Abstract
Greenhouse gas (GHG) emission estimates originating from river networks
remain highly uncertain in many parts of the world, leading to gaps in
global inventories and preventing effective management. In-situ sensor
technology advances, coupled with mobile sensors on robotic
sensor-deployment platforms, will allow more effective data acquisition
to monitor carbon cycle processes influencing river CO2and CH4 emissions; however, if countries are to respond
effectively to global climate change threats, data sensors must be
installed more strategically to ensure that they can be used to directly
evaluate a range of management responses across river networks. We
evaluate how sensors and analytical advances can be integrated into
networks that are adaptable to monitor a range of catchment processes
and human modifications. The most promising data analytics that provide
processing, modelling, and visualising approaches for high-resolution
river system data are assessed, illustrating how multi-sensor data
coupled with machine learning solutions can improve both proactive (e.g.
forecasting) and reactive strategies to better manage river catchment
carbon emissions.
Keywords: carbon dioxide; machine learning; methane;
metabolism; sensors; water quality;