A harmonized global prediction of biodegradable dissolved organic carbon in freshwater systems using optical indices...

Biodegradable dissolved organic carbon (BDOC) is the most reactive fraction of dissolved organic matter (DOM) and a major driver of carbon dioxide (CO₂) emissions from inland waters, yet its large-scale assessment is constrained by incubation-based measurements. This study conducted a global meta-analysis integrating harmonized BDOC observations, optical DOM indices, and machine-learning (ML) modeling to enable scalable BDOC prediction across freshwater systems. A total of 1063 paired observations were compiled from 17 studies across eight countries, with cross-study comparability improved through correction of variable incubation periods using first-order degradation kinetics, and standardization of UV-visible absorbance wavelengths. The harmonized dataset revealed pronounced hydrological and climatic controls on BDOC dynamics. Biodegradation rate constants were consistently higher in rivers than in lakes, with maximum values observed in temperate rivers during the wet season. Seasonal analyses showed a decoupling between bulk dissolved organic carbon (DOC) and BDOC, with dry conditions favoring DOC accumulation and aromaticity, while wet conditions enhanced inputs of bio-labile DOM. Across climatic gradients, BDOC exhibited greater sensitivity to environmental variability than bulk DOC. Using DOC concentration, absorbance at 254 nm, and humification index (HIX) as predictors, nonlinear ML models outperformed linear regression. Extreme gradient boosting (XGBoost) achieved the highest predictive performance (R² > 0.6), with stronger accuracy for riverine than lacustrine systems. Overall, this study demonstrates that harmonized optical datasets combined with ML modeling provide a robust and scalable alternative to incubation-based BDOC measurements, advancing quantitative assessment of freshwater carbon biodegradability and inland-water carbon cycling.