Forsimulations that require switches between climates, rules used in equilibriumbiogeography models to identify climax plant functional types may beapplied. Such models may be coupled with the recent notable contributionsin hydrological LEMs with some modifications (e.g., Huang and Niemann2006, 2008).



Significant research is needed to explore the two-way interactionsbetween biotic and geomorphic processes. Lack of knowledge in this areahampers the development of numerical models. We know relatively littleabout how plants influence geomorphic transport, but also importantlyhow geomorphic disturbances impact plant communities (e.g., loss ofplant biomass with fluvial incision) and their physiological activities. Somedependencies were reported between the location and size of landslidescars and the spatial distribution of trees, their age, root density, and rootstructure (Abe and Zimmer 1991; Schmidt et al. 2002; Roering et al.2003). In river channels, morphodynamic feedbacks were shown tocontribute to the development of distinct channel patterns (see reviewby Murray et al. 2008). To further examine the two-way biotic-abioticprocess interplay, field research in space and time scales commensuratewith those of the phenomena of interest will be critical. Recentdevelopments in remote sensing technology including LIDAR, andNASA’s Earth Observing System satellites may serve for this purpose inactively eroding regions.

In summary, the author is in the opinion that simple models should bedeveloped that: (1) capture dominant forms of climate-soil-vegetation-erosion interactions in different climates; (2) allow ecosystem shifts inresponse to climate change; and (3) characterize realistic time scales forwindow of opportunity for erosion when vegetation is sparse. To guideand promote field observations and modeling, an eco-hydro-geomorphicbasin classification system, involving certain aspects of regional climate,hydrology, ecology, and geology, would be critical to advance this excitingnew landscape system modeling endeavor.

Acknowledgements

I thank Jeff Niemann and X Huang for providing Figure 3. Funding fromthe US National Science Foundation (NSF, EAR-0819923) supported thewriting of this article and some of the research presented here.

Short Biography

Erkan Istanbulluoglu is an Assistant Professor at the School of NaturalResources and Biological Systems Engineering at the University ofNebraska, Lincoln, NE, USA. He holds BS, and MS degrees inAgricultural Engineering from Uludag University, Turkey, and a PhD inCivil and Environmental Engineering from Utah State University. Hisactive and future research interests include geomorphology: studyinggeomorphic processes, modeling landscape evolution, vegetation-erosioncoupling in landscape evolution; and hydrology: data analysis andmodeling of water balance in river basins, evapotranspiration, andecohydrological vegetation dynamics.



Note

* Correspondence address: Erkan Istanbulluoglu, University of Nebraska, School of NaturalResources, 519 Hardin Hall, 3310 Holdrege Street, Lincoln, NE 68583, USA. E-mail:[email protected].

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