Stable Isotope Analysis Reveals Lower-Order River Dissolved Inorganic Carbon Pools Are Highly Dynamic

Abstract

River systems draining peaty catchments are considered a source of atmospheric CO2,thus understanding the behavior of the dissolved inorganic carbon pool (DIC) is valuable. The carbon isotopic composition, delta13C(DIC), and concentration, [DIC], of fluvial samples collected diurnally, over 14 months, reveal the DIC pools to be dynamic in range (-22 to -4.9% per hundred, 0.012 to 0.468 mmol L(-1) C), responding predictably to environmental influences such as changing hydrologic conditions or increased levels of primary production. delta(18)O of dissolved oxygen (DO) corroborates the delta(13)C(DIC) interpretation. A nested catchment sampling matrix reveals that similar processes affect the DIC pool and thus delta(13)C(DIC) across catchment sizes. Not so with [DIC]: at high flow, the DIC export converges across catchment size, but at low flow catchments diverge in their DIC load. Contextualizing delta(13)C with discharge reveals that organic soil-waters and groundwaters comprise end-member sources, which in varying proportions constitute the fluvial DIC pool. Discharge and pH describe well [DIC] and delta(13)C(DIC), allowing carbon to be apportioned to each end-member from continuous profiles, demonstrated here for the hydrological year 2003-2004. This approach is powerful for assessing whether the dynamic response exhibited here is ubiquitous in other fluvial systems at the terrestrial-aquatic interface or in larger catchments.

Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial $$\hbox {CO}_2$$ CO 2

“…It was also obvious that the hydrology has an influence on the level of EpCO 2 . At low flow, DIC concentration is highest (Waldron et al 2007) and biological activity is greatest (as temperature tends to be higher); event flow, whilst flushing out soil CO 2 so increasing the pool size, ultimately dilutes the DIC pool and so lowers saturation of dissolved carbon dioxide. Turbulent waters and colder temperatures reduce biological activity.…”

“…Incised catchment slopes have the most freely-draining humus iron podzols (<1 m deep); the main river valley bottoms generally have freely draining alluvial deposits. For a detailed description of the study site and its geology and climate characteristics, see Waldron et al (2007).…”

“…DIC (mmol L −1 C) is quantified by direct measurement using a headspace analysis approach (Waldron et al 2014), to internal precision better than ±0.03 mmol L −1 . This was regressed onto discharge, which is measured semi-continuously, to generate a relationship from which DIC is predicted, thus creating a continuous DIC profile (Waldron et al 2007). The generated relationship between discharge and DIC was indistinguishable from the same relationship constructed 10 years earlier, allowing confidence that this relationship is temporally stable over the constructed 3 years profile.…”

“…The aqueous partial pressure of carbon dioxide is a measure of the capacity for CO 2 exchange between the water and the atmosphere (Li et al 2012). The excess partial pressure of carbon dioxide (EpCO 2 ) in surface freshwater is a dynamic representation of the interacting biogeochemical and hydrological processes that produce, consume, and transport carbon dioxide (Waldron et al 2007). If the river is over-saturated (an excess partial pressure, >1), CO 2 can be effluxed, representing direct linkage of terrestrial and atmospheric carbon cycles (Butman and Raymond 2011).…”

CADICA: Continuous Automated Dissolved Inorganic Carbon Analyzer with application to aquatic carbon cycle science

“…Hope et al (2004) reached similar conclusions using fortnightly sampling of riverine pCO 2 from a peatland catchment in the UK. There is clearly promise in using fluvial DIC to infer changes in catchment productivity but higher temporal resolution work has shown that variation can be significant over diurnal time scales (e.g., Waldron et al 2007;Parker et al 2007), thus a method is required to quantify this variation over long time scales and at high resolution.…”