Evaluating Spatiotemporal Trends of Water Quality in a Coupled Terrestrial-Coastal Basin: The Case of St. Lucie Estuary and River Basin

Water pollution as a result of excess nutrients remains one of the most pressing issues across the globe, negatively affecting aquatic ecosystems. Understanding how water pollution changes over time is important to predict water quality at the basin scale and support the development of pollution mitigation strategies. Spatiotemporal analyses of water quality at the basin scale are complex due to the limited amount of water quality observations and temporal inconsistencies among these observations at different stations; this becomes even more complex in coupled terrestrial-coastal basins where surface waters, ground waters and estuaries are interconnected but water quality observations are not collected consistently for these waterbodies. Statistical methods to study the spatiotemporal patterns of water quality at the basin scale are missing though. Water pollution drivers should be also analyzed alongside water quality constituents to investigate the reasons for changes in water quality. This research explores various statistical methods—trend detection, clustering, and complex network analysis— to study spatiotemporal patterns of water quality constituents—nutrients (nitrogen and phosphorous), salinity and fecal coliform—and the pertinent drivers (e.g., land cover, water table, meteorology and soil) for period 1992-2021 in St. Lucie River and Estuary Basin in Central Florida. This basin has had a history of water pollution issues and is part of the Greater Everglades ecosystem. Our preliminary analyses show that dependencies among water quality stations in term of the study constituents change over time (the spatial pattern is unstable). Our presented framework can be applied for analyzing the space-time structure of water quality in other coupled terrestrial-coastal basins.