Development of a Model Framework for Terrestrial Carbon Flux Prediction: the Regional Carbon and Climate Analytics Tool (RCCAT) Applied to Non-tidal Wetlands

Abstract. Wetlands play a pivotal role in carbon sequestration but emit methane (CH₄), creating uncertainty in their net climate impact. Although process-based models offer mechanistic insights into wetland dynamics, they are computationally expensive, uncertain, and difficult to upscale. In contrast, data-driven models provide a scalable alternative by leveraging extensive datasets to identify patterns and relationships, making them more adaptable for large-scale applications. However, their performance can vary significantly depending on the quality and representativeness of the data, as well as the model design, which raises questions about their reliability and generalizability in complex wetland systems. To address these issues, we present a data-driven framework for upscaling wetland CO₂ and CH₄ emissions, across a range of machine learning models that vary in complexity, validated against an extensive observational dataset from the Sacramento-San Joaquin Delta. We show that artificial intelligence (AI) approaches, including Random Forests, gradient boosting methods (XGBoost, LightGBM), Support Vector Machines (SVM) and Recurrent Neural Networks (GRU, LSTM), outperform linear regression models, with RNNs standing out, achieving an R² of 0.71 for daily CO₂ flux predictions compared to 0.62 for linear regression, and an R² of 0.60 for CH₄ flux predictions compared to 0.54 for linear regression. Despite that, interannual variability is less well captured, with annual mean absolute error of 193 gC m^-2 yr^-1 for CO₂ fluxes and 11 gC-CH₄ m^-2 yr^-1 for CH₄ fluxes. By integrating vertically-resolved atmospheric, subsurface, and spectral reflectance information from readily available sources, the model identifies key drivers of wetland CO₂ and CH₄ emissions and enables regional upscaling. These findings demonstrate the potential of AI methods for upscaling, providing practical tools for wetland management and restoration planning to support climate mitigation efforts.