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 require extensive site-specific parameterisation (e.g. soil carbon profiles, pore-water chemistry, vegetation-specific model parameters), as well as high-resolution hydrological and meteorological inputs that are often difficult to obtain outside of well-instrumented research sites, which makes regional upscaling challenging. In contrast, data-driven models provide a scalable alternative by leveraging available 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 0.73 for daily CO2 flux predictions compared to 0.64 for linear regression, and an R2 of 0.53 for CH4 flux predictions compared to 0.47 for linear regression. Interestingly, linear regression performed better than random forest for methane flux, which highlights the necessity for comparison. The, interannual variability is less well captured, with annual mean absolute error of 176 gC m−2 yr−1 for CO2 fluxes and 9 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.