M-ICR013

Scalable On-site GHG Measurements Methodology for Sustainable Land Management Projects

Methodology developer

Carbon Real

Sectoral scope

1. Afforestation and reforestation

2. Agriculture

Status

Under development

Current Stage:

Public consultation

ID:

M-ICR013

Summary:

The capability of the eddy covariance technology to measure carbon fluxes at the ecosystem scale makes this methodology suitable for any project activities focused on sustainable land management, particularly those designed to enhance carbon sequestration from the atmosphere into terrestrial carbon pools. Biogenic carbon fluxes are assessed as a whole without disaggregating individual sources from different carbon pools. This innovative approach is critical for reducing the cumulative errors that typically arise when estimating changes across separate carbon pools over time. In parallel, it enables monitoring and reporting of additionality in real-time, as the quantification of carbon dynamics is based on evaluating Net Ecosystem Exchange (NEE) data from both a control area and a sustainably managed treatment area. Consequently, it effectively measures differences in carbon balance resulting from ecosystem processes such as carbon uptake through photosynthesis and emissions through respiration, decomposition, soil microbial activity, and other ecosystem processes occurring within both zones.

This methodology relies on the combined use of eddy covariance (EC) stations installed specifically for the project, along with those that are part of a global network, which can serve as a complementary source of information. The stations installed specifically for the project provide real-time measurements of carbon fluxes in key areas where sustainable management activities are implemented, as well as in control areas where no project activities take place. By measuring carbon fluxes directly at the project implementation sites, accurate quantification of the actual impact of the activities and dynamic reporting of additionality are possible. The scalability of this model is achieved through the use of ML algorithms capable of spatially extrapolating the data to regions with similar vegetation, topographic, and climatic characteristics. One of the essential features of this approach is the evaluation of model uncertainty and error, which shall be explicitly addressed to ensure clarity regarding the accuracy and precision of its application. At the same time, the methodology is adaptive, allowing for the continuous improvement of models through calibration with independently measured carbon flux data throughout the duration of the project. It also enables the incorporation of new EC technologies and advanced statistical data treatments, all of which will be based on scientific evidence and have the potential to improve data quality and measurement precision throughout the data collection process.

Documentation and public consultation