Northeastern Section - 57th Annual Meeting - 2022

Paper No. 15-1
Presentation Time: 1:35 PM

LARGE-SCALE MAPPING OF POST-WILDFIRE DRY SEDIMENT LOADING USING REPEAT AIRBORNE LIDAR IN SOUTHERN CALIFORNIA


DIBIASE, Roman1, FONG, Brandon T.2 and WALTER, Nicholas2, (1)Department of Geosciences, Pennsylvania State University, University Park, PA 16802; Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA 16802, (2)Department of Geosciences, Pennsylvania State University, University Park, PA 16802

Post-wildfire debris flows are a major hazard to life and property along the wildland-urban interface, arising in response to the 10- to 100-fold increase in sediment delivery from hillslopes to channels that occurs following fire. In the steep landscapes of southern California, delivery of fine-grained sediment by dry ravel immediately following wildfire primes headwater channels for debris flows. This sediment loading in channels (101-102 cm) can be resolved by repeat airborne lidar over large areas (100-102 km2), where pre-fire lidar data exist. Here we present analysis of repeat airborne lidar and high-resolution air photos from the San Gabriel and San Bernardino Mountains bracketing the 2020 fire season to map the spatial pattern of dry sediment loading across varied topography, lithology, and burn severity. Depending on the quality of pre-fire lidar data, sediment loading can be resolved to vertical changes of ~50 cm. Additionally, high point densities in post-fire lidar datasets allow for identification of angle-of-repose sediment cones in channels, and we supplemented automated change detection maps with manual mapping of dry ravel deposits. Overall, we found that the spatial patterns of dry sediment loading do not systematically vary with slope or burn severity, provided hillslopes are steeper than the angle of repose for sediment. Instead, the magnitude of loading varies regionally in a manner that suggests a poorly understood lithologic control on sediment production and/or delivery to channels.