Advancing river management with sensor networks and data
analytics
Many traditional methods for monitoring river systems are
resource-intensive and deliver results sometimes weeks-months after
sampling and ecosystem changes occur (e.g. biological sampling followed
by laboratory identification, then analysis/interpretation, or;
‘snapshot’ sampling of water chemistry (Dean & Battin, 2024)). Manually
operated sensors furthermore offer only a snapshot of temporal dynamics.
Both delays and low-resolution data can result in less effective
management responses, such as detecting pollution incidents, or
optimizing water systems where trade-offs between water supply and
environmental needs are required. In contrast, there is increasing
availability of affordable, robust, and high-resolution sensors, coupled
with distributed data transfer systems (e.g. LoRaWAN - long range wide
area network) and the array of data analytics solutions. If we are to
truly revolutionise water resource management, river monitoring needs to
embrace the collation of large, integrated datasets in complete packages
rather than considering layered approaches (Dean & Battin, 2024) that
re-iterate long-standing collection protocols. For instance, IoT devices
can incorporate software sensors (such as those based on machine
learning) for predicting a range of water quality parameters based on
the information from physical sensors (Ba-Alawi et al., 2023), reducing
monitoring costs. River ecosystem metabolism for example, which can be
quantified routinely and continuously using optical measurements of
dissolved oxygen, would be a core carbon cycle process measurement which
has been found to respond consistently to environmental change with a
high sensitivity, including detecting effects of river restoration
practices (Ferreira et al., 2020), wastewater treatment upgrades
(Arroita et al., 2019) and stressor events such as sedimentation (Aspray
et al., 2017). When combined with a range of other sensor-based
measurements, it offers significant potential for assessing the impacts
of river system responses to human modification.