This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The Earth?s ?critical zone? is where water, atmosphere, ecosystems and soils interact on a geomorphic and geologic template, and extends from bedrock to the atmospheric boundary layer. Process understanding of erosion, weathering, soil formation, water movement and nutrient transport in the critical zone depends in large part on new observations, coupled with models that can take advantage of those new data. In particular, accurate, high‐resolution images of the land surface and vegetation canopy, and the structure in between, have the ability to transform our ability to describe and model those processes, and to predict how changes in climate and landcover will perturb water cycles and critical‐zone processes linked to water. Airborne LiDAR (Light Detection and Ranging) is proving to be a transformational technology that can determine the three‐dimensional structure of the critical zone, and thus enable this process research. LiDAR flights will measure canopy during leaf on/leaf off conditions, snow distribution and other physical features of the land surface at the three NSF‐supported Critical Zone Observatories (CZOs) and other three key sites. Physiographic data will be used to derive the LiDAR products, such as a high‐resolution digital elevation model, tree heights, tree diameter at breast height, leaf area index, crown cover, and snow depth. Ground‐truth data will be collected to validate and calibrate the LiDAR derived products. Advanced, state‐of‐the‐art processing will be carried out to assure that products are accurate.
Intellectual merit. The resulting information will be used for hypothesis‐driven research across these sites. High‐resolution topographic data will characterize landscapes and enable testing hypotheses about the geomorphic processes that have generated these landscapes. The data will enable examining the role of aspect in geomorphic and hydrologic behavior, and the feedbacks between slope, soil moisture, weathering, soil formation and vegetation. Hydrologic simulations using emerging, physics‐based models will also be carried out, using the high‐spatial‐resolution topographic and canopy products from LiDAR. High‐resolution estimates of spatial patterns of snow depth will provide an unprecedented ground‐truth data set for modeling the physiographic controls on snow accumulation and melt. LiDAR scenes will contribute to estimating vegetation structure, which will then be used for parameter estimation in coupled hydro‐ecologic model analysis and in scaling of evapotranspiration and carbon flux. LiDAR‐based estimates of micro‐topography, the patterns of which give rise to ?hot? and ?cold? spots of soil biogeochemical cycling generated by preferential flow of nutrient‐rich litter leachate into mineral soils, will help guide sampling of litter and soil nutrient concentrations along gradients of water and biological availability. By quantifying functional controls on rates of erosion and weathering, the LIDAR data will contribute to improved understanding of how (and why) sediment and solutes move across (and through) the landscape.
Broader impacts: There are two main, direct broader impacts of this project. First, making LiDAR data available for the three CZOs will enhance their potential and use as community platforms for research. The goal of CZOs is to build a network to advance interdisciplinary studies of Earth surface processes as well as foster collaboration among scientists and engineers from different disciplines. However, existing spatial data cannot meet the research needs of the CZO teams because of being incomplete, outdated and of insufficient spatial resolution and temporal scale. A second broader impact will be to make LiDAR products easily understood and widely used by researchers working at CZOs and similar study areas, building on the community nature of CZO data and resources, and well‐developed plans to share those resources and disseminate CZO products.
