Linking Soils and Geomorphic Process Zones Across Hillslopes Using Lidar.

Digital elevation models (DEMs) are a primary quantitative predictor of soil patterns due to their increasing availability and the tight coupling between topography and soil variability. As fine-scale DEMs gain popularity a consensus is emerging to upscale fine-grain terrain attributes to ~10-30m to maximize predictive power. This calls into question the value of fine-scale datasets for predicting soil properties, and revisits assumptions implicit in the soil mapping paradigm. For example, soil-landscape mapping seeks to delineate fixed boundaries for practical reasons, while critical zone geomorphology focuses on drivers and resistors that are actively forming and consuming soil patterns. Given their idealized form and spatial chain of process-response patterns, soil mantled hillslopes are well suited for exploring soil-landscape predictions that blend scale and process. This talk will demonstrate such an approach for segmenting hillslopes that attempts to bridge basic soil and geomorphic concept models. The method employs simple diagnostics that distill fine-scale lidar data to reveal hillslope geomorphic thresholds specific to a given location, and scaled accordingly. The resulting hillslope components may correspond to catenary soil classes, with breaks conditioned by quantitative geomorphic process-response signatures. Comparative examples are provided from distinct lithologies to further highlight differences in the spatial distribution of soil hillslope components arising from differing controls on hillslope soil formation and redistribution processes.