Resolving spatiotemporal water quality variability in a river-influenced estuarine system using high-frequency autonomous surface vessel observations

River-influenced estuarine systems support critical habitats but are vulnerable to water quality degradation driven by freshwater discharge variability, nutrient loading, and climate forcing. Understanding high-frequency variability and spatial heterogeneity remains limited due to reliance on discrete or short-term observations. This study addresses these gaps by analyzing multi-year (2021–2024), high-frequency observations collected by an autonomous surface vessel across eight transects in the Western Mississippi Sound. Ten parameters, including chlorophyll-a, phycocyanin, phycoerythrin, temperature, pH, dissolved oxygen, partial pressure of carbon dioxide, salinity, colored dissolved organic matter (CDOM), and turbidity were evaluated using Seasonal Mann–Kendall trend analyses, Kruskal–Wallis tests, Generalized Additive Models, Principal Component Analyses (PCA), hierarchical clustering, and a Composite Water Quality Score (CWQS). All parameters exhibited significant seasonal variability (Kruskal–Wallis p < 0.001), with elevated temperature and algal indicators during summer and reduced biological activity in winter. PCA identified a dominant freshwater–marine mixing and metabolic gradient (47.5% variance) and a secondary axis reflecting carbon cycling and oxygen dynamics (24.2%). Interannual variability was highest for phycoerythrin, CDOM, and temperature, with peak biomass in 2022 indicating episodic bloom events. Discharge–water quality relationships were regime-dependent: correlations were weak overall but strengthened under high-discharge conditions and reversed under low discharge, reflecting shifts between terrestrial and marine controls. Spatial analyses revealed distinct transect groupings and localized extremes. CWQS identified Transect 7 as critically degraded (CWQS = 0.54), driven by eutrophication. These findings demonstrate the value of high-frequency autonomous observations for resolving regime-dependent dynamics and informing spatially targeted coastal management. • High frequency ASV observations resolve spatiotemporal water quality variability. • Discharge water quality relationships are regime dependent across conditions. • PCA identifies freshwater marine mixing and biogeochemical gradients. • Clustering and GAM analyses resolve distinct spatial and seasonal regimes. • Composite Water Quality Scores identify spatially variable water quality patterns.