Applicability of the landscape evolution model in the absence of rills
Despite numerous applications of physically-based models for incised landscapes, their applicability for overland flow on unchanneled surfaces is not known. This work challenges a widely used landscape evolution model for the case of non-uniform rainfall and absence of rills using laboratory flume experiment. Rainfall with an average intensity of 85 mm/h was applied for 16 h during which high resolution laser scans of the morphology were captured. The overland flow was modeled as a network that preserves the water flux for each cell in the discretized domain. This network represented the gravity-driven surface flow and determined the evolution direction. The model was calibrated using the first 8 h of the experiment and was then used to predict the second 8 h. The calibrated model predicted, as expected, a smoother surface morphology (and less detailed overland flow network) than that measured. This difference resulted from quenched randomness (e.g., small pebbles) within the experimental soil that emerged during erosion and that were captured by the laser scans. To investigate the quality of the prediction, a low-pass filter was applied to remove the small-scale variability of the surface morphology. This step confirmed that the model simulations captured the main characteristics of the measured morphology. The experimental results were found to satisfy a scaling relation for the exceedance probability of discharge even in absence of rills. However, the model did not reproduce the experimental scaling relation as the detailed surface micro-roughness was not accounted for by the model. A lower cutoff on the scale of applicability of the general landscape evolution equation is thus suggested, complementing other work on the upper cutoff underpinned by runoff-producing areas.
feart-10-872711.pdf
publisher
openaccess
CC BY
3.48 MB
Adobe PDF
a769ea3aa587300698a4ecb8664a1c68
data sheet 1.zip
publisher
openaccess
CC BY
12.67 MB
ZIP
2c64b833f21afe2b28b691768a143209