A Framework for Modeling Soil Organic Carbon Dynamics Under Climate Variability

Thesis Abstract

Climate variability significantly influences soil organic carbon (SOC) stocks, thereby affecting terrestrial ecosystem stability and global carbon cycles. However, existing models often lack comprehensive frameworks that integrate climate factors with SOC dynamics, limiting the accuracy of predictions and the development of effective land management strategies. This study aims to develop and validate an integrated modeling framework for SOC dynamics that accounts for climatic variability, with specific objectives including the identification of key climate variables impacting SOC, the formulation of a theoretical model linking climate factors and SOC processes, and the empirical validation of this model using field data. The research adopts a mixed-methods approach, combining quantitative modeling techniques with qualitative assessments to enhance model robustness and applicability. The core methodology involves a quantitative research design underpinned by a sequential explanatory approach. The population for the study comprises soils from agricultural and forested lands within the temperate zone of the Midwest region, with a total of 150 soil sampling sites selected via stratified random sampling. Soil samples are collected at 0-15 cm and 15-30 cm depths, and their SOC content is determined using dry combustion via a LECO CNS analyzer. Climate data, including temperature, precipitation, and humidity, are obtained from regional weather stations for the past decade. Data collection instruments include standardized soil sampling kits and meteorological data logs. The validity and reliability of measurements are ensured through calibration with certified reference materials and repeated sampling for consistency. The analysis employs multiple regression analysis to identify significant climate variables affecting SOC, ANOVA to compare SOC levels across different land-use types, and structural equation modeling (SEM) to develop an integrated framework depicting the causal relationships among climate variables, soil properties, and SOC stabilization mechanisms. The proposed model integrates the theory of soil organic matter stabilization with the climate sensitivity hypothesis, providing a comprehensive understanding of how climate variability influences SOC decomposition, mineralization, and sequestration processes. It hypothesizes that climate factors such as temperature increase and altered precipitation patterns significantly impact SOC stocks by modifying microbial activity, soil moisture, and organic matter inputs. Expected findings indicate that higher temperatures correlate with reduced SOC due to enhanced microbial mineralization, whereas increased precipitation may either facilitate SOC accumulation through plant productivity or accelerate decomposition depending on soil moisture regimes. The model predicts nonlinear responses of SOC to climate variables, emphasizing the importance of integrating multiple factors for accurate predictions. This research offers a significant contribution to the knowledge of SOC dynamics by providing a validated, context-specific framework capable of predicting changes under various climate scenarios. It fills existing gaps by explicitly modeling the interactions among climate variables, soil properties, and biological factors influencing SOC. The study’s findings can inform land management practices aimed at enhancing carbon sequestration and mitigating climate change impacts. The main conclusion underscores the necessity of considering multi-factorial climate influences when modeling SOC dynamics, advocating for adaptive land use policies based on predictive models tailored to regional climatic conditions. Recommendations include the adoption of climate-resilient agricultural practices, the integration of soil carbon monitoring into routine land management, and further research into incorporating additional biotic factors such as microbial community composition. Future studies should explore the applicability of the developed framework across different biomes and extend the temporal scale of analysis to improve the predictive capacity of SOC models under evolving climate patterns.