Contrasting influences of diurnal patterns and rainfall events on riverine CO2 degassing: insights from in situ high-resolution monitoring

Abstract

High-resolution measurements of CO₂ flux are critical for accurate estimation of CO₂ degassing in inland waters. However, persistent challenges remain due to limited high-frequency datasets and incomplete understanding of diurnal CO₂ dynamics and rainfall influences. This study implemented 35 days high-resolution monitoring campaign targeting hydrological, hydrogeochemical, and atmospheric parameters across three transects of the Lijiang River (LJR), a representative karst river in southwestern China, during the monsoon season. Key findings demonstrate that: (1) The LJR consistently functioned as a CO₂ source. Neglecting diurnal pCO_2air variability introduced systematic biases, causing daytime flux underestimation (136.9 mg·m⁻²·h⁻¹) and nighttime overestimation (72.6 mg·m⁻²·h⁻¹). Site-specific continuous pCO_2air monitoring reduced these errors by 18–32 % compared to fixed atmospheric defaults; (2) Flood events amplified CO₂ emissions by 200–300 %, primarily driven by turbulence-enhanced gas transfer velocity ($k_{{CO}_2}$, contributing 91.0–94.6 % to evasion). In contrast, non-flood periods were governed by metabolic pCO_2water signals (71.0–86.9 % flux contribution). This mechanistic shift necessitates phase-specific sampling strategies; (3) Excluding flood events caused 34.1–45 % flux underestimation in this karst basin, exceeding parametric uncertainties (V, S, pCO_2water and pCO_2air) by 2.2–4.5 times. DIC-enriched floods delivered > 62 % of annual CO₂ evasion, aligning with global observations of 25–45 % tropical flux underestimation from flood neglect; (4) Monitoring ≥ 3 flood events per hydrological year and at the same time extend the monitoring to the transition before and after the flood, quantifying that carbonate weathering pulses dominate the annual escape can reduce flux deviation by 60–70 %. Combining dynamic pCO_2water and pCO_2air measurements with specific stage models can reduce the uncertainty of global river fluxes by 30–50 %, especially in carbonate-rich catchments. Conventional approaches risk overestimating CO₂ degassing through neglect of diurnal variability during stable hydrologic conditions while underestimating fluxes by disregarding rainfall impacts. These results underscore the necessity of high-frequency monitoring protocols to refine greenhouse gas (GHG) flux quantifications in fluvial systems.