Figure
1. The global distribution of river monitoring stations with
sensors suitable for developing metabolism estimates and carbon
emissions has a strong spatial bias . Examples of the most dense
nationwide networks are: (a) StreamPULSE sites in the continental USA;
(b) Spanish Environmental Department Water Quality Automatic Information
System (SAICA); (c) a catchment-scale monitoring network in the
Connecticut River, NE USA (Hosen et al., 2021)
In this review, we evaluate the potential for using sensor data and
machine-learning to advance river carbon cycle processes and emission
management, both responsively to stressor events and proactively to
enhance the management of both water resource security and downstream
river system services. Taking into consideration the key drivers of
river carbon processes and emissions, we demonstrate how recent
technological advances in the development and implementation of sensor
networks for river catchment management can be harnessed to improve
knowledge of aquatic processes. We examine how sensor and analytics
advances offer new opportunities to develop strategic monitoring
networks that can capture impacts resulting from a range of catchment
processes and human modifications. We illustrate the benefits of
incorporating emerging, affordable sensor technologies, and novel
robotic sensor deployment technologies, which allow for high-resolution
monitoring, and explain how a variety of water quality parameters can be
used to develop causal relationships between drivers and response
variables. We then assess the most promising analytical approaches and
methods for processing, modelling, and visualising high-resolution river
system data, demonstrating how novel applications of sensor networks
coupled with artificial intelligence (AI) solutions could be developed.