Integrating high-resolution sensor data with analytics advances
Sensor networks present opportunities to develop a new understanding of fundamental environmental processes alongside applied management scenarios, by coupling high-resolution data sources with concurrent advances in statistical analysis and modeling. For example, 10 years ago, many studies using dissolved oxygen sensing technology typically comprised snapshots of metabolism on daily timescales at the river reach (~10-100m) scale (Hoellein et al., 2013, Demars et al., 2015), sometimes with seasonal repeat sampling (Von Schiller et al., 2008). With the development of more reliable and robust sensors including automatic cleaning (e.g. wipers, pressurized air), dissolved oxygen time-series data have been collected to calculate continuous metabolism (i.e. GPP, ER, NEP) over periods of months-years (Roberts et al., 2007, Pathak et al., 2022). The spatial distribution of sampling networks has also seen a recent shift from reach-based assessments toward efforts to quantify metabolism for catchments and whole river systems (Rodríguez-Castillo et al., 2019, Segatto et al., 2021, Segatto et al., 2023). Large volumes of high-resolution water quality data are becoming available from continental-global scale networks (Bernhardt et al., 2022). These ongoing increases in sensor data coverage offer the potential for significant improvements in pinpointing key drivers and constraints of aquatic ecosystem health, such as temperature, light, nutrients and discharge (Bernhardt et al., 2018) enabling improved decision-making and more strategic intervention efforts.