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.