A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States

The complex feedback relationship between climate variability and vegetation dynamics is a subject of intense investigation for its implications in furthering our understanding of the global biogeochemical cycle. We address an important question in this context: “How does topography influence the vegetation's response to natural climate fluctuations?” We explore this issue through the analysis of inter-annual vegetation variability over a very large area (continental United States) using long-term (13-year period of 1989–2001), monthly averaged, biweekly maximum value composite normalized difference vegetation index (NDVI) data. These data are obtained from satellite remote sensing at 1-km resolution. Through the novel implementation of data mining techniques, we show that the Northern Pacific climate oscillation and the ENSO phenomena influence the year-to-year vegetation variability over an extensive geographical domain. Further, the vegetation response to these fluctuations depends on a variety of topographic attributes such as elevation, slope, aspect, and proximity to moisture convergence zones, although the first two are the predominant controls. Therefore, the dynamic response of terrestrial vegetation to climate fluctuations, which shows tremendous spatial heterogeneity, is closely linked to the variability induced by the topography. These findings suggest that the representation of vegetation dynamics in existing climate models, which do not incorporate such dependencies, may be inadequate. Therefore, climate models that are regularly employed to guide policy decisions need to better incorporate these dependencies for the assessment of terrestrial carbon sequestration under evolving climate scenarios.