Integrating sensor data and machine learning to advance the science and management of river carbon emissions

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 CO 2 and CH 4 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 Posted on 16 Apr 2024 | CC-BY 4.0 | https://doi.org/10.22541/essoar.171322696.69831029/v1| This is a preprint and has not been peer-reviewed.Data may be preliminary.are assessed, illustrating how multi-sensor data coupled with machine learning solutions can improve both proactive (e.g.forecasting)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.