Abstract
Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.
Highlights
Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink
Accurate representation of dryland carbon dynamics in global-scale process- and remote sensing-based models could improve the accuracy of terrestrial carbon sink estimates and advance our understanding of the global carbon cycle
Drylands are highly sensitive to variations in water availability[17], and persistent water limitation in these systems has resulted in physiological adaptations that lead to tight coupling of biogeochemical and water cycles[6,18,19,20]
Summary
Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Our approach consisted of two main components: first we derived relationships between climate and vegetation predictors with GPP from flux towers using the random forest machine learning algorithm, and second, we applied the trained model to remotely sensed data inputs to generate spatially and temporally continuous carbon uptake estimates from 2000 to 2016 at 0.5° spatial resolution.
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