Diagnosing Dissolved Organic Carbon Simulation of SWAT-C model Using Machine Learning Approaches

Abstract. Dissolved organic carbon (DOC) plays a critical role in the terrestrial carbon cycle, and accurate simulation of its dynamics is essential for understanding carbon balance and climate change mitigation. However, DOC simulations still involve large uncertainty under complex environmental conditions. To address this challenge, we proposed a Module Diagnosis Framework (MDF) that quantitatively identifies the module-level sources of uncertainty in DOC modeling. The SWAT-MDF integrates the physically based SWAT-Carbon (SWAT-C) model with a data-driven module that employs machine learning algorithms and applies Shapley additive explanations (SHAP) and residual analysis to diagnose the uncertain source of DOC simulation in the Yalong River Basin.We found that the the data-driven module based on bidirectional long short-term memory (Bi-LSTM) networks achieved good performance for daily DOC predictions with an average NSE = 0.62 and R² = 0.67 while the original SWAT-C model yielded average NSE = 0.51 and R² = 0.61. Despite this improvement, the testing performance remains limited, suggesting that the main uncertainty arises from the structural limitations of SWAT-C and highlighting the need for further structural improvement and module-level diagnosis. The MDF results revealed that the carbon cycle module and pollutant transport module mainly regulated the magnitude and variation of DOC predictions in the original SWAT-C model, and the vegetation growth module and the carbon cycle module were major sources of DOC prediction uncertainties. We therefore proposed that further improvements in DOC prediction in the SWAT-C model should focus on the vegetation growth, carbon cycle modules. Our proposed SWAT-MDF framework significantly enhances the reliability of DOC simulations, and provides a quantitative basis for improving the SWAT-C model and offers a generalizable approach to module optimization in similar coupled modeling frameworks.