Estimation of global methane soil sink using-synthesized datasets and knowledge-guided machine learning

The objective of this proposed research is to estimate the spatial and temporal variability in global methane

methane

A hydrocarbon that is a primary component of natural gas. Methane is also a greenhouse gas (GHG), so its presence in the atmosphere affects the earth’s temperature and climate system

Source: EPA Source

soil sinks using a knowledge-guided machine learning

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

(KGML) framework. This novel framework combines process-based and machine-learning models, and synthesizes multi-source direct and indirect measurements of soil methane oxidation to improve model training, interpretability, and accuracy across spatial and temporal scales. Natural methane oxidation by microbes

microbe

Microbes are tiny living things that are found all around us and are too small to be seen by the naked eye. They live in water, soil, and in the air.

Source: NIH Source

in upland soils is the second largest sink in the global methane budget, but its importance has been widely underestimated. The magnitude and long-term trends of global methane soil sinks are highly uncertain due to overlooked microbial processes and contradicting studies. Accurately quantifying global methane soil sinks is extremely important to reduce biases in current and future global methane budgets. In this proposed KGML framework, process-based models will be used as scientific foundations to develop the KGML hierarchical structure and to generate millions of synthetic data for pretraining. We will build separate machine-learning submodules for soil thermal, hydrological, and biogeochemical processes,

biogeochemical processes

The complex interactions and transformations of chemical elements and compounds that occur throughout Earth's ecosystems, such as nutrient and element cycling between living organisms, the atmosphere, hydrosphere, lithosphere, and pedosphere (soil).

Source: Source

and an overarching model structure to link the submodules. The key biogeochemical constraints (e.g. soil methane substrate, temperature, and moisture influences) will be carefully embedded into the cost function using known principles and empirical functions as knowledge-guided losses. The developed KGML framework will be trained/validated with direct measurements of soil methane oxidation fluxes

flux

The amount of some type of particle or energy crossing a unit area per unit time.

Source: NRC Library Source

from FLUXNET-CH4 and chamber measurements. Using global soil moisture and temperature data, we will further optimize the model to capture temporal and spatial heterogeneity. The finely-constrained KGML model will finally be extrapolated to the global scale and be used to generate new global methane soil sink products at daily and 4-km resolution from 1984 to 2022.