Advancing river management with sensor networks and data analytics
Many traditional methods for monitoring river systems are resource-intensive and deliver results sometimes weeks-months after sampling and ecosystem changes occur (e.g. biological sampling followed by laboratory identification, then analysis/interpretation, or; ‘snapshot’ sampling of water chemistry (Dean & Battin, 2024)). Manually operated sensors furthermore offer only a snapshot of temporal dynamics. Both delays and low-resolution data can result in less effective management responses, such as detecting pollution incidents, or optimizing water systems where trade-offs between water supply and environmental needs are required. In contrast, there is increasing availability of affordable, robust, and high-resolution sensors, coupled with distributed data transfer systems (e.g. LoRaWAN - long range wide area network) and the array of data analytics solutions. If we are to truly revolutionise water resource management, river monitoring needs to embrace the collation of large, integrated datasets in complete packages rather than considering layered approaches (Dean & Battin, 2024) that re-iterate long-standing collection protocols. For instance, IoT devices can incorporate software sensors (such as those based on machine learning) for predicting a range of water quality parameters based on the information from physical sensors (Ba-Alawi et al., 2023), reducing monitoring costs. River ecosystem metabolism for example, which can be quantified routinely and continuously using optical measurements of dissolved oxygen, would be a core carbon cycle process measurement which has been found to respond consistently to environmental change with a high sensitivity, including detecting effects of river restoration practices (Ferreira et al., 2020), wastewater treatment upgrades (Arroita et al., 2019) and stressor events such as sedimentation (Aspray et al., 2017). When combined with a range of other sensor-based measurements, it offers significant potential for assessing the impacts of river system responses to human modification.