Gully-erosion estimation and terrain reconstruction using analyses of microtopographic roughness and LiDAR

Abstract Gully mapping techniques successfully identify gullies over a large range of breadths and depths in complex landscapes but practices for estimating gully volumes need further development. gap-interpolation estimation volume does not often factor landscape microtopography the generation new surface. These approaches can thus overestimate classical volumes, averaging depressions, or underestimate by creating overly-smooth highly curved surfaces. Microtopographic methodology was developed to estimate pre-gully surface across Calhoun Critical Zone Observatory (CCZO) South Carolina, USA. The CCZO is Southern Piedmont severely gullied historic agriculture with upland Ultisols many meters deep. Our gully-mapping gully-filling used 1 m2 LiDAR elevation data based on premise that are local depressions uplands which deeply incised high microtopographic roughness. smoothing-via-filling-rough-depressions (SvFRD) algorithm iteratively fills until roughness reduced unchanging after subsequent iteration. Results were evaluated context prior bulk erosion estimates ranging from 1483 3708 m3/ha as well field surveys gullies. Minimally eroded reference highly-eroded post-agricultural terrain compared test accuracy. Comparing gully-volume techniques, inverse-distance-weighting (IDW) yielded highest (1072 m3/ha) followed ANUDEM (638 m3/ha) while spline-interpolation lowest (555 m3/ha). SvFRD (615.5 m3/ha) most similar interpolation extent results spline interpolation. Spline effective easily implemented if accuracy fine-scale erosions features desired hindcast conditions, our depression-filling approach, using free GIS statistical software, an method reasonable volumes.