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Microbial Activity and Root Carbon Inputs Are More Important than Soil Carbon Diffusion in Simulating Soil Carbon Profiles
- Award ID(s):
- 1638688
- PAR ID:
- 10312717
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 126
- Issue:
- 4
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Soils store more carbon than other terrestrial ecosystems 1,2 . How soil organic carbon (SOC) forms and persists remains uncertain 1,3 , which makes it challenging to understand how it will respond to climatic change 3,4 . It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss 5–7 . Although microorganisms affect the accumulation and loss of soil organic matter through many pathways 4,6,8–11 , microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes 12,13 . Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved 7,14,15 . Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.more » « less
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Accurate modeling and prediction of soil organic carbon (SOC) stocks are critical for realistic estimates of climate‐carbon feedback and ecosystem carbon sequestration potential. Process‐based models are widely used for simulating the dynamics of SOC stocks but often have considerable uncertainty. Exploring how process‐based models might be better informed by observations is crucial for improving the ability to forecast SOC. In this study, we assimilated the SoilGrids SOC datasets into the Simplified Photosynthesis and Evapotranspiration model for 39 National Ecological Observatory Network (NEON) terrestrial sites across the continental US using the Predictive Ecosystem Analyzer platform. We explored several strategies to determine the best approach for assimilating both SOC data and other indirect data constraints. Our results show that direct and continuous constraint from SoilGrids SOC data is crucial to achieve more accurate and precise SOC and ecosystem respiration states. The effectiveness of data assimilation is strongly determined by the uncertainty of both models and observations across sites, therefore careful characterization and propagation of uncertainties are important. Indirect constraints on SOC from Moderate Resolution Imaging Spectroradiometer leaf area index, LandTrendr aboveground biomass, and Enhanced Soil Moisture Active Passive soil moisture data also help to reduce the uncertainty in SOC prediction. Overall, our results show that data assimilation represents a promising approach to providing more realistic SOC estimates for regional and global carbon budgets in the future.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2020JG006205
