Abstract-Applications of transport time scales are pervasive in biological, hydrologic, and geochemical studies yet these times scales are not consistently defined and applied with rigor in the literature. We compare three transport time scales (flushing time, age, and residence time) commonly used to measure the retention of water or scalar quantities transported with water. We identify the underlying assumptions associated with each time scale, describe procedures for computing these time scales in idealized cases, and identify pitfalls when real-world systems deviate from these idealizations. We then apply the time scale definitions to a shallow 378 ha tidal lake to illustrate how deviations between real water bodies and the idealized examples can result from: (1) non-steady flow; (2) spatial variability in bathymetry, circulation, and transport time scales; and (3) tides that introduce complexities not accounted for in the idealized cases. These examples illustrate that no single transport time scale is valid for all time periods, locations, and constituents, and no one time scale describes all transport processes. We encourage aquatic scientists to rigorously define the transport time scale when it is applied, identify the underlying assumptions in the application of that concept, and ask if those assumptions are valid in the application of that approach for computing transport time scales in real systems.In aquatic systems, most of the living biomass and masses of nutrients, contaminants, dissolved gases, and suspended particles are carried in a fluid medium, so it is essential to understand hydrodynamic processes that transport water and its constituents. A first-order description of transport is expressed as ''residence time'' or ''flushing time,'' which we conceive as measures of water-mass retention within defined boundaries. Aquatic scientists often estimate retention time and compare it to time scales of inputs or biogeochemical processes to calculate mass balances or understand dynamics of populations and chemical properties. Boynton et al. (1995) argue that residence time is such an important attribute that it should be the basis for comparative analyses of ecosystem-scale nutrient budgets.The classical empirical model of lake eutrophication (Vollenweider 1976) describes algal biomass as a function of phosphorus loading rate scaled by the hydraulic residence time. Since Vollenweider's recognition that the biogeochemical processing of phosphorus varies with residence time, variable water retention or flushing has been used to describe variability of lake thermal stratification (Hamilton and Lewis 1987), isotopic composition (Herczeg and Imboden 1988), alkalinity (Eshleman and Hemond 1988), dissolved organic carbon concentration (Christensen et al. 1996), elemental ratios of heavy metals (Hilton et al. 1995) and nutrients (Hecky et al. 1993), mineralization rates of organic matter (den Heyer and Kalff 1998), and primary production (Jassby et al. 1990). The mechanistic explanation of low plankton abun...

Transport time scales such as flushing time and residence time are often used to explain variability in phytoplankton biomass. In many cases, empirical data are consistent with a positive phytoplankton-transport time relationship (i.e., phytoplankton biomass increases as transport time increases). However, negative relationships, varying relationships, or no significant relationship may also be observed. We present a simple conceptual model, in both mathematical and graphical form, to help explain why phytoplankton may have a range of relationships with transport time, and we apply it to several real systems. The phytoplankton growth-loss balance determines whether phytoplankton biomass increases with, decreases with, or is insensitive to transport time. If algal growth is faster than loss (e.g., grazing, sedimentation), then phytoplankton biomass increases with increasing transport time. If loss is faster than growth, phytoplankton biomass decreases with increasing transport time. If growth and loss are approximately balanced, then phytoplankton biomass is relatively insensitive to transport time. In analyses of several systems, portions of an individual system, or time periods, apparent insensitivity of phytoplankton biomass to changes in transport time could arise due to the superposition of cases with different phytoplankton-transport time relationships. Thus, in order to understand or predict responses of phytoplankton biomass to changes in transport time, the relative rates of algal growth and loss must be known.Aquatic scientists and resource managers commonly invoke time for transport through a surface water body to help explain variability in phytoplankton biomass, often seeking empirical relationships between phytoplankton and transport time scales such as flushing time and residence time to characterize that variability. A positive phytoplankton-transport time (P-T) relationship suggests that as transport time increases (or decreases), so does phytoplankton biomass or production. Observations consistent with a positive P-T relationship are frequently made in rivers and lakes

A presumed value of shallow-habitat enhanced pelagic productivity derives from the principle that in nutrient-rich aquatic systems phytoplankton growth rate is controlled by light availability, which varies inversely with habitat depth. We measured a set of biological indicators across the gradient of habitat depth within the Sacramento-San Joaquin River Delta (California) to test the hypothesis that plankton biomass, production, and pelagic energy flow also vary systematically with habitat depth. Results showed that phytoplankton biomass and production were only weakly related to phytoplankton growth rates whereas other processes (transport, consumption) were important controls. Distribution of the invasive clam Corbicula fluminea was patchy, and heavily colonized habitats all supported low phytoplankton biomass and production and functioned as food sinks. Surplus primary production in shallow, uncolonized habitats provided potential subsidies to neighboring recipient habitats. Zooplankton in deeper habitats, where grazing exceeded phytoplankton production, were likely supported by significant fluxes of phytoplankton biomass from connected donor habitats. Our results provide three important lessons for ecosystem science: (a) in the absence of process measurements, derived indices provide valuable information to improve our mechanistic understanding of ecosystem function and to benefit adaptive management strategies; (b) the benefits of some ecosystem functions are displaced by water movements, so the value of individual habitat types can only be revealed through a regional perspective that includes connectedness among habitats; and (c) invasive species can act as overriding controls of habitat function, adding to the uncertainty of management outcomes.

A central challenge of coastal ecology is sorting out the interacting spatial and temporal components of environmental variability that combine to drive changes in phytoplankton biomass. For 2 decades, we have combined sustained observation and experimentation in South San Francisco Bay (SSFB) with numerical modeling analyses to search for general principles that define phytoplankton population responses to physical dynamics characteristic of shallow, nutrient-rich coastal waters having complex bathymetry and influenced by tides, wind and river flow. This study is the latest contribution where we investigate light-limited phytoplankton growth using a numerical model, by modeling turbidity as a function of suspended sediment concentrations (SSC). The goal was to explore the sensitivity of estuarine phytoplankton dynamics to spatial and temporal variations in turbidity, and to synthesize outcomes of simulation experiments into a new conceptual framework for defining the combinations of physical-biological forcings that promote or preclude development of phytoplankton blooms in coastal ecosystems. The 3 main conclusions of this study are: (1) The timing of the wind with semidiurnal tides and the spring-neap cycle can significantly enhance springneap variability in turbidity and phytoplankton biomass; (2) Fetch is a significant factor potentially affecting phytoplankton dynamics by enhancing and/or creating spatial variability in turbidity; and (3) It is possible to parameterize the combined effect of the processes influencing turbidity -and thus affecting potential phytoplankton bloom development -with 2 indices for vertical and horizontal clearing of the water column. Our conceptual framework is built around these 2 indices, providing a means to determine under what conditions a phytoplankton bloom can occur, and whether a potential bloom is only locally supported or system-wide in scale. This conceptual framework provides a tool for exploring the inherent light climate attributes of shallow estuarine ecosystems and helps determine susceptibility to the harmful effects of nutrient enrichment.

In this paper we use numerical models of coupled biological-hydrody~~arnic processes to search for general principles of bloom regulation in estuarine waters. We address three questions: What are the dynamics of slratificalion in coasial systems a\ influenced by cariable freshwater input and tidal stirring'? How does phytoplankton growth respond to these dynamics? Can the classical Sverdrup Critical Depth Model (SCDM) be used to predict the tinling of bloom events in shallow coastal domains such as estuaries? We present results of simulation experiments which assume that vertical transport and net phytoplankton growth ratcs arc horizontally homogcncous. In the present approach the temporally and spatially varying turbulent diffusivities for various stratification scenarios are calculated using a hydrodynamic code that includes the Mellor-Yarnada 2.5 turbulence closure model. These difl'usivi