Conventional process-based models inaccurately represent the intricate spatial diversity of agricultural landscapes, especially the influence of microtopographic features on soil moisture, redox conditions, and greenhouse gas emissions. Assumptions in large-scale modeling frequently fail to align with field data, resulting in considerable uncertainties in the estimations of methane (CH₄) and nitrous oxide (N₂O) fluxes. This gap emphasizes the necessity for a refined approach to modeling that explicitly integrates microtopographic effects into biogeochemical simulations to improve the accuracy of greenhouse gas inventories.

This PhD thesis aims to create an advanced process-based modeling framework that integrates quality empirical data on microtopographic variability into the LandscapeDNDC model. The work aims to enhance the predicted accuracy of greenhouse gas emission models by systematically incorporating soil moisture dynamics, nutrient cycling, and redox-sensitive processes. Furthermore, it will evaluate the effects of various land management strategies and climate change scenarios, ensuring the model's adaptability to changing agroecosystem circumstances.

The project will provide a comprehensive, spatially explicit modeling tool that enhances the precision of regional greenhouse gas inventories and guides climate-smart agricultural policies. The work will facilitate evidence-based decisions for sustainable land management by diminishing uncertainties in CH₄ and N₂O emission estimations. The model's scalability and predictive powers will let stakeholders evaluate future emission hotspots and enhance mitigation methods in reaction to evolving climatic and agronomic conditions.