Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

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. Upscaling in situ carbon flux measurements using remotely sensed and meteorological observations in a machine learning algorithm leads to improved estimates of average uptake, and interannual variability in global drylands.

Automated summary

DryFlux is a product that combines in situ carbon flux measurements with remote sensing and meteorological observations using machine learning, accurately predicting the variability of carbon uptake in dryland ecosystems. This approach can serve as an improved benchmark for earth system models in drylands, prompting a more sensitive consideration of the impact of water limitation on the carbon cycle.