Effects of seasonal variations in vegetation and precipitation on catchment erosion rates along a climate and ecological gradient: Insights from numerical modelling
Abstract. Precipitation in wet seasons influences catchment erosion and contributes to annual erosion rates. However, wet seasons are also associated with increased vegetation cover, which helps resist erosion. This study investigates the effect of present-day seasonal variations in rainfall and vegetation cover on erosion rates for four catchments along the extreme climate and ecological gradient (from arid to temperate) of the Chilean Coastal Cordillera (~26° S – ~38° S). We do this using the Landlab-SPACE landscape evolution model modified to account for vegetation-dependent hillslope-fluvial processes and hillslope hydrology. Model inputs include present-day (90 m) topography, and a timeseries (from 2000–2019) of MODIS-derived NDVI for vegetation seasonality; weather station observations of precipitation; and evapotranspiration obtained from GLDAS NOAH. Simulations were conducted with a step-wise increase in complexity to quantify the sensitivity of catchment scale erosion rates to seasonal variations in precipitation and/or vegetation cover. Simulations were conducted for 1,000 years (20 years of vegetation and precipitation observations repeated 50 times). After detrending the results for long-term transient changes, the last 20 years were analyzed. Results indicate that when vegetation cover is varied but precipitation is held constant, the amplitude of change in erosion rates relative to mean erosion rates ranges between 6.5 % (humid-temperate) to 36 % (Mediterranean setting). In contrast, in simulations with variable precipitation change and constant vegetation cover, the amplitude of change in erosion rates is higher and ranges between 13 % (arid) to 91 % (Mediterranean setting). Finally, simulations with coupled precipitation and vegetation cover variations demonstrate variations in catchment erosion of 13 % (arid) to 97 % (Mediterranean setting). Taken together, we find that precipitation variations more strongly influence seasonal variations in erosion rates. However, the effects of seasonal variations in vegetation cover on erosion are also significant (between 5–36 %) and are most pronounced in semi-arid to Mediterranean settings and least prevalent in arid and humid-temperature settings.
Hemanti Sharma and Todd A. Ehlers
Status: final response (author comments only)
RC1: 'Comment on esurf-2022-65', Anonymous Referee #1, 06 Feb 2023
- CC1: 'Reply on RC1', Todd A. Ehlers, 07 Feb 2023
- RC2: 'Comment on esurf-2022-65', Anonymous Referee #2, 01 Mar 2023
- RC3: 'Comment on esurf-2022-65 by Sharma and Ehlers', Omer Yetemen, 22 Mar 2023
Hemanti Sharma and Todd A. Ehlers
Hemanti Sharma and Todd A. Ehlers
Viewed (geographical distribution)
This paper uses landscape evolution modeling to investigate the relative influence of seasonal precipitation and vegetation variations on erosion rates across four unique climates. The authors drive their LEM with precipitation data and NDVI-derived vegetation cover to determine, across climates ranging from arid to humid temperate, that precipitation outcompetes vegetation cover as a control on erosion, and that these effects vary significantly across the type of climate. Vegetation effects seem to matter most in middle-moisture climates and least in very arid or very humid climates.
The paper deals with an important issue (influence of vegetation on erosion and how we might separate it from precipitation effects). The study design is simple—this is a good thing—and the well-understood suite of study sites provides a nice starting point for the analysis. I do think though that there is one fairly significant (but fixable!) structural weakness in the analysis, and I see a variety of smaller (also fixable) issues related to the treatment and presentation of data and its derived statistics. I could see this paper ultimately being publishable in ESurf, but it will require substantial revision. Below I detail my main concerns and then move on to smaller line comments.
Abstract: the last sentence has a low end of 5%, but just above it says 6.5%. Are these different quantities being reported?
34: changes play a crucial role. Also the grammar here is odd because seasonality is a noun. Maybe just seasonal?
39: Or in this case, “plantscape evolution modeling” !!! (just a joke)
45: If this paper is worth mentioning, you should state its main conclusion rather than just its topic
47: pluralization mismatch—proofread for clarity
53: do you mean a reduction in sensitivity of soil loss potential to storm frequency?
54: again here a very vague statement about what past workers did. If it’s worth mentioning, surely it has some relevant conclusion. Also consider restructuring the sentence because differences in vegetation don’t drive erosion and sedimentation.
56: unclosed (
65: could you state the direction of this effect? I can assume, but haven’t read the paper
66: is this species richness or some other metric?
71-72: “…seasonality in precipitation and vegetation cover conspire to influence…”
74: I suppose it could work either way, but I would have reached for “transience” rather than “transients”
75: “across the extreme…” or “spanning the extreme…”
77: hypothesis 1 sounds strangely tautological. Why not just say “1) P is the first-order driver of seasonal erosion rates.” That implies that everything else is of low significance.
121-122: This is fine, but it might be worth adding one sentence to emphasize the limitations of NDVI, chiefly that it saturates out once the ground is basically shrub-covered and so it couldn’t tell you much about different plant communities for associated erosion-relevant properties like rooting depth, etc.
133: Landlab I think(?) is typically written with only “Land” capitalized
136: typo /
137: is this total relief in the whole catchment? Just want to be clear.
140-143: True, but this is not the only reason you could have initial transience. LEMs (and the systems they represent) have inherent timescales, and you can’t know a priori whether the state of the system as captured by the SRTM DEM and your various other inputs is at a full equilibrium condition. Surely it is less likely to be in equilibrium with respect to all relevant forcings than to be in some stage of transient response.
151: “summing up” can just be “summing”
157: yes and this is fine, but again there are lots of erosion-relevant properties of vegetation that NDVI is NOT good at measuring. It would be good to be up front about this in the writing.
160: could you provide a few words on the resampling method?
Figure 2: I wonder about the utility of plotting these things against each other given that we surely expect lags between the water balance and the vegetation response? 182-193 accurately describes the figures, but does not lead us to any real insights about the system. What do you want readers to take away from this figure? Some other points about this figure: Please check that these color schemes are friendly to all (i.e. are color-impaired friendly). I doubt they are, given the use of red and green. An easy remedy would be to use varying marker styles (squares, triangles, stars) in addition to colors. Also panel c would be better if the markers had some transparency so the blue didn’t hide the red and the red didn’t hide the black. Separate issue: it is not clear to me why we would necessarily expect these relationships to be linear and therefore why one should use a linear regression here to assess correlation. Finally, why don’t you label the different data series by their climate rather than by the study area names? Any given reader will only care about the former.
215: This description is, to the extent that I understand these things, not quite right: Landlab is a modeling toolkit, not a model in and of itself (I think of it as a toolbox holding many models, each of which is a possible tool to use). SPACE is one of many models that operate within the landlab toolkit or framework.
222: Was there actually model calibration in the true sense? My read is that you chose values that are appropriate for the study basins, which is 100% fine, but is not the same thing as running an actual calibration exercise.
222-223: Revise this sentence for grammar
223: does “erosion” mean erodibility? Save for diffusion/diffusivity. Also lithology is not a model parameter; there are parameters that incorporate the effects of lithology. But these have specific and more descriptive names that we should use.
229-230: re-read this sentence for grammatical consistency
242-243: delete “initial.” It’s just the uplift rate for the whole simulation, which is fine.
277: I guess it’s ok if you want to keep it, but I think the units of veg cover are pretty clear so I don’t know how important it is to write [-] after every use. It’s common colloquially to just speak of proportions with no units.
Figure 4: Modify for color vision impairment viewing; this will be very simple.
Figure 5: same as figure 4. Also the arid data should really be shown on a zoomed-in inset or sub-panel. Just because the arid landscape doesn’t show the same absolute response does not mean that it can’t have equal or greater proportional sensitivity than another climate. We can’t evaluate much about this because the black dots are all jammed together in the corner.
Figures 6 and 7: same comments about figure readability and construction as 4 and 5.
Figures 8 and 9: same concerns as above
Figure 10: Why is this rendered as a line graph? These data are not connected in any sort of sequential relationship (except to the extent that precip varies, but if you wanted to plot that you’d put precip on the x-axis). A bar graph would be better, or a stacked bar graph such that all three plots could be condensed into one. Again here I recommend not using site names but their climate labels.
392-394: Not the best example of agreement with your findings. Rainfall intensity in a plot experiment is not the same as total seasonal precip, so this is a bit of a stretch to use as a direct parallel. The others cited here make sense though.
Figure 11: You’re probably sick of hearing it, but please just use different markers or line styles or something to distinguish these series.
442-443: This sentence is vague. How do the dynamics change as a result of changes in veg?
471: “subject to”
486: All of these limitations are fine, but the biggest limitation of the study is that there is no mechanism to consider transient dynamics, for example that sediment eroded off a hillslope in March might take until December to move out of the model domain.
536: see main point above.