Exploring Estuarine Nutrient Susceptibility DONALD SCAVIA* AND YONG LIU School of Natural Resources & Environment, University of Michigan, Ann Arbor, Michigan 48109 Received December 1, 2008. Revised manuscript received March 12, 2009. Accepted March 17, 2009. The susceptibility of estuaries to nutrient loading is an important issue that cuts across a range of management needs. We used a theory-driven but data-tested simple model to assist classifying estuaries according to their susceptibility to nutrients. This simple nutrient-driven phytoplankton model is based on fundamental principles of mass balance and empirical response functions for a wide variety of estuaries in the United States. Phytoplankton production was assumed to be stoichiometrically proportional to nitrogen load and an introduced “efficiency factor” intended to capture the myriad processes involved in converting nitrogen load to algal production. A Markov Chain Monte Carlo algorithm of Bayesian inference was then employed for parameter estimation. The model performed remarkably well for chlorophyll estimates, and the predicted estimates of primary production, grazing, and sinking losses are consistent with measurements reported in the literature from a wide array of systems. Analysis of the efficiency factor suggests that estuaries with the ratio of river inflow to estuarine volume ( Q/ V) greater than 2.0 per year are less susceptible to nutrient loads, and those with Q/ V between 0.3 and 2.0 per year are moderately susceptible. This simple model analysis provides a first-order screening tool for estuarine susceptibility classification. Introduction Eutrophication is a threat to coastal waters that is most often a result of society-mediated delivery of excess nutrients (1-4). This overenrichment can lead to serious and negative effects, such as harmful algal blooms, habitat loss, biodiversity changes, bottom oxygen depletion, and fishery loss (4, 5). Determining nutrient loading targets to ameliorate these impacts is ultimately an estuary-specific enterprise; however, there is also a growing need to understand more generally why some systems are more susceptible than others so that management guidance can be provided across systems (6). The diversity of estuaries has made classification an important and difficult question for researchers and decision makers since the 1950s (7-9). The National Research Council proposed 12 factors that control estuarine responses, in- cluding physiographic setting, primary production, nutrient load, dilution, water residence time, stratification, hypsog- raphy, grazing of phytoplankton, suspended materials load and light extinction, denitrification, spatial and temporal distributions of nutrient inputs, and allochthonous organic matter inputs (4). Some recent U.S. classification efforts include a dissolved concentration potential (DCP) index (2), an Assessment of Estuarine Trophic Status (ASSETS) meth- odology (10), the Coastal and Marine Ecological Classification Standard (CMECS) conceptual classification (11), stressor- response relationships developed over broad geographical scales (12), and a multivariate regression analysis as part of a synthesis to guide development of estuarine nutrient criteria (13). Similar efforts have also been developed for Peninsular Malaysia (14), Portugal, the EU Water Framework Directive (15), and England and Wales (16). A review of 26 classification schemes found that past systems focused mainly on terrest- rial and aquatic systems and for specific regions and habitat types (9, 12). Kurtz et al. (6) reviewed dozens of classification schemes and concluded that the distinctions among ap- proaches appear to be between hierarchical and nonhier- archical structures, data-driven and theory-driven, and functional vs physical structural and that some classifications combine two or more methods or combine classification with other tools like modeling. Our approach is nonhierarchical, theory-driven but data- tested, and functional. It is a modeling approach to identify key features useful for classification. We use a simple model, based on fundamental principles of mass balance, empirical response functions, and an introduced estuarine efficiency term for a wide variety of estuaries to explore the basis for their susceptibility to nutrient loads, ultimately contributing to a classification scheme to guide nutrient control policies. As such, our aim is to develop a screening model for estuaries in general, not a prediction or forecasting model for specific estuaries. Methods Data Sources. Data for 99 estuaries are described in NEEA Estuaries Database (http://ian.umces.edu/neea) (3). For our analysis, we used 75 of those systems: 14 estuaries were dropped from our analysis based on extreme physical characteristics (e.g., very shallow, very deep, long residence time, or excessive loads). Ten others were dropped because early attempts with our model generated estimates of estuarine efficiency that were quite unrealistic (see below and Supporting Information). The remaining 75 estuaries (37 drowned rivers; 19 lagoons; 9 coastal bays; 10 fjords) still represent a diversity of depths (0.5 to 46 m), volumes (1.7 × 10 7 to 2.9 × 10 10 m 3 ), residence times (4 to 979 days), total nitrogen (TN) loads (1.3 × 10 4 to 5.3 × 10 7 kg/year), and summer surface chlorophyll concentrations (2.3 to 24.8 μg/L) (see Supporting Information). Freshwater discharge, salinity, and ocean boundary nitrogen concentrations were also obtained from this database; however, we found the reported values for ocean salinity were inconsistent with other published values for some subtributaries of the Chesapeake Bay. Accordingly, we recalculated water residence times (see below), based on updated salinity estimates for the Chester, Choptank, Rappahannock, Tangier/Pocomoke, and York river subestuaries from 1222, 713, 185, 1120, and 121 days to 276, 85, 108, 586, and 92 days, respectively. Growing season chlorophyll a concentrations were derived from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery reported monthly for 1997 to 2004 (http://geoportal. kgs.ku.edu/estuary/) (17). We used June-August averages for each of the 7 years. Annual average total nitrogen daily loads, based on the most recent SPARROW model updates (18), were provided by the U.S. Geological Survey (R. Alexander, personal communication). Because SPARROW is not well suited for the relatively flat Florida watersheds, we used NOAA-report fluxes reported on the NEEA Web site. Model Development. While models can be useful tools for describing and predicting specific estuarine responses to * Corresponding author e-mail: scavia@umich.edu. Environ. Sci. Technol. 2009, 43, 3474–3479 3474 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 10, 2009 10.1021/es803401y CCC: $40.75  2009 American Chemical Society Published on Web 04/10/2009