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 (RodrÃguez-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.