Distinct diurnal drivers of estuarine pCO2 revealed by interpretable machine learning.

Pronounced diurnal differences in the partial pressure of CO2 (pCO2) in surface estuary waters are observed, and resolving these disparities is a prerequisite for improving regional carbon budget estimates. However, underlying drivers of pCO2 at the sub-daily scale remain poorly understood mainly due to nocturnal data gaps, which inherently restrict our predictive capacity. This study leverages automated high-frequency observations and constructs an XGBoost-SHAP framework based on Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to decouple environmental drivers of pCO2 during the daytime and nighttime in the Jiulong River Estuary, China. The developed diel-specific models transparently disentangled the diurnal distinction of environmental drivers (coefficient of determination R2: 0.676 for the daytime model and 0.653 for the nighttime model). Results reveal that while daytime dynamics are predominantly governed by thermodynamic forcing, nocturnal dynamics revert to a baseline hydrology-dominated structure where salinity-tracked water mass transport becomes the primary determinant. Furthermore, rather than representing internal ecosystem memory, historical states serve as highly effective predictors by physically capturing the time-lags of advective transport. These findings confirm that carbon regulation drivers undergo a structural transformation characterized by asymmetric diurnal disparities. Therefore, accurate estuarine carbon modeling requires diel-partitioned frameworks that integrate time-lagged hydrodynamic processes, a step that is essential to enhancing the predictive capacity for coastal carbon flux estimates.