Remote sensing to inform Plant Functional Type (PFT) distributions in the Community Land Model

Abstract:

Sensitive Arctic ecosystems are vulnerable to change as warming climate impacts the hydrological, thermal, biogeochemical and plant physiological processes on the landscape, leading to geomorphic, biophysical and biogeochemical changes. In particular, Arctic vegetation is expected to exhibit significant shifts in community composition, phenology, distribution and productivity under a changing climate. Modeling of vegetation communities, often represented as Plant Functional Types (PFTs) in Earth System Models (ESMs), requires accurate characterization of their distributions on the landscape as input to ESMs. The unique spectral characteristics exhibited by vegetation can be sensed by remote sensing platforms and used to characterize and distinguish different vegetation types. In this study we employ multi-spectral remote sensing from WorldView--2 and LIDAR--derived digital elevation models to characterize the Arctic tundra vegetation communities near Barrow, Alaska. Using field vegetation surveys at a number of sites, we derived statistical relationships between vegetation distributions and spectral data, which were then employed to estimate the distributions of evergreen shrub, deciduous shrub, grass, sedge, forb, moss and lichen PFTs for the Barrow Environmental Observatory. Plant physiological parameters for these tundra-specific PFTs were implemented in the Community Land Model (CLM). We will present CLM results from simulations employing different distributions of these new PFTs, created using different subsets of remote sensing and in situ vegetation data, to test the sensitivity of the model to a range of predicted distributions.