A Magnitude–Variability Framework for Dynamic River Water Quality Characterization

Continuous river water-quality monitoring increasingly captures short-term fluctuations that are poorly represented by conventional static assessments, yet frameworks for translating high-frequency observations into interpretable dynamic water-quality states remain limited. In this study, we developed a state-based framework for dynamic water-quality characterization using continuous river monitoring data. Ecological water quality departure (EWQD) was first used to quantify deviations from background conditions, after which two complementary descriptors, exposure level (EWQDL) and exposure variability (EWQDV), were introduced to characterize the magnitude and temporal instability of water-quality departures. Their joint distribution was then used to construct a two-dimensional dynamic state space, from which four representative departure states were identified to describe distinct combinations of departure intensity and temporal variability. Machine-learning models were further employed to identify the environmental controls associated with transitions among these dynamic states. The results showed that 34.7% of monitoring days exhibited within-day transitions in water-quality states, demonstrating substantial short-term variability. Simulated conventional grab sampling failed to identify 18.8% of unfavorable water-quality states revealed by continuous monitoring, illustrating the information loss that can arise from discrete observations. The EWQDL–EWQDV state space distinguished persistent, transient, stable, and highly unstable departure regimes that could not be differentiated from departure magnitude alone. Hydrometeorological conditions, particularly antecedent rainfall and temperature, were the dominant controls on dynamic state transitions, whereas land use primarily regulated the background susceptibility of river systems to water-quality departures. These findings demonstrate that river water quality is better represented as a time-varying state process than as a static condition. The proposed framework provides a systematic approach for translating continuous monitoring records into interpretable dynamic states and offers a basis for more representative water-quality characterization and adaptive monitoring strategies.