The use of high-frequency water quality monitoring has increased over several decades. This has mostly been motivated by curiosity-driven research and has significantly improved our understanding of hydrochemical processes. Despite these scientific successes and the growth in sensor technology, the large-scale uptake of high-frequency water quality monitoring by water managers is hampered by a lack of comprehensive practical guidelines. Low-frequency hydrochemical data are still routinely used to review environmental policies but are prone to missing important event-driven processes. With a changing climate where such event-driven processes are more likely to occur and have a greater impact, the adoption of high-frequency water quality monitoring is becoming more pressing. To prepare regulators and environmental and hydrological agencies for these new challenges, this paper reviews international best practice in high-frequency data provision. As a result, we summarise the added value of high-frequency water quality monitoring, describe international best practices for sensors and analysers in the field, and evaluate the experience with high-frequency data cleaning. We propose a decision workflow that includes considerations of monitoring data needs, sensor choice, maintenance and calibration, and structured data processing. The workflow fills an important knowledge-exchange gap between research and statutory surveillance for future high-frequency water quality sensor uptake by practitioners and agencies.

The use of in‐situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real‐time data for science, the data are prone to technical anomalies that can undermine the trustworthiness of the data and the accuracy of statistical inferences, particularly in spatial and temporal analyses. Here we propose a framework for detecting anomalies in sensor data recorded in stream networks, which takes advantage of spatial and temporal autocorrelation to improve detection rates. The proposed framework involves the implementation of effective data imputation to handle missing data, alignment of time‐series to address temporal disparities, and the identification of water quality events. We explore the effectiveness of a suite of state‐of‐the‐art statistical methods including posterior predictive distributions, finite mixtures, and Hidden Markov Models (HMM). We showcase the practical implementation of automated anomaly detection in near‐real time by employing a Bayesian recursive approach. This demonstration is conducted through a comprehensive simulation study and a practical application to a substantive case study situated in the Herbert River, located in Queensland, Australia, which flows into the Great Barrier Reef. We found that methods such as posterior predictive distributions and HMM produce the best performance in detecting multiple types of anomalies. Utilizing data from multiple sensors deployed relatively near one another enhances the ability to distinguish between water quality events and technical anomalies, thereby significantly improving the accuracy of anomaly detection. Thus, uncertainty and biases in water quality reporting, interpretation, and modeling are reduced, and the effectiveness of subsequent management actions improved.