Abstract
Abstract. We quantified the temporal trend and climatic sensitivity of vegetation phenology in dryland ecosystems in the US Great Basin during 1982–2011. Our results indicated that vegetation greenness in the Great Basin increased significantly during the study period, and this positive trend occurred in autumn but not in spring and summer. Spatially, increases in vegetation greenness were more apparent in the northwestern, southeastern, and eastern Great Basin but less apparent in the central and southwestern Great Basin. In addition, the start of growing season (SOS) was not advanced while the end of growing season (EOS) was delayed significantly at a rate of 3.0 days per decade during the study period. The significant delay in EOS and lack of earlier leaf onset caused growing season length (GSL) to increase at a rate of 3.0 days per decade. Interestingly, we found that the interannual variation of mean vegetation greenness calculated for the period of March to November (spring, summer, and autumn – SSA) was not significantly correlated with mean surface air temperature in SSA but was strongly correlated with total precipitation. On a seasonal basis, the variation of mean vegetation greenness in spring, summer, and autumn was mainly attributable to changes in pre-season precipitation in winter and spring. Nevertheless, climate warming appeared to play a strong role in extending GSL that, in turn, resulted in the upward trend in mean vegetation greenness. Overall, our results suggest that changes in wintertime and springtime precipitation played a stronger role than temperature in affecting the interannual variability of vegetation greenness, while climate warming was mainly responsible for the upward trend in vegetation greenness we observed in Great Basin dryland ecosystems during the 30-year period from 1982 to 2011.
Highlights
Shifts in plant phenology resulting from climate change can affect the cycling of carbon, water, and energy between the biosphere and atmosphere (Wu and Liu, 2013), availability of biological and physical resources (White and Nemani, 2011), and best practices for managing these resources for production of fiber and food to sustain human life (Butt et al, 2011)
When averaged for the period of March to November (i.e., SSA), both mean NDVI and mean surface air temperature in SSA in the dryland ecosystems increased significantly during the period 1982–2011 (Fig. 2a, b) while total precipitation in SSA showed no significant trend during the study period (Fig. 2c)
The interannual variability of mean SSA NDVI was not significantly correlated with the variation in mean surface air temperature, the warming trend we observed in autumn (Fig. 3f) was likely the major driver responsible for the significant positive trend we measured in growing season length (GSL) (Fig. 7f), which in turn resulted in the 30-year positive trend in mean NDVI values in SSA (Fig. 9b) that we observed in the dryland ecosystems of the US Great Basin
Summary
Shifts in plant phenology (e.g., greenness, spring leaf onset) resulting from climate change can affect the cycling of carbon, water, and energy between the biosphere and atmosphere (Wu and Liu, 2013), availability of biological and physical resources (White and Nemani, 2011), and best practices for managing these resources for production of fiber and food to sustain human life (Butt et al, 2011). Terrestrial carbon sequestration has been considered to be relatively low in dryland ecosystems, these ecosystems cover almost 40 % of Earth’s land area (UNDP/UNSO, 1997) and account for nearly 20 % of the global soil carbon pool (Field et al, 1998; Lal, 2004) They may be buffering anthropogenic CO2 rise more than expected (Jasoni et al, 2005; Wohlfahrt et al, 2008; Poulter et al, 2014; Ahlström et al, 2015) and are sensitive to both climatic variation (Jasoni et al, 2005; Wohlfahrt et al, 2008) and increasing atmospheric CO2 concentrations (Jasoni et al, 2005). Quantification of the responses of dryland plant phenology to climate variability at the regional scale is needed to improve forecasting of shifts in ecosystem functioning and consequences for ecosystem services (including livestock grazing, wildlife habitat, and modulation of atmospheric CO2) that drylands provide
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