资源环境科技发展态势分析平台

Dryland vegetation is influenced by biotic and abiotic land surface template (LST) conditions and precipitation (PPT), such that enhanced vegetation responses to periods of high PPT may be shaped by multiple factors. High PPT stochasticity in the Chihuahuan Desert suggests that enhanced responses across broad geographic areas are improbable. Yet, multiyear wet periods may homogenize PPT patterns, interact with favorable LST conditions, and in this way produce enhanced responses. In contrast, periods containing multiple extreme high PPT pulse events could overwhelm LST influences, suggesting a divergence in how climate change could influence vegetation by altering PPT periods. Using a suite of stacked remote sensing and LST datasets from the 1980s to the present, we evaluated PPT-LST-Vegetation relationships across this region and tested the hypothesis that enhanced vegetation responses would be initiated by high PPT, but that LST favorability would underlie response magnitude, producing geographic differences between wet periods. We focused on two multiyear wet periods; one of above average, regionally distributed PPT (1990-1993) and a second with locally distributed PPT that contained two extreme wet pulses (2006-2008). 1990-1993 had regional vegetation responses that were correlated with soil properties. 2006-2008 had higher vegetation responses over a smaller area that were correlated primarily with PPT and secondarily to soil properties. Within the overlapping PPT area of both periods, enhanced vegetation responses occurred in similar locations. Thus, LST favorability underlied the geographic pattern of vegetation responses, whereas PPT initiated the response and controlled response area and maximum response magnitude. Multiyear periods provide foresight on the differing impacts that directional changes in mean climate and changes in extreme PPT pulses could have in drylands. Our study shows that future vegetation responses during wet periods will be tied to LST favorability, yet will be shaped by the pattern and magnitude of multiyear PPT events.