Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 108, Issue 12, Pages 4902-4907Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1013100108
Keywords
quantile regression; intraannual variability; remote sensing; potential cover; ecohydrology
Categories
Funding
- National Science Foundation [DEB-742933, EAR-0847368]
- Division Of Earth Sciences
- Directorate For Geosciences [847368] Funding Source: National Science Foundation
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Determining the factors that influence the distribution of woody vegetation cover and resolving the sensitivity of woody vegetation cover to shifts in environmental forcing are critical steps necessary to predict continental-scale responses of dryland ecosystems to climate change. We use a 6-year satellite data record of fractional woody vegetation cover and an 11-year daily precipitation record to investigate the climatological controls on woody vegetation cover across the African continent. We find that-as opposed to a relationship with only mean annual rainfall-the upper limit of fractional woody vegetation cover is strongly influenced by both the quantity and intensity of rainfall events. Using a set of statistics derived from the seasonal distribution of rainfall, we show that areas with similar seasonal rainfall totals have higher fractional woody cover if the local rainfall climatology consists of frequent, less intense precipitation events. Based on these observations, we develop a generalized response surface between rainfall climatology and maximum woody vegetation cover across the African continent. The normalized local gradient of this response surface is used as an estimator of ecosystem vegetation sensitivity to climatological variation. A comparison between predicted climate sensitivity patterns and observed shifts in both rainfall and vegetation during 2009 reveals both the importance of rainfall climatology in governing how ecosystems respond to inter-annual fluctuations in climate and the utility of our framework as a means to forecast continental-scale patterns of vegetation shifts in response to future climate change.
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