Measuring methane (CH 4 ) emissions from aquatic ecosystems is essential for constraining greenhouse gas budgets and understanding ecosystem functioning. In these systems, CH 4 is released through diffusion and ebullition, two pathways with distinct temporal dynamics and controls. Recent advances in continuous in situ sensing have generated high-frequency CH 4 time series that require automated methods for robust interpretation. Here, we present MethaneSignalProcessor (MSP), an open-source computational framework for separating and quantifying diffusive and ebullitive CH 4 fluxes from concentration time series. The pipeline integrates automated data cleaning, band-pass filtering, dual-branch correction, peak detection, and regression-based flux estimation within a unified workflow. MSP was tested using two independent datasets from contrasting environments: a CH 4 -rich tropical soda lake and temperate freshwater systems. Across both datasets, the method consistently reconstructed diffusion-dominated signals, identified multiple valid regression segments, and isolated transient ebullitive events. Estimated fluxes were consistent with ranges reported in the literature. By enabling reproducible decomposition of CH 4 emission dynamics, MSP provides a transferable framework for analyzing high-frequency aquatic greenhouse gas data and supports improved integration of sensor-based observations into ecosystem-scale assessments. • Automated pipeline for separating diffusive and ebullitive methane fluxes at the water–air interface from time-series data. • Robust across heterogeneous sensors, chamber designs, and field conditions. • Separates diffusive and ebullitive components without manual parameter tuning. • Open-source tool enabling standardized and transferable methane flux analysis.
Cardona et al. (Wed,) studied this question.