Hydrology-driven greenhouse gas emissions from temperate, natural river-floodplain system

Natural river-floodplains are hydrologically dynamic and characterised by an array of diverse aquatic systems, which are significant sources of greenhouse gases (GHG): CO₂ (FCO₂) and CH₄ (FCH₄). Although hydrology can potentially impact GHG emissions, there is a lack of understanding of whether and how hydrological connectivity of side-arms to the main channel shapes aquatic FCO₂ and FCH₄ throughout a year. This study was conducted in a natural, temperate river-floodplain system (Poland), where we used a combination of manual and automated flux chambers and measured a total of 1776 FCH4 and 1783 FCO2 during various water levels across dormant and active seasons. We found that the system was characterized by high spatio-temporal variability of FCO2 and FCH4, which were significantly different between the sites. The highest FCH4 were noted in the disconnected side-arm (p<0.001) and the highest FCO₂ in the river. Our results highlight the critical role of hydrology in driving GHG fluxes, especially CO2, whereas for FCH4, the temperature was a better predictor. Overall, the system was the strongest source of GHG (as CO2-equivalents -CO2-eq) in the summer and the smallest in the spring. The last ten years (2015-2025) estimates of total emissions showed that significantly more (p<0.001) of the CO2-eq was lost during the active seasons, even though the aquatic floodplain area during the dormant seasons was significantly larger (p<0.001). This study highlights that accounting for hydrological diversity, in systems located in the closest proximity and with similar physicochemical characteristics, is crucial to avoid bias in estimates of aquatic GHG emissions. Overall, it fills a knowledge gap on the role of hydrology for CO2 and CH4 emissions in hydrologically complex river-floodplains and, by deriving system-specific emission factors (EF), improves emission inventories. Our findings underscore implications of climate change, including warming and shift in flow regimes, for GHG emissions from river-floodplains. By analyzing long-term data, we sought to understand the temporal variability of GHG.