Estimates of greenhouse gas emissions 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 CO₂ and CH₄ emissions. However, if countries are to respond effectively to global climate change threats, 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, modeling, and visualizing 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 (e.g. alerts) strategies to better manage river catchment carbon emissions.

中文翻译：

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整合传感器数据和机器学习，推动河流碳排放的科学和管理


在世界许多地方，河流网络温室气体排放的估计仍然高度不确定，导致全球清单出现缺口，阻碍了有效管理。原位传感器技术的进步，加上机器人传感器部署平台上的移动传感器，将允许更有效的数据采集，以监测影响河流 CO₂ 和 CH₄ 排放的碳循环过程。然而，如果各国要有效应对全球气候变化威胁，就必须更具战略性地安装传感器，以确保它们可用于直接评估整个河流网络的一系列管理响应。我们评估如何将传感器和分析进展集成到网络中，以适应监控一系列集水过程和人工修改。评估了为高分辨率河流系统数据提供处理、建模和可视化方法的最有前途的数据分析，说明了多传感器数据与机器学习解决方案相结合如何改进主动（例如预测）和被动（例如警报）策略，以更好地管理河流集水区的碳排放。