Figure 4. Catchment-scale river metabolism estimates can now be
developed from distributed sensor networks : (a) GPP measured in the
1200km2 Deva-Cares catchment, northern Spain
(Rodríguez-Castillo et al., 2019); (b) GPP measured in the
256km2 Ybbs river, Austria (Segatto et al., 2021).
Network outputs such as these can be developed as visualisation tools to
aid catchment management decision-making, with dynamic updating in
near-real time from linked sensors, telecommunication systems, and
computational models.
In the past five years, high-resolution river network data products have
become available at both national and global scales, including
MERIT-Hydro and the associated GRADES dataset (Lin et al., 2019,
Yamazaki et al., 2019) with 35 years of daily flow data from nearly 3
million river segments worldwide. These river network maps have enabled
the further development of biogeochemical models that can be used
alongside discrete location sensor data to quantify how nutrient and
carbon sources, sinks, and transformations vary according to river size,
flow, and season in large watershed networks. For example, Maavara et
al. (2023) used the US National Hydrology Dataset (NHD Plus HR) product
to develop a DOC model for the Connecticut River watershed, NE USA
(Figure 1). This model calibrated GPP, terrestrial DOC loading,
photo-mineralization, and respiration, partly from a sensor network at
10 locations from 1st-8th order
rivers. These continuous dissolved oxygen measurements at 15-min
intervals were used to estimate GPP and ER using a Markov Chain Monte
Carlo algorithm, which was then scaled to estimate GPP across the entire
watershed during all flows and seasons, by calibrating a random forest
ML model (Appling et al., 2018)
Efforts in improving process-based metabolism models have focused on
expanding estimation to a more diverse set of river environments than
previously possible, including estimation in river reaches with large
discontinuities (e.g. flow and water quality regulation) or river
reaches with significant transient storage (Pathak & Demars, 2023).
Progress in this direction is valuable for reducing uncertainties in
global estimates of freshwater carbon fluxes. Such process-based models
could facilitate large-scale assessments of metabolism and its drivers
across river environments, when combined with ML methods (Appling et
al., 2018, Bernhardt et al., 2022). Several other physical properties
currently overlooked in field studies may significantly impact
metabolism and will need to be incorporated into future network models,
for example sediment movement (Risse-Buhl et al., 2023, Schulz et al.,
2023) and groundwater interactions (Galloway et al., 2019), which can
have major impacts on ER.
Quantification of spatial and temporal dynamics of metabolism across
river networks is important for estimating regional carbon emissions
from rivers (Battin et al., 2023). However, only a few studies have
focused on metabolism estimation at the river network scale (Figure 4).
Rodríguez-Castillo et al.(2019) utilized the spatial stream network
model to identify the factors that govern spatial variations in river
metabolism within the Deva-Cares catchment in northern Spain,
highlighting benthic biomass, river channel properties, and human
activities as important controlling factors. Segatto et al. (2021, 2023)
found that ER played a larger role in metabolic stability at the river
network scale in the Ybbs River Austria, whereas GPP showed higher
sensitivity to flow-induced disturbances and variations in light
availability. Mejia et al. (2018) used the BAyesian Single-station
Estimation (BASE) (Grace et al., 2015) model to estimate metabolism over
a year at ten sites across the Methow River network in Washington State,
USA. Their findings indicated that metabolism timing may vary between
sites within a river catchment due to the combined influence of local
physicochemical conditions, despite having similar regional climates.
Metabolism studies at the river network scale are admittedly
data-intensive and these approaches need to be evaluated in river
systems that are heavily polluted and where water quality often varies
significantly over even short distances (Casillas-García et al., 2021).
In these systems the implications may be that more dense networks of
fixed and robot-mounted sensors are required, alongside additional
predictor datasets such as point-source input locations and land use;
however, such information is increasingly becoming available with
advances in sensor technology, remote sensing products, and modelling
techniques including ML. Mobile robots can be used to both increase the
range and spatial resolution of the data on which models are trained and
validate predictive models by increasing empirical field data
collection.