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The long-term average seasonal distribution of chlorophyll concentration in a central reach of the Rhode River estuary (Maryland, USA) has a peak in the spring of about 90 mg m-3. Here we examine interannual variability in chlorophyll and nutrienf concentrations in recent years. Years are classified as having an average bloom, extraordinary bloom, or no bloom, and patterns of nutrient concentration are described for each category. To determine processes associated with the different categories of behavior, we performed a generalized senslt~vity analysis of a model of nutrient-limited phytoplankton net growth which was previously shown to simulate the chlorophyll concentration and the observed pattern of shift from P to N limitation in an average year. Model parameters controlling the delivery of N and P by the environment and the biological utilization of nutrients by the phyto- plankton were drawn from random distributions based on literature reports or, where available, data from the Rhode River. Model output for each parameter set was classified according to which, if any, of the classes of bloom it exhibited. Environmental processes most responsible for the variability in pre- dicted spring blooms were the rate of phosphorus release from the sediment, the timing of nitrate depletion at the seaward boundary, and the maximal nitrate concentration at the boundary. Simulation of bloom failure was most strongly associated with low rates of phosphorus release from the sediment. Phytoplankton physiological parameters most responsible for variability in categories of behavior were the nitrogen:chlorophyll conversion factor, and the maximum internal storage capaaty for phosphorus. Blooms of extraordinary magnitude were associated with low nitrogen:chlorophyll ratios and high stor- age capacity for phosphorus. Results help identify key processes and parameters In need of further understanding as well as possible points for managerial intervention.

Interannual variability in spring bloom timing and magnitude in the Rhode River, Maryland, USA:observations and modeling