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;