Ecosystem metabolic variability associated with meteorology, physiology, phenology, and water supply

Background/Question/Methods Understanding ecosystem-atmosphere carbon exchanges in dry environments has been more challenging than in mesic environments. This difference is likely due to more pronounced non-linear responses of ecosystem processes to environmental variation. To better address diurnal to interannual ecosystem carbon exchange variation, we examined a simplified sub-hourly ecosystem physiologic theory based on fundamental principles of diffusion, mass balance, reaction kinetics, and biochemical regulation of photosynthesis. We evaluated the suitability of this theory to describe patterns observed from eddy-covariance derived net ecosystem exchange (NEE) of CO₂ data through a Monte-Carlo Markov chain model-data fusion procedure. The model-data fusion was independently implemented during the early and late growing seasons for three years at both a riparian terrace grassland and woodland in southern Arizona.

Results/Conclusions The data-model fusion procedure skillfully reproduced the majority of diurnal variation in NEE for all sampling periods. Both sites exhibited a maximum in diurnal assimilation occurring on average between 0.5 to 4 hours before solar noon; the timing of peak assimilation was closely related to maximum daily vapor pressure deficit. The woodland site had consistently higher assimilation rates, lower seasonal variability, and a larger diurnal assimilation hysteresis compared to the grassland site. We examined the causes of this variation using a new state factor model analysis that partitioned ecosystem physiologic variation into four factors: meteorology, water supply, physiology, and leaf area. The largest proportion of variation in assimilation and net exchanges was primarily associated with physiological differences. When comparing the two community types, the woodland showed a greater sensitivity than the grassland to water supply, while the grassland showed a greater sensitivity to leaf area than the woodland. The coupling of ecophysiological theory with sub-hourly eddy-covariance data in the context of a state factor model provided an important approach for understanding ecological responses to environmental variability.