Extreme Resolution Modeling of Integrated Critical Zone Processes

Abstract:

The advent of high-resolution measurements of topographic and (vertical) vegetation features using areal LiDAR are enabling us to resolve micro-scale (~1m) landscape structural characteristics over large areas. Availability of hyperspectral measurements is further augmenting these LiDAR data by enabling the biogeochemical characterization of vegetation and soils at unprecedented spatial resolutions (~1-10m). Such data have opened up novel opportunities for modeling critical zone processes and addressing questions that were not possible before. We show how an integrated 3-D model at ~1m resolution can enable us to resolve micro-topographic and ecological dynamics and their control on hydrologic and biogeochemical processes. We capture vertical vegetation structure, i.e. vertical leaf area profile, using LiDAR point cloud data conditioned on vegetation species identified through hyperspectral data. This allows the resolutions of the vertical light regime and the resulting photosynthesis and latent and sensible heat energy profile. We address the computational challenge of such detailed modeling by exploiting GPU computing technologies. We show results of moisture, biogeochemical, and vegetation dynamics from studies in the Critical Zone Observatory for Intensively managed Landscapes (IMLCZO).