Fundamental drivers of dissolved organic matter composition across an Arctic effective precipitation gradient

The standard model for aquatic ecosystems is to link hydrologic connectivity to dissolved organic carbon (DOC) concentration and dissolved organic matter (DOM) composition and, ultimately, reactivity. Studies across effective precipitation gradients have been suggested as models for predicting how carbon cycling will change in Arctic aquatic ecosystems with projected drying (i.e., reduced hydrologic connectivity). To evaluate links between DOM dynamics and hydrologic connectivity, 41 stream samples from Greenland were analyzed across an effective precipitation gradient for DOM optical properties and elemental composition using ultrahigh-resolution mass spectrometry. Sites with negative effective precipitation and decreased hydrologic connectivity exhibited elevated specific conductivity (SpC) and DOC concentrations as well as DOM composition indicative of decreased hydrologic connectivity, for example, lower aromaticity, assessed using carbon-specific UV absorbance at 254 nm, decreased relative abundances of polyphenolic and condensed aromatic compounds, and increased relative abundances of highly unsaturated and phenolic compounds. Allochthonous inputs decreased as the summer progressed as exhibited by decreases in aromatic compounds. A decrease in molecular richness and N-containing compounds coincided with the decrease in allochthonous inputs. DOC concentrations increased over the summer but more slowly than SpC, suggesting degradation processes outweighed combined evapoconcentration and production. The patterns in DOM composition suggest evapoconcentration and photodegradation are dominant controls. However, when hydrologic connectivity was high, regardless of effective precipitation, DOM reflected allochthonous sources such as snowmelt-fed wetlands. These results highlight the challenges of modeling carbon cycling in aquatic ecosystems across effective precipitation gradients, particularly those with strong seasonality and regional variability in hydrologic inputs.