Empirical upscaling of OzFlux eddy covariance for accurate, high-resolution monitoring of terrestrial carbon and water fluxes in Australia.

We discuss the development of high-resolution (1 km) estimates of terrestrial carbon and water fluxes over the Australian continent by empirical upscaling of a regional network of flux tower measurements (“AusEFlux” v1.1 https://zenodo.org/records/7947265). We detail our ensemble learning approach for estimating the per-pixel epistemic uncertainty in flux predictions.  Our investigations demonstrate that regional or continental upscaling has several advantages over global upscaling, including: the ability to use regionally derived covariable datasets tailored to the regional environmental context; reduced computational constraints allowing for higher-resolution predictions, thus reducing the impacts of sub-cell landscape heterogeneity; ameliorating spatial biases present in global datasets that often have a strong northern hemisphere bias; simpler interpretation of results due the reduced requirement to generalise across vastly different climates, ecosystem types, and plant functional traits; and increased relevance to local stakeholders.  We compare AusEFlux with estimates from nine other products that cover the three broad categories that define current methods for estimating the terrestrial carbon cycle. We argue that consiliences between datasets derived using different methodologies offer alternative value for assessing the quality of an upscaling product than any given cross-validation technique, especially where training datasets have spatial or temporal biases that are difficult to mitigate. Lastly, we discuss the benefits of regularly updating our upscaling product to arrive at a systematic monitoring of terrestrial carbon and water fluxes.