Drivers of river carbon cycle processes
Understanding the drivers of carbon cycling in rivers is needed to
predict the effects of modification by stressors such as warming, land
use changes and flow regulation (Bernhardt et al., 2018). Unlike
terrestrial systems, carbon processing in rivers may not be synchronized
with subsequent emissions of the produced GHG due to the dynamic nature
and spatiotemporal variability in their physicochemical and biological
characteristics(Dodds et al., 2013). Continuous measurements of the key
drivers of metabolism in rivers (e.g. hydrological conditions, light,
temperature, organic matter availability, and nutrient concentrations)
are needed therefore to compare and pinpoint their relative importance
under different stressor regimes. Additionally, because metabolism and
physicochemical drivers act at multiple spatial scales, from local
(riparian vegetation, channel morphology) to regional (climate,
topography), and vary along the river network (Alberts et al., 2017),
their combined impacts can only be examined by in-situ sensor networks
and remotely-sensed data products. For example, the extra dimensionality
offered by mobile robots with on-board sensors offers a potential
solution to measuring spatial variation in such parameters along the
course of a river.
River hydrology plays a significant role in shaping metabolism in rivers
due to its control on ecosystem structure and functioning (Von Schiller
et al., 2008, Hosen et al., 2019, Maavara et al., 2023). In their study
based on sensor data from 222 US rivers, Bernhardt et al. (2022) found
light and flow stability (and their interaction) to be key controls on
primary production and respiration. Studies in temperate rivers have
shown that, in addition to the obvious seasonal drivers of Gross Primary
Production (GPP) (i.e. light availability, including canopy shading, and
temperature), GPP’s dependence on flow should be considered in the
context of river size (Hosen et al., 2019). In large rivers, GPP is
maximized at low flows, but reduced in high flows due to short water
residence times and high turbidity obscuring light availability (Roberts
et al., 2007, Pathak et al., 2022), while there is little flow-related
change on GPP fluxes in smaller rivers (Hosen et al., 2021, Maavara et
al., 2023). The dependence of ecosystem respiration (ER) on flow is
somewhat less straightforward; low flows may reduce benthic production
due to riverbed drying, but ER can increase after flow resumes, fuelled
by the accumulation of terrestrial organic matter on the dry riverbed
(Acuna et al., 2005). Maavara et al. (2023) showed that ER was generally
maximized in a temperate forested watershed close to median flows when
water residence times allowed ample time for carbon uptake to occur,
with higher flows resulting in a deeper and wider water column allowing
for more DOC availability and more uptake due to larger water column
volume.
Although light availability is a key driver of primary production (Savoy
& Harvey, 2021), it is not straightforward to model GPP as other
factors impact river autotrophs such as turbidity, vegetation, nutrient
availability/stoichiometry, and shading (Behrenfeld & Falkowski, 1997).
Light and temperature models can be developed for whole river networks
by calibrating ML approaches from local sensor data and scaling the
findings using remote sensing products such as land use/cover
classifications and digital elevation models for topographical
information (Segatto et al., 2021, Maavara et al., 2023). Drones with
attached sensors could be utilised to build and validate such models
with high spatial resolution data. Light availability can also modify
temperature (Nebgen & Herrman, 2019), which has a stronger control on
ER compared to GPP, indicating a possibility of higher
CO2 emissions from rivers with climate warming (Demars
et al., 2011). However, the impact of warming on emissions is still less
predictable compared to estimates derived from metabolic theory (Battin
et al., 2023). Incoming solar radiation can also mineralise DOC to
inorganic forms and this must be considered alongside aquatic biological
processes (Maavara et al., 2021). Indeed, recent research has shown that
the magnitude of photomineralization during low flows as well as in
winter often dramatically exceeds ER fluxes. Failing to consider
year-round photomineralization fluxes may vastly underestimate the total
magnitude of DOC uptake and CO2 production (Maavara et
al., 2023).
While GPP and ER are sensitive to nutrient loading, studies have yielded
equivocal results regarding the impact of nutrient loading. Some studies
have reported an increase in GPP and ER due to nitrogen and/or
phosphorus loading (Kominoski et al., 2018), whereas others have
suggested that nutrient concentrations may be only secondary drivers due
to the effects of light and food web structure (Dodds & Cole, 2007).
Conversely, some studies have found evidence for reverse causality,
where metabolism variations strongly control riverine nutrient dynamics
(Jarvie et al., 2018, Pathak et al., 2022). The development of sensor
networks, and sensor deployment technologies, designed specifically to
monitor river ecosystem carbon dynamics will therefore enable the
relative importance of multivariate drivers of metabolic processes and
their feedback to be understood in far more detail. Moreover,
bidirectional relationships between water quality and biogeochemical
cycles could eventually be explained and predicted through ML using both
water quality information from sensors alongside next generation
sequencing data focused on microbial communities and their
biogeochemical processes (Díaz-Torres et al., 2022) such as sulphur,
nitrogen, phosphorus and carbon metabolism.