Sensor networks for river system monitoring
Catchment management aimed at improving freshwater quality and reducing
carbon emission is complicated due to multiple transport pathways that
convey water and a wide range of contaminants into rivers (Khamis et
al., 2018). These include point sources such as industrial and municipal
wastes, and non-point contributions such as agriculture. Identifying
hotspot areas (both sources and impacts) is a critical first step in
developing adequate intervention measures to improve water quality;
however, monitoring is needed to evaluate the effects of these
intervention efforts, protect water quality, and meet regulations
(Lofton et al., 2023). Sensor networks provide the potential to meet
these aims, but operational water quality monitoring programs globally
are commonly based on fixed sampling points with periodic manual
collection of ”grab” samples and subsequent laboratory analysis for
targeted parameters. Due to limitations of personnel, equipment, and
access this type of sampling can provide good spatial snapshots of river
conditions at the time of sampling (Meyer et al., 2019) but is difficult
to implement across entire catchments (Xing et al., 2013). These
sampling approaches also largely miss sporadic extreme events, such as
contaminant releases or stormflows (Charriau et al., 2016). In response
to deficiencies in capturing event-based changes in river ecosystem
properties, and with the emergence of more reliable sensor technology,
high-frequency monitoring using field deployable sensors and actuators
is increasing (Blaen et al., 2016, Bieroza et al., 2023). Autonomous and
remotely operated robotic surface vehicles with on-board sensors have
increased the achievable spatial resolution of field-deployed water
quality sensors(Lee et al., 2023), and show great potential to improve
detection of, and response to, short-term changes in river environments
(Powers et al., 2018). For example, localisation of a pollution hotspot
could trigger reactive behaviours, such as increasing the resolution of
data collection or tracking concentration gradients.
In-situ automated systems with multiple sensors that measure at
high-frequency (typically 15-60 min resolution but can vary depending on
the application) can be used to deliver near real-time data (Meyer et
al., 2019, Singh et al., 2022). Various sensors can be deployed to
quantify carbon cycling or to supply information on physicochemical
drivers (Table 1). However, to advance catchment scale carbon
management, networks of these automated systems (i.e. sensor nodes
(Figure 3)) are needed to pinpoint areas, such as those with high
emissions, and to track event propagation through river basins (Zia et
al., 2013). Further potential for enhancing the dimensionality of
environmental data is emerging from the development of autonomous
robotic platforms to deploy sensors in parts of river systems that are
difficult to access. The integration of these approaches and datasets
presents a challenge, but these networks offer significant potential for
advances in real-time understanding and mitigation of risk for river
users, managers, decision-makers, and regulators (Jankowski et al.,
2021, O’Grady et al., 2021).