On Improvement of Soil Organic Carbon Modeling and Simulation viaIntegrated Deep Learning and Data Assimilation Approaches

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

acquire energy and nutrients from soils and release CO2

carbon dioxide (CO2)

An important heat-trapping gas, also known as a greenhouse gas, that comes from the extraction and burning of fossil fuels (such as coal, oil, and natural gas), from wildfires, and natural processes like volcanic eruptions.

Source: NASA Source

and other gases to atmosphere. The next generation earth system model strives to integrate microbes to improve its accuracy. The increasing available dataset can help reduce the model uncertainty by a Bayesian Markov Chain Monte Carlo (MCMC) data assimilation approach. Despite the progress, to deepen the use and value of large dataset, this project is proposed to combine a deep learning technique and MCMC data assimilation to further improve the model performance. Based on a former success in integrating dataset of Harvard Forest (HF), this project will integrate other covariates such as environmental and edaphic dataset at HF that have not been considered formerly. The current proposed study will integrate deep learning, data assimilation, a highly comprehensive monitoring dataset of plants, soils and microbes, and a microbial model to optimize the model representation of soil carbon

soil carbon

The measurable amount carbon dioxide stored in soil. It is the primary indicator of soil health, and it impacts the chemical, physical, and biological characteristics of the soil.

Source: Science Direct Source

over two decades at HF. This project is expected to identify more accurate microbial models and will likely provide alternative model structures to scale microbial functions to regional and global scales. Through the collaborations with ORNL scientists, the HBCU faculty member and students will promote their research capacity. Ultimately, work force from minority groups can be prepared to help advance soil model development.