Empowering local Climate Change mitigation policies through Eddy Covariance flux measurements

According to the IPCC and the Paris Agreement, the imperative to limit global warming to 1.5°C, or well below 2°C, compared to the pre-industrial era, necessitates achieving a balance between anthropogenic emissions by sources and removals by sinks (net-zero anthropogenic CO₂ emissions) by the second half of this century. In this context, a prerequisite for credible climate action is an accurate estimation of both these large fluxes. Recent initiatives have focused on improving the quantification of the land sector mitigation potential and reducing the discrepancies between models and observations at a global level. However, the implementation of climate mitigation policies often takes place at the local level, where entities like local authorities (e.g., cities and regions) often wield more impactful roles in the transition to a sustainable economy than higher-level bodies such as Nations. Conversely, the assessment of CO₂ removals from forests and other land uses is traditionally lacking at the local compared to the national level. 

Following a support request from the local administration for the definition of a long-term climate mitigation plan, we tested a data-driven method relying on eddy covariance (EC) data to quantify the carbon sink of the Aosta Valley Region in northwestern Italy for the period 2010-2022. Our model integrates various approaches, incorporating eddy covariance measurements of CO₂ fluxes, MODIS NDVI data, daily gridded meteorological variables, and a 250m spatial resolution land cover map to calculate the net carbon uptake of the regional ecosystems. A Random Forest model was used to up-scale the eddy covariance data to the regional level, by testing different sets of drivers (air temperature, VPD, Snow (presence/absence), NDVI, solar radiation,...). Furthermore, global models developed in the framework of the FLUXCOM initiative were fine-tuned with the local predictors and applied at a regional scale. Finally, we compared our findings with independent data derived from the National Greenhouse Gas Inventory.

Preliminary results revealed that forests and other ecosystems in the region currently offset on average nearly 70% of anthropogenic emissions in the region, also depending on the interannual variation of air temperature and the occurrence of extreme events. We will delve into the discrepancies among various methods, exploring their respective advantages, limitations, and spatiotemporal variability. This evaluation of the regional carbon budget and associated uncertainties represents an important step toward the benefit of using flux observations for implementing climate-smart land management—a pivotal component in meeting carbon neutrality targets.