Catchment erosion and sedimentation are influenced by variations in the rates of rock uplift (tectonics) and periodic fluctuations in climate and vegetation cover. This study focuses on quantifying the effects of changing climate and vegetation on erosion and sedimentation over distinct climate–vegetation settings by applying the Landlab–SPACE landscape evolution model. As catchment evolution is subjected to tectonic and climate forcings at millennial to million-year timescales, the simulations are performed for different tectonic scenarios and periodicities in climate–vegetation change. We present a series of generalized experiments that explore the sensitivity of catchment hillslope and fluvial erosion as well as sedimentation for different rock uplift rates (0.05, 0.1, 0.2 mm a
The pioneering work of Grove Karl Gilbert (Gilbert, 1877) highlighted the fact that surface uplift, climate, and biota (amongst other things) jointly influence catchment-scale rates of weathering and erosion. In recent decades a wide range of studies have built upon these concepts and quantified different ways in which climate, tectonics, or vegetation cover influence rates of erosion and sedimentation. For example, recent work highlights the fact that denser vegetation and lower precipitation both decrease erosion (Alonso et al., 2006; Bonnet and Crave, 2003; Huntley et al., 2013; McPhillips et al., 2013; Miller et al., 2013; Perron, 2017; Schaller et al., 2018; Starke et al., 2020; Tucker, 2004). In addition, periodic changes in climate (such as changes driven by Milankovitch timescale orbital variations) have also been recognized as influencing rates of catchment erosion and sedimentation (Braun et al., 2015; Hancock and Anderson, 2002; Hyun et al., 2005; Schaller et al., 2004), although our ability to measure orbital-timescale-induced erosional changes can be challenging (e.g., Schaller and Ehlers, 2006; Whipple, 2009). Several studies have also documented how the combined effects of either climate and vegetation change or variable rates of rock uplift and climate change (including glaciation) impact catchment-scale processes (Herman et al., 2010; Mishra et al., 2019; Schmid et al., 2018; Tucker, 2004; Yanites and Ehlers, 2012). Taken together, previous studies have found that the long-term development of topography (such as over million-year timescales) is in many situations sensitive to the tectonic, climate, and vegetation history of the region and that competing effects of different coeval processes (e.g., climate change and tectonics) exist but are not well understood.
Quantification of climate, vegetation, and tectonic effects on catchment erosion is challenging because these processes are confounded and can, if coupled, have opposing effects on erosion and/or sedimentation. For example, precipitation has both direct (positive) and indirect effects on erosion that operate via vegetation cover. Namely, plants require water to grow and survive, and vegetation cover is usually positively affected by precipitation both on a global scale (i.e., when comparing biomes across latitudinal gradients) and on a regional or local scale (e.g., Huxman et al., 2004; Sala et al., 1988; Zhang et al., 2016). Though vegetation cover is also influenced by temperature, seasonality, and many other abiotic factors such as soil type and thickness, the positive relationship of biomass and cover with water availability is rather general. For example, in dry ecosystems such as hot deserts and Mediterranean systems, vegetation cover is primarily limited by water availability and is therefore very low. As precipitation increases, vegetation cover increases rapidly, although water availability can still be the limiting factor in addition to other factors (Breckle, 2002). In temperate systems, wherein water is abundant and soils are well developed, plant growth is primarily limited by low winter temperatures. Overall, the relationship between precipitation and vegetation cover follows a saturation curve with large sensitivity (e.g., measured as rain use efficiency – RUE) to precipitation in arid to Mediterranean systems and low sensitivity in temperate or tropical systems (Gerten et al., 2008; Huxman et al., 2004; Yang et al., 2008; Knapp et al., 2017).
Previous modeling and observational studies have made significant progress in understanding the interactions between surface processes and either climate (Dixon et al., 2009; Routschek et al., 2014; Seybold et al., 2017; Slater and Singer, 2013), vegetation (Acosta et al., 2015; Amundson et al., 2015; Istanbulluoglu and Bras, 2005), or coupled climate–vegetation dynamics (Dosseto et al., 2010; Jeffery et al., 2014; Mishra et al., 2019; Schmid et al., 2018). Over geologic (millennial to million-year) timescales, observational studies of these interactions are impossible (or require proxy data) and numerical modeling approaches provide a means to explore interactions between climate, vegetation, tectonics, and topography. The first observational study of this kind suggested that high MAP (mean annual precipitation) is associated with denser vegetation, hence resulting in lower erosion rates (Davy and Lague, 2009). One of the first numerical modeling studies implementing a vegetation–erosion coupling was conducted by Collins et al. (2004). This study was followed by work from Istanbulluoglu and Bras (2006), which quantified the effect of vegetation on landscape relief and drainage formation. More recently, work by Schmid et al. (2018) included the effects of transient climate and vegetation coupled with a landscape evolution model to predict topographic and erosional variations over millennial to million-year timescales. However, Schmid et al. (2018) presented a simplified approach to consider hillslope and detachment-limited fluvial erosion and only considered a homogeneous substrate. Other studies have documented the fact that sediment or bedrock erosion by rivers is not dominated purely by detachment-limited (Howard, 1994) or transport-limited fluvial erosion (Willgoose et al., 1991). Rather, it often involves a combination of or transition between the two conditions (e.g., Pelletier, 2012). Given this, treatment of bedrock erosion and sediment transport for mixed bedrock–alluvial streambeds provides a more realistic framework for understanding the influence of climate, vegetation, and tectonic processes on topographic development. Recent work (Shobe et al., 2017) presented an additional component (SPACE) to the Landlab surface process model. SPACE allows for the simulation of mixed detachment–transport-limited fluvial processes, including separate layers for bedrock and loose sediment. Finally, the sensitivity of topography to different rock uplift rates in variable climate–vegetation settings has not yet been investigated. The combined interactions of tectonics (rock uplift) and variable climate and vegetation warrant investigation given the significant influence of rock uplift on mean elevation, erosion rates and river channel profiles (Kirby and Whipple, 2012; Turowski et al., 2006), and hillslopes.
In this study, we complement previous work and investigate the transient
landscape response for mixed bedrock–alluvial systems. We do this for different rates of rock uplift and periodic changes (Milankovitch cycles) in
precipitation and vegetation. Our focus is on erosion and sedimentation changes occurring over millennial to million-year timescales. Sub-annual to
decadal-scale changes are beyond the scope of this study. More specifically,
this study evaluates the following two hypotheses: first, if vegetation
cover and climate vary on Milankovitch timescales, then any increases or
decreases in catchment erosion will be more pronounced over longer (e.g., 100 kyr) rather than shorter (e.g., 21 kyr) periodicities due to the longer duration of change imposed. Second, if increasing rates of tectonic uplift cause increases in catchment erosion rates, then any periodic variations in climate and vegetation cover will be muted (lower amplitude) at higher uplift rates because the effect of rock uplift on erosion will outweigh climate and vegetation change effects. Given the complexity of this problem, we investigate these hypotheses through numerical landscape evolution modeling using a stepwise increase in model complexity whereby the
contributions of individual processes (i.e., climate, vegetation, or
tectonics) are identified separately before looking into the fully coupled
system and resulting interactions. We apply a two-dimensional coupled
detachment–transport-limited landscape evolution model for fluvial processes. In addition, hillslope diffusion (Johnstone and Hilley, 2014) and weathering and soil production (Ahnert, 1977) processes are considered. Although this study is primarily focused on documenting the predicted sensitivity of catchments to variations in tectonics, climate, and vegetation change, we have tuned our model setup to the conditions along the Chilean Coastal Cordillera (Fig. 1), which features a similar tectonic setting but an extreme climate and ecological gradient. This was done to provide realistic parameterizations for vegetation cover and precipitation in different ecological settings. This area is also part of the German–Chilean priority research program, EarthShape: Earth Surface Shaping by Biota (
The representative study areas in the Chilean Coastal Cordillera used for the model setup. The model parameters were loosely tuned to the
climate and vegetation conditions in these areas (Schmid et al., 2018). The Pan de Azucar area in the north neighbors the Atacama Desert and has sparse vegetation cover (10 %) and an arid climate (30 mm yr
We apply the landscape evolution model, Landlab (Hobley et al., 2017), using the SPACE 1.0 module of Shobe et al. (2017) for detachment- vs. transport-limited fluvial processes. The Landlab–SPACE programs were modified for vegetation-dependent hillslope and fluvial erosion using the approach of Schmid et al. (2018). In general, the geomorphic processes considered involve weathering and regolith production calibrated to the Chilean Coastal Cordillera observations of Schaller et al. (2018), vegetation-dependent coupled detachment–transport-limited fluvial erosion, and depth-dependent hillslope diffusion. The model parameters (i.e., bedrock and sediment erodibility and diffusion coefficient) in the simulations are based on those of Schmid et al. (2018). A detailed explanation of the weathering, erosion, sediment transport, and deposition processes is provided in Appendix A, and a summary of model parameters used is given in Table A1.
The model consists of a 10 km by 10 km rectangular grid with 100 m node
spacing (Fig. 2a), with a total domain area of 100 km
Model geometry as well as climate and vegetation forcings used in this study.
Bedrock elevation and sediment cover thickness are considered to be separate layers to quantify simultaneous bedrock erosion and sediment entrainment across the model domain. Simulations were conducted for 15 Myr to generate a
steady-state topography with the mean values of precipitation and vegetation
cover for the two study areas. The rates of rock uplift are kept constant
during the steady-state simulations and subsequently in the transient stage
with oscillating vegetation cover and precipitation. After the development
of a steady-state topography, transient forcings in vegetation cover and
mean annual precipitation (MAP) (Fig. 2b) were introduced for 3 Myr. Vegetation cover varied by
Changes in vegetation cover are driven by climatic variations; MAP has been shown to be much more influential than temperature changes, especially in relatively dry regions (e.g., Mowll et al., 2015) and in grasslands (e.g., Sala et al., 1988). Many previous studies have shown that annual primary production (ANPP) and associated vegetation cover increase linearly (Mowll et al., 2015; Xia et al., 2014) or in an asymptotic manner with MAP (Huxman et al., 2004; Smith et al., 2017; Yang et al., 2008; Zhang et al., 2016; Knapp et al., 2017). These findings are also highly consistent among different approaches such as global (Gerten et al., 2008) and regional (Zhang et al., 2016) models, field and remotely sensed observations across biomes and among years (Huxman et al., 2004; Xia et al., 2014; Yang et al., 2008), and rapid vegetation responses to rainfall manipulation experiments (Smith et al., 2017). An asymptotic relationship appears to be the more common case, especially when looking at warm and dry ecosystems, i.e., regions up to approximately 600 mm MAP (Huxman et al., 2004; Mowll et al., 2015). Here, it has been demonstrated that the sensitivity of ANPP to MAP decreases from more water-limited systems such as deserts to Mediterranean and temperate regions (Huxman et al., 2004; Yang et al., 2008). Namely, the same increase in MAP will yield a much larger increase in vegetation cover in dry regions than in wetter ones. To implement these effects, we use an empirical approach based on vegetation–precipitation relationships observed in the Chilean Coastal Cordillera (see Schmid et al., 2018, for details) to estimate what mean annual precipitation rates are associated with different vegetation cover amounts (Fig. 2b and c).
The effects of vegetation cover on hillslope and fluvial processes are modified from the approach of Schmid et al. (2018); see also the Appendix and
Table A1. Briefly, we applied a slope- and depth-dependent linear diffusion
rule following the approach of Johnstone and Hilley (2014). The diffusion coefficient (
Graphical representation of the range of vegetation-dependent diffusion coefficient (
As the study areas exhibit similar granitoid lithology, the erosional parameters (Table A1) are kept uniform for both the study areas. However,
parameters based on climate conditions, namely soil production rate (Schaller et al., 2018), MAP, and vegetation cover (Schmid et al., 2018), are different for these areas. The vegetation cover and precipitation rate are kept uniform across the model domain due to low to moderate relief in target catchments (
The model scenarios considered were designed to provide a stepwise increase in model complexity to identify how variations in precipitation, vegetation
cover, or rock uplift rate influence erosion and sedimentation. The model
scenarios include the following.
Influence of oscillating precipitation and constant vegetation cover on erosion and sedimentation (Figs. 4a and 5, Sect. 3.1) Influence of constant precipitation and oscillating vegetation cover on erosion and sedimentation (Figs. 4b and 6, Sect. 3.2) Influence of coupled oscillations in precipitation and vegetation cover on erosion and sedimentation (Figs. 4c and 7, Sect. 3.3)
Influence of different periodicities of precipitation–vegetation change on erosion and sedimentation (Fig. 8, Sect. 3.4) Influence of rock uplift rate and oscillating precipitation–vegetation on erosion sedimentation (Fig. 9, Sect. 3.5)
The porosity (0.2) used in this study is lower than the usual range for soil (0.3–0.4), as sediment produced as a result of weathering in the study
areas is a mixture of fine- and coarse-grained regolith (Schaller et al., 2020). Manning's numbers for bare soil and reference vegetation cover are the same as used by Schmid et al. (2018). The rate of rock uplift is kept temporally and spatially constant (0.05 mm a
Graphical representation of the different precipitation and vegetation forcings applied to the model scenarios described in the text.
Forcings for sparse vegetation (10 %) cover are shown on the left and dense vegetation (70 %) cover on the right. Scenarios explored include
An initial low-relief (
In the following sections, we focus our analysis on the mean catchment sediment thickness (i.e., the combined thickness of soil and regolith) over the entire domain, mean bedrock erosion rates (excluding sediment erosion), mean sediment entrainment rates, and mean catchment erosion rates. The mean catchment erosion rates are the sum of bedrock erosion and sediment entrainment rates. To simplify the presentation, results are shown only for the first cycle of transient climate and vegetation change. Results from the first cycle were representative of subsequent cycles (not shown), and no longer-term variations or trends in erosion–sedimentation were identified or warrant discussion.
In this scenario, with a rock uplift rate of 0.05 mm a
Temporal evolution of catchment-averaged predictions for scenario 1 described in the text (Sect. 3.1). Graphical representation of mean catchment sedimentation and erosion to
The absence of a phase lag between the mean catchment erosion and precipitation rates reflects the fact that the combined sediment entrainment and bedrock erosion rates when added together track the overall trend in precipitation rate changes, but the individual components (sediment vs. bedrock) respond differently.
Results from this scenario with constant mean annual precipitation (at the
mean value of the previous scenario) and oscillating vegetation cover (Figs. 4b and 6a) show a starkly different catchment response from scenario 1 (Sect. 3.1). The sediment entrainment rates for both simulations (Fig. 6b) show a small decrease in entrainment as vegetation cover increases (e.g.,
Temporal evolution of catchment-averaged predictions for scenario 2 described in the text (Sect. 3.2). Graphical representation of mean catchment sedimentation and erosion to
The range of mean catchment sediment thickness varies significantly in the
simulations (e.g.,
The catchment response to coupled oscillations in precipitation rate and
vegetation cover (Fig. 4c) for erosion and sedimentation represents a composite of the effects discussed in the previous two sections (Fig. 7). For
example, the mean catchment sediment entrainment rates have a peak in
entrainment rates (
Temporal evolution of catchment-averaged predictions for scenario 3 described in the text (Sect. 3.3). Graphical representation of mean catchment sedimentation and erosion to
Mean catchment sediment thicknesses in the 10 % vegetation cover simulation show a modest response and vary between 1.16 and 1.24 m (Fig. 7c), with a time lag of
The amplitude of change in bedrock erosion is 0.05–0.06 mm yr
Finally, the mean catchment erosion rates (Fig. 7e) again show the combined
effects of the sediment entrainment rate and bedrock erosion histories
previously discussed (Fig. 7b and d). In the simulation with 70 % initial
vegetation cover, the mean catchment erosion rates follow the pattern of
changes in precipitation rates (e.g., ranging from 0.04 to 0.1 mm yr
Temporal evolution of catchment-averaged predictions for scenario 4 described in the text (Sect. 3.4). Graphical representation of mean catchment sedimentation and erosion to
Here we show the influence of different periodicities (23, 41, and 100 kyr)
in precipitation and vegetation change on catchment erosion and sedimentation for the cases of a 10 % mean vegetation cover (Fig. 8) and 70 % vegetation cover (Fig. 9). We find higher variations in mean sediment entrainment rates (Figs. 8b and 9b) for both the 10 % and 70 % vegetation
cover simulations for the shorter periodicities (23 and 41 kyr). However, the phase lag in the peaks of sediment entrainment and precipitation rates was higher for longer periodicities (e.g.,
Temporal evolution of catchment-averaged predictions for scenario 4 described in the text (Sect. 3.4). Graphical representation of mean catchment sedimentation and erosion to
Overall variations in mean catchment erosion rates (Figs. 8e and 9e) were not
observed to be significant (
Here we investigate the response of mean catchment erosion and sedimentation
for different rates of rock uplift (i.e., 0.05, 0.1, 0.2 mm yr
Temporal evolution of catchment-averaged predictions for scenario 5 described in the text (Sect. 3.5). Graphical representation of mean catchment sedimentation and erosion with different rates of rock uplift [mm a
Temporal evolution of catchment-averaged predictions for scenario 5 described in the text (Sect. 3.5). Graphical representation of mean catchment sedimentation and erosion with different rates of rock uplift [mm a
In more detail, the temporal pattern of changes in sediment entrainment rates (Figs. 10b and 11b) is similar for all uplift rates considered, but the
amplitude of change increases as the uplift rate increases. In addition, the phase lag between the peaks in sediment entrainment rates and maximum precipitation rates in the 10 % vegetation simulation (Fig. 10b) varies with the rock uplift rate. For example, the peaks in sediment entrainment rates have a phase lag of
For the landscape with 10 % vegetation cover, the simulation with the highest rates of rock uplift (0.02 mm a
Temporal variations in bedrock and mean catchment erosion rates are similar to those described in Sect. 3.3 (Fig. 7) for the sparsely and more heavily vegetated conditions. The primary difference is that at high rock uplift rates the amplitude of bedrock or mean catchment erosion increases (Figs. 10d, e and 11d, e). To summarize, these results highlight the fact that regardless of the rock uplift rate, similar temporal changes are observed in sediment entrainment or thickness and in bedrock and catchment erosion for oscillating precipitation rates and vegetation cover. However, the amplitude of change (or absolute change) in entrainment and erosion rates increases with increases in rock uplift rate. This will be discussed in detail in Sect. 4.4.
In this section, we synthesize the results from previous sections (scenarios 1–5) in detail. We further investigate the effects of coupled climate and vegetation oscillations (scenario 3) on the occurrence of erosion and sedimentation on a spatial scale.
Here the sensitivity of erosion and sedimentation to variable precipitation
and/or vegetation cover is analyzed. In the scenario with oscillating
precipitation and constant vegetation cover, sparsely vegetated landscapes
(10 %) erode slowly during periods of lower precipitation. This
might be attributed to the dependency of the bedrock erosion and sediment
entrainment on the amount of water available through precipitation, which in
turn affects the erosion rates. The mean erosion in this scenario is dominated by bedrock erosion with a significant contribution from sediment
entrainment. Also, the mean erosion rates over one climate oscillation cycle
are observed to be slightly higher (
Similarly, in a scenario with constant precipitation and variable vegetation cover, sparsely vegetated landscapes (10 %) are observed to be much more sensitive in terms of erosion rates during periods of no vegetation cover. The amplitude of erosional change was 10 times higher than that of densely vegetated landscapes. The mean erosion in sparsely vegetated landscapes is dominated equally by bedrock erosion (Fig. 6d) and sediment entrainment due to the higher availability of bare soil. This justifies the argument of a higher sensitivity of sparsely vegetated landscapes to erosion and sedimentation. This result confirms the findings of Yetemen et al. (2015) (see Fig. 2g), which suggests that shear stress (erosion) decreases significantly (1 to 0.1) as the total grass cover (vegetation) is increased from 0 % (bare soil) to 20 %. Also, a small change in vegetation cover in densely vegetated landscapes would not result in significant differences in erosional processes. Unlike the previous scenario (oscillating precipitation and constant vegetation cover), we do not observe nonlinearity in erosion response to the changes in vegetation cover (i.e., mean erosion rates over one transient cycle are equal to steady-state mean erosion rates).
In general, mean catchment sediment thickness is observed to be inversely proportional to precipitation owing to higher stream power. This in turn translates to a higher sediment flux during wetter periods. The influence of oscillating precipitation and constant vegetation cover on sediment thickness is slightly higher in simulations with sparse vegetation cover. In simulations with constant precipitation and oscillating vegetation cover, the sensitivity of sediment thickness is much higher in landscapes with sparse vegetation. This can be attributed to an absence of vegetation cover. A decreased impact of oscillating vegetation cover on sediment thickness occurs in landscapes with denser vegetation cover and demonstrates that surface processes in these settings are not highly dependent on changes in vegetation density. This has been explained by Huxman et al. (2004), who found that vegetation cover responds to MAP variations in wet and dry systems during dry years.
The sensitivity of erosion and sedimentation to coupled oscillations in
precipitation and vegetation cover (scenario 3, Sect. 3.3) indicates that
mean catchment erosion rates (Fig. 7e) are correlated with precipitation for
densely vegetated landscapes (70 %). This is due to the dominating effect
of mean annual precipitation changes (from 26 to 72 cm yr
Thus, the temporal evolution of mean erosion rates between heavily (70 %) and sparsely (10 %) vegetated landscapes varies depending on the initial vegetation state of the catchment. As a result, correlated and anticorrelated relationships between precipitation, vegetation cover, and erosion are predicted and are the result of precipitation or vegetation exerting a dominant or subsidiary influence on catchment erosion at different times in the catchment history and for different catchment precipitation and vegetation cover conditions. This prediction is consistent with observed correlations of vegetation cover and catchment average erosion rates recently documented along the western Andean margin by Starke et al. (2020).
The lag behavior observed in sediment entrainment, thickness, and bedrock erosion is explained in additional simulations we conducted (results not shown for brevity) wherein the weathering (regolith production) function was
turned off in the model simulations (see Fig. A1). In these simulations, we did not observe any significant phase lags in maximum and minimum erosion rates, sediment thickness, or vegetation cover–precipitation. Also, the erosion rates for sparsely vegetated catchment (10 %
The periodicity of change in climate will mainly affect vegetation via the lag time it takes for the vegetation to respond; i.e., if the vegetation structure does not change (e.g., grasslands or forests), then grasslands are very flexible (Bellard et al., 2012; Kelly and Goulden, 2008; Smith et al., 2017). Grasslands can plastically respond from year to year, while forests may die off and be replaced by grasslands when it becomes drier and vice versa. This change in vegetation type might lead to the fluctuations in sedimentation and erosion rates due to periodicity of change in climate and vegetation cover.
No difference in erosion rates was identified between the two different vegetation–precipitation simulations for a given uplift rate when the erosion rate is averaged over the full period of vegetation–precipitation change. In a steady-state landscape, erosion rates are equal to the rock uplift rates according to the law of continuity of mass (e.g., Tucker et al., 2001). This means that steady-state landscapes experience higher erosion rates with higher uplift rates. However, the mean catchment erosion rates shown in Figs. 10e and 11e show temporal variations in the erosion rate driven by oscillations in the precipitation rate and vegetation. When average erosion rates are calculated over a complete cycle of the oscillation, the mean erosion rates are slightly higher than rock uplift rates owing to the nonlinearity of erosion response to changes in MAP. This result indicates that any climate- or vegetation-driven changes in erosion will not be evident when observed over too long a period of time, but they might introduce shorter-term transients (high or low) depending on the climate–vegetation cycle of change. This finding is significant for observational studies seeking to measure the predictions shown in this study. More specifically, thermochronometer dating approaches used to quantify denudation rates over million-year timescales will be hard-pressed to measure any signal of how climate or vegetation change on Milankovitch timescales influences denudation. Rather, the rate of tectonic rock uplift or exhumation (in the case of erosion rates equalling the rock uplift rate) will be measured. In contrast, observational techniques sensitive to decadal (e.g., sediment fluxes) or millennial (e.g., cosmogenic radionuclides measured from river terraces) processes can be sensitive to timescales less than the period of oscillation and are more likely to record transient catchment erosion rates influenced by variations in precipitation or vegetation cover.
The vegetation- and precipitation-driven transients in mean catchment erosion rates predicted by this study were large enough to be measured by some
observational techniques. For example, in sparsely vegetated landscapes the
half-amplitude of change in erosion rates (from steady-state values) slightly decreases as the uplift rate increases. A higher magnitude of change in transient erosion rates (from steady-state values) is found in densely vegetated landscapes and is again slightly decreased as the uplift rate increases. Previous work by Schaller and Ehlers (2006) investigated the ability of denudation rates calculated from cosmogenic radionuclides measured in a sequence of fluvial terraces to record periodic (Milankovitch timescale) variations in denudation rates. The magnitude of change in predicted transient erosion rates described above is above the detection limit reported by Schaller and Ehlers (2006), particularly when the mean catchment denudation rate is
In the previous sections, our analysis focused on the spatially averaged response of the catchment in terms of changes in sedimentation and erosion. Here, we discuss the same model results as previously presented for but show two examples (for two different vegetation covers) of the spatial variations of erosion and sediment thickness within the catchments. This provides a basis for understanding where in the catchment changes are occurring.
Two-dimensional map-view representation of changes in topographic
elevations [m]
Spatial variations in the pattern of erosion and sedimentation in the simulations with 23 kyr coupled precipitation and vegetation oscillations,
as well as a rock uplift rate of 0.05 mm a
In the simulations with dense vegetation cover (70 %) (Fig. 13), erosion
rate changes from steady-state conditions are higher during the maximum in
the precipitation and vegetation cover cycle with higher magnitudes
(
Two-dimensional map-view representation of changes in topographic
elevations [m]
Results presented in this study document a higher sensitivity of catchment
erosion and sedimentation of sparsely vegetation landscapes (10 %) to
changes in vegetation cover, whereas densely vegetated (70%) landscapes
are more responsive to changes in precipitation than vegetation changes.
This confirms the broad findings of Schmid et al. (2018) and Yetemen et al. (2019), which suggest vulnerability of erosion rates in sparsely vegetated landscapes to changes in vegetation cover and, for densely vegetated landscapes, sensitivity to the changes in MAP. However, there are differences between the results of Schmid et al. (2018) and this study, particularly for the temporal changes in erosion rates we observe for the sparse-vegetation-cover (10 %) scenario with coupled precipitation–vegetation cover oscillations. More specifically, previous results from the detachment-limited model shown in Fig. 17 of Schmid et al. (2018) show that catchment erosion rates in sparsely vegetated landscapes decrease as the precipitation and vegetation cover increases in the first part of a cycle. In the second part of the cycle when precipitation and vegetation decrease to their minimum Schmid et al. (2018) predict erosion rates of
Previous geochemistry-related observational studies from the Chilean Coastal
Cordillera (EarthShape study areas,
In addition, previous field studies (Oeser et al., 2018; Owen et al., 2011; Schaller et al., 2018) applied cosmogenic nuclides to estimate the denudation and soil production rates in the Chilean Coastal Cordillera. They suggest an increase in soil production rates from arid zones in the north to wet tropical zones in the south of the Chilean Coastal Cordillera. These findings are consistent with the predicted increase in sediment depths (e.g., 1.24 m for
The model setup used in this study was intended to quantify the sensitivity of hillslope and fluvial erosion as well as sediment transport and depositional processes for different climates with variations in precipitation rates and vegetation cover over Milankovitch timescales. This study was designed as an incremental step forward from previous modeling studies (Collins et al., 2004; Istanbulluoglu and Bras, 2005, 2006; Schmid et al., 2018).
There are several simplifying assumptions made in our modeling approach
that warrant discussion and potential investigation in future studies. For
example, this study assumed uniform vegetation cover and lithology for the
entire catchment. The assumption of uniform vegetation cover in the
catchment is likely reasonable given the relatively small (
The vegetation–erosion parameterization considered in this study follows from that of Istanbulluoglu and Bras (2006) and Schmid et al. (2018). In this parameterization the total vegetation cover of the catchment is considered only, rather than the distribution of vegetation cover by individual plant functional types (e.g., grass, shrubs, trees) that would have different Manning's coefficients associated with them. The “total vegetation cover” approach used in our (and previous) work is a reasonable starting point for understanding landscape evolution over large spatial and temporal scales because (a) more detailed observations about the changes in the distribution of plant functional types over Milankovitch timescales is not available and would be poorly constrained, and (b) empirical relationships between total vegetation cover and precipitation are available and easily implemented (e.g., Fig. 2b). However, future work should focus on exploring how the temporal and spatial distribution of different plant functional types during changing climate impacts catchment erosion given that recent work (Mishra et al., 2019; Starke et al., 2020) has identified this as important. This limitation can be handled in future studies with the full coupling of dynamic vegetation models, such as LPJ-GUESS (Smith et al., 2014; Werner et al., 2018), to a landscape evolution model for the explicit treatment of how different vegetation types change temporally and spatially within a catchment and influence catchment erosion. Also, the total vegetation cover in the model is not disturbed by flow and entrainment, which were observed to have a
large impact on the results of Collins et al. (2004) and Istanbulluoglu and Bras (2005). If the vegetation cover was spatiotemporally influenced by the above processes in our simulations, the resulting erosion and sedimentation would have been hybrid between sparse (10 %
Finally, the results of this study rely upon the vegetation–erosion parameterizations described in Sect. 2 and the Appendix (see also Fig. 3). While there is an observational basis for these relationships (see Sect. A1 and A2), there are, frankly, a sparse number of field studies available robustly constraining how different vegetation types and amounts influence hillslope and surface water erosional processes. Thus, we consider the erosional parameterizations used here to be hypotheses (rather than robust geomorphic transport laws) that warrant investigation in future field or flume studies.
In this study, we investigate the effects of variable vegetation cover and
climate over Milankovitch timescales on catchment-scale erosion and sedimentation. Simulations were presented to document if these transients
are muted (lower amplitude) at higher rock uplift rates. The approach used
here complements previous studies by using a coupled fluvial detachment–transport-limited and hillslope diffusion landscape evolution
model, and it also investigates the degree to which transient effects of vegetation cover and precipitation are measurable in observational studies.
The main conclusions deduced from this study are the following.
The stepwise increase in complexity of the model simulations was essential for identifying temporal changes in catchment erosion and sediment thickness. A nonlinear response in erosion and sediment thickness to varying precipitation and vegetation cover was observed, and results were dependent on the initial vegetation and precipitation state of the catchment. The sources of nonlinearity stem from (a) a nonlinear relationship between precipitation changes required to cause a Analysis of results for covarying precipitation and vegetation cover indicates that erosion and sedimentation in densely vegetated landscapes ( Analysis of results for covarying precipitation and vegetation cover indicates that erosion and sedimentation in sparsely vegetated landscapes ( Concerning the first hypotheses stated in the Introduction, we found that the effect of Milankovitch periodicity variations on the amplitude of change in sediment thickness and bedrock erosion is more pronounced for longer climate and vegetation oscillations (100 kyr) in both climate and vegetation settings. This finding confirms the hypothesis. Furthermore, periodicity effects on erosion and sediment thickness are larger in densely (70 %) vegetated landscapes than sparsely (10 %) vegetated landscapes, thereby indicating a sensitivity of the response to the biogeographic zone the changes are imposed on. With respect to our second hypothesis, all transient forcings in precipitation and vegetation cover explored in this study resulted in variations in erosion and sediment thickness around the mean erosion rate, which is determined by the rock uplift rate. As rock uplift rates increased from 0.05 to 0.2 mm a Finally, in comparison to previous studies, the 35 % to 110 % transient changes in erosion rate documented here are at, or above, the detection limit for measurement cosmogenic radionuclides in river sediments preserved in fluvial terraces, but they would be undetectable with bedrock thermochronometer dating techniques that average erosion rates over longer timescales. The potential to measure vegetation-related transient changes in erosion rates with cosmogenic nuclides is highest in settings with higher rock uplift rates (e.g., 0.1 and 0.2 mm a
The approach in our study follows the law of continuity of mass (e.g., Tucker et al., 2001). It states that the rate of change in topographic elevation (
The rate of change in topography due to hillslope diffusion (Fernandes and Dietrich, 1997; Martin, 2000) is defined as follows:
The diffusion coefficient is defined as a function of vegetation cover present on hillslopes, which is estimated following the approach of
Istanbulluoglu and Bras (2005), Dunne et al. (2010), and Schmid et al. (2018) as follows:
The fluvial erosion is estimated for a two-layer topography (i.e., bedrock
and sediment are treated explicitly) in the coupled detachment–transport-limited model, SPACE 1.0 (Shobe et al., 2017). Bedrock erosion and sediment entrainment are calculated simultaneously in the model. Total fluvial erosion is defined as
The rate of change in the height of bedrock
The change in sediment thickness
Following the approach of Shobe et al. (2017),
By combining the stream power equation (Tucker et al., 1999; Howard, 1994; Whipple and Tucker, 1999) and the above concept of the effect of vegetation on shear stress, we follow the approach of Schmid et al. (2018) to define new sediment and bedrock erodibility parameters influenced by the surface vegetation cover on fluvial erosion, as follows:
Temporal evolution of catchment-averaged predictions for scenario 3 (with no weathering) described in the text (Sect. 3.3). Graphical representation of normalized mean catchment sedimentation and erosion to
Landscape evolution model input parameters used and corresponding units.
The code and data used in this study are freely available upon request.
HS and TAE designed the initial model setup and simulation programs. HS and TAE conducted model modifications, simulation runs, and analysis. MS provided assistance in program modification. KT provided insight into plant ecology in Chilean study areas and vegetation–climate change relationships. HS and TAE prepared the paper with contributions from CG, KT, and MS.
The authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hemanti Sharma and Todd A. Ehlers acknowledge support from the Open Access Publishing Fund of the University of Tübingen. We also acknowledge support from the Research Training Group 1829 Integrated Hydrosystem Modelling, funded by the German Research Foundation (DFG). In addition, Todd A. Ehlers, Katja Tielbörger, and Manuel Schmid acknowledge support from the German priority research program (SPP-1803; grants EH329/14-1 and EH329/14-2 to Todd A. Ehlers; TI 338/14-1 and TI338/14-2 to Katja Tielbörger). We thank Erkan Istanbulluoglu and Omer Yetemen for their constructive reviews.
This research has been supported by the Deutsche Forschungsgemeinschaft (Research Training Group 1829 Integrated Hydrosystem Modelling, grant nos. SPP-1803, EH329/14-1, EH329/14-2, TI 338/14-1, and TI338/14-2). This open-access publication was funded by the University of Tübingen.
This paper was edited by Greg Hancock and reviewed by Erkan Istanbulluoglu and Omer Yetemen.