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Modeling blue-green algal blooms in the lower neuse river
Wat. Res. Vol. 22, No. 7, pp. 895-905, 1988 Printed in Great Britain. All fights reserved
0043-1354/88 $3.00+0.00
Copyright © 1988PergamonPress plc
MODELING BLUE-GREEN ALGAL BLOOMS IN THE LOWER NEUSE RIVER Wu-SENG LUNG1 and HANS W. PAERL2 tDepartment of Civil Engineering, University of Virginia, Charlottesville, VA 22903 and 2Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City, NC 27514, U.S.A. (Received January 1987; accepted in revisedform January 1988) Abstract--Over the past decade, segments of the lower Neuse River between Kinston and New Bern, N.C. revealed alarming symptoms of advanced eutrophication, culminating in the appearance and persistence of nuisance blue-green algal blooms. A mathematical model of the lower Neuse River has been developed to help evaluate control alternatives. The model includes differentiation between four phytoplankton groups: diatoms, green algae and blue--green algae (nitrogen fixing and non-nitrogen fixing). Other water quality constituents modeled included nitrogen and phosphorus components and dissolved oxygen. The water column is partitioned into two layers and the associated mass transport pattern is incorporated. The model is able to mimic the seasonal trends of algal biomass, nutrient and dissolved oxygen levels observed in 1983, 1984 and 1985, each year having a different hydrologic pattern. Comparisons of model calibration results suggest that freshwater flow and associated mixing conditions in the river play a major role in initiating and maintaining blue-green blooms when sufficient nutrient supplies are present. Key words--blue-green algae, eutrophication, estuary, multiple phytoplankton groups
INTRODUCTION
The commercially and recreationally important Neuse River drains approx. 25% of North Carolina's land area, flowing through the Piedmont and Coastal Plain regions (Fig. 1). The river's basin supports urban centers (Hillsborough, Raleigh-Durham, Goldsboro, Kinston, New Bern), agriculture and industry. The past decade has witnessed an alarming rate of eutrophication accompanying accelerated nutrient inputs in the basin (Tedder et al., 1980; Paerl, 1987). The most ominous symptoms of enhanced eutrophication are spring-summer-fall blooms of nuisance blue-green algal genera (Microcystis, Oscillatoria, Anabaena and Aphanizomen) which at times can coat the river with green paint-like scums. On average, such blooms can be expected at a frequency of once every 2 or 3 years. Years exhibiting prolonged (1-2 months) low summer flow rates following high spring runoff periods have supported maximal bloom activity over the past decade (Paerl, 1987). Blooms are often persistent. During 1983, for example, much of the lower Neuse River was plagued with Microcystis blooms over a 4-month (June-September) period. Blue-green algal blooms of this magnitude and duration can lead to radical and long lasting alterations in water quality (Reynolds and Walsby, 1975) and trophic (food chain) characteristics (Porter and Orcutt, 1980). Periods of complete dissolved oxygen depletion (anoxia) in river bottom waters and sediments are known to exist under surface blooms in stagnant sections of the Neuse River (Paerl, 1987), leaving such portions of the water column and sediments uninhabitable for desirable fauna and flora.
Blue-green algal bloom species are generally of poor food value for herbivorous zooplankton and larval fish (Porter and Orcutt, 1980; Fulton and Paerl, 1987), leading to both a lack of effective utilization of blue--green algal biomass and to alterations at the grazer (and possibly higher consumer) level of aquatic food chains (Fogg, 1969; Porter and Orcutt, 1980). Toxic effects, including death, have been reported among inhabitants and consumers (including cattle, domestic pets and man) of waters affected by nuisance blooms (Gentile, 1971; Gorham and Carmichael, 1979). Lastly, foul odors, tastes and undersirable aesthetic values are common results of blooms (Fog,g, 1969; Paerl, 1987). Recent conditions on the Neuse River have included most of these forms of water quality deterioriation (Paerl, 1987). Questions arising during consideration of management options have included: (1) What are the factors and their interrelationships that cause blue-green algal blooms in the lower Neuse River? (2) Would major reductions of nutrient inputs (either from point or nonpoint sources or both) to the lower Neuse River help to arrest the occurrence and persistence of nuisance blue-green algal blooms? (3) If so, what magnitude of nutrient input cutbacks would be required to control and ultimately eliminate the nuisance blue-green algal blooms? (4) What other management options have potential for controlling the blooms? To help address these questions, a mathematical model of the lower Neuse River has been developed. The modeling effort focuses on a better under895
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Fig. 1. Neuse River watershed and study area. standing of the mechanisms intitiating and sustaining algal blooms in the lower Neuse River. This paper presents the model development and model calibration results.
MODELDESIGN The following features are included in the model: (1) Multiple functional groups of phytoplankton (diatoms, green algae, non-nitrogen fixing and nitrogen fixing blue-green algae). (2) Two-layer mass transport in tidal and estuarine portions of the lower Neuse River to characterize the surface-dwelling blue-greens in the surface layer. (3) Time-variable simulation (tidally averaged) of seasonal phytoplankton and nutrient dynamics, incoporating seasonal variations of mass transport. Like many estuarine eutrophication models (Lung, 1986; HydroQual Inc., 1987; Thomann and Fitzpatrick, 1982), the Neuse Estuary eutrophication model is based on the principle of conservation of mass. The modeling framework developed in this study is made up of three components--transport due to freshwater flow and dispersion, the kinetic interaction between variables and external inputs. Descriptions of these components are presented below.
Model segmentation and mass transport
The study area from Kinston to New Bern (the portion of the river exhibiting repeated blooms) was divided into 18 segments, the first 8 being the riverine portion of the model, segments 9-13 being the surface layer and segments 14-18 the bottom layer of the tidal portion of the river [Fig. 2(a)]. Each segment was considered completely mixed. Figure 2(a) shows such a two-layer mass transport pattern in the estufine portion on a tidally averaged basis. The upstream flows in the bottom layer occurred during the summers of 1983 and 1985 when low freshwater flows caused salinity intrusions. In 1984 an upstream flow pattern was not necessary for the model because the increased flow in the summer pushed salinity downstream of the study area, thereby eliminating the saline bottom layer. Model variables and kinetics
Eleven state variables are incorporated into the modeling framework [Fig. 2(b)]. They were chosen to characterize the seasonal phytoplankton and nutrient dynamics with the context of available water quality data. Principal kinetic interactions for nutrient cycles, dissolved oxygen and four algal functional groups are also incorporated in the model. Since a complete description of the model kinetics can be found in another document (Lung and Paerl, 1986), only the
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Fig. 2. Neuse River Estuary water quality model. salient features of the model's kinetics are described in the following paragraphs. The kinetics are similar to those in the multi-class phytoplankton model for the Saginaw Bay (Bierman and Doland, 1986). Orthophosphate is utilized by algae for growth. Phosphorus is recycled from the phytoplankton biomass pool to organic phosphorus and orthophosphate through non-predatory mortality. Organic phosphorus is converted to orthophosphate via microbial-mineralization and hydrolysis at a temperature dependent rate. Zooplankton populations encountered during this study were very low in biomass and hence contributed little to nutrient recycling (Paerl et al., 1984; Fulton and Paerl, 1988). Ammonia and nitrate are used by phytoplankton for growth. Nitrogen is recycled from algal biomass and generally follows pathways similar to phosphorus. Organic nitrogen is converted to ammonia via hydrolysis and mineralization at a temperature dependent rate, while ammonia is converted to nitrate (nitrification) at a temperature dependent rate.
Dissolved oxygen is coupled to several other system variables. The sources of oxygen considered included physical reaeration and evolution by phytoplankton photosynthetic production. Sinks of dissolved oxygen are algal and bacterial respiration; oxidation of detrital carbon, nitrogen, phosphorus and carbonaceous material from waste effluents and nonpoint discharges; as well as sediment oxygen demands. The reaeration process is formulated in such a way that during dissolved oxygen supersaturation periods, oxygen can be lost to the air. Algal growth and death kinetics are formulated individually for each algal group. Growth rates are directly related to temperature from data available for temperate climates. Differences in the way temperature influences growth rates among the different algal groups as determined by Auer and Canale (1980) and Canale and Vogel (1974) were used for the model. Phytoplankton growth rates are also dependent on light intensity. Growth rates increase up to a satur-
Wu-SENG LUNG and HANSW. PAERL
898
ating condition; greater light intensities may lead to decreases in growth rates. To account for variations in light energy available to phytoplankton due to depth and time of day, such effects on algal growth rates are incorporated into the model (Di Toro et al., 1971). Growth rates are also a function of nutrient concentrations up to saturation. Such a relationship is described by a Michaelis-Menton kinetics (Di Toro et al., 1971). When both nitrogen and phosphorus are utilized, growth rates are assumed, as they are in this work, to be proportional to the product of the Michaelis-Menton expressions for each of the nutrients (U.S. EPA, 1985). The Michaelis-Menton constant values usually range from 5 to 25/~gl ~ for nitrogen and from 1 to 5 # g l ~ for phosphorus, depending on species. In the model, different Michaelis-Menton constant values are assigned to different phytoplankton functional groups. There is a general consensus that most freshwater algal species exhibit decreased viability when encountering saline waters (Paerl et al., t984; Morris et al., 1982; Sharp et al., 1982; Pennock, 1983). In the model, salinity effect is quantified as follows: growth rates are not affected by salinity until the salinity level in the water column rises to 1 part per thousand (ppt); when the salinity reaches 2 ppt, growth rates are reduced to 40% of the normal growth rate in freshwater. These threshold salinity values are based on observations showing a narrow range of salinity tolerance for freshwater algal species (Filardo, 1984). Decreases in algal biomass concentrations in the water column are brought about through four processes: algal respiration, death, grazing and algal settling. Algal death includes grazing by zooplankton and cell destruction through bacterial attack, disease, physical damage, seasonal mortality following climate and nutrition changes or other mechanisms. Settling rates of algae are specified in units of m d l, and are internally converted to d -~ units on a segment by segment basis, according to segment depth.
MODEL CALIBRATION
Calibration procedure
The first step in model calibration is assigning appropriate values of transport and reaction coefficients to the model. These coefficients may be determined from a fundamental analysis relating to the specific processes, as may be accomplished in the case of the hydrologic or hydrodynamic terms. Thus, the two-layer transport pattern in the lower Neuse River attributable to density driven currents is derived analytically from hydrodynamic principles as described in the next paragraph. Other model coefficients are reported in the literature, for instance, biological and chemical kinetic terms. In any case, assuming a range of these kinetic coefficient values is
known, a best estimate is made of each, the model is run and the output is compared to the data. Invariably, successive adjustments are required to obtain a "reasonable fit" of the model and data (O'Connor et al., 1975). Many algal growth kinetic coefficients, nutrient limitation formulations and algal biomass stoichiometry constants have been well documented (U.S. EPA, 1985). Further, more narrow ranges of kinetic coefficients are available from recent eutrophication modeling studies of the Potomac Estuary (Thomann and Fitzpatrick, 1982; Thomann et al., 1985), the James Estuary (Lung, 1986; HydroQual Inc., 1981) and the Chesapeake Bay (HydroQual Inc., 1987). As a result, relatively narrow ranges of kinetic coefficients and stoichiometry constants considerably expedite the calibration process for this model and generate a unique set of model coefficients and constants. Derivation o f model input
During the summer of 1983 and part of the summer in 1985, freshwater flows in the lower Neuse River were relatively low, resulting in salinity intrusions into the study area. A simplified method developed by Lung and O'Connor (1984) was employed to derive the time variable, two-layer transport patterns [Fig. 2(a)] to be incorporated into the model using data on freshwater flows (from the U.S. Geological Survey surface water records) and the salinity distribution of the lower Neuse River. The derived mass transport patterns were eventually validated by reproducing the salinity distribution in the lower Neuse River on a time-variable basis. Such an effort was not needed for the 1984 calibration as salinity did not reach the study area in that year. The model coefficients associated with algal growth were derived using data obtained by the Institute of Marine Sciences, University of North Carolina. Measured temperatures were averaged for each layer. Surface light intensity was obtained from the field data. The photoperiod, as a function of time of the year, was derived from climatological data at Kinston. Measurements of light intensity at different depths in the water column were used to determine light extinction coefficients. The light extinction coefficient was modified to account for the selfshading effects of the algal biomass. The resulting value was entered into the model as the light extinction coefficient due to detrital materials. Total light extinction for diatoms and green algae also included the shading effect owing to the blue-green algae, which periodically formed surface dwelling blooms. Other model parameters and coefficients related to algal growth and nutrient interactions were derived from laboratory and field empirical estimates reported in the literature (U.S. EPA, 1985). The values used in the Neuse River model are summarized in Table 1. Values used in recent modeling studies on other estuarine systems are also presented in the table for comparison.
Modeling blue-green algal blooms Table Parameter
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Neuse Estuary§ 350 (Diatoms, greens) 250 (blue-greens) 2.0 (diatoms) 1.8 (greens) 1.3 (blue-greens) 0.05 0.05 1.50 (diatoms, greens) 0.05(non-N fixing blue-greens) 0.19 (N fixing blue-greens) 5.0 25 (diatoms, greens) 15 (non-N fixing blue-greens) 0 (N fixing blue-greens) 50 7 1 133 0.1 0.1 0.05
*HydroQual Inc. (1987). fThomann and Fitzpatrick (1982). ~:Lung (1986). §This study (Lung and Paerl, 1986).
Finally, water quality data from North Carolina Division of Environmental Management (NCDEM) were used to develop the upstream boundary conditions at Kinston and downstream boundary conditions at New Bern. In addition, tributary input from two tributaries, Contentnea Creek and Swift Creek, and wastewater loading rates for the Weyerhauser paper mill in Vanceboro were also derived from NCDEM data and incorporated into the model.
period, ammonia and nitrate levels were significantly reduced by the non-nitrogen fixing blue-greens. While seasonal variations of dissolved oxygen are reproduced by the model, daily fluctuations reflected in the data are not expected to be matched by the model. The two-layer mass transport pattern reproduces temporal and spatial salinity distributions very well, suggesting that the mass transport pattern employed is valid.
Model calibration of 1983 data
Model calibration of 1984 and 1985 data
Figure 3 shows the comparison between model results and measured chlorophyll a levels in four algal groups in 1983 for the surface layer of the water column at four locations in the blooms area. The blue-green algal blooms, more or less dominated by the non-nitrogen fixing blue-green algae, are closely mimicked by the model in terms of their seasonal trends and magnitudes of biomass levels. During blue-green algal blooms, diatoms do not thrive, most likely because salinity reduces their growth rates and the surface dwelling blue-green algae reduce the amount of light penetrating the water column. In Fig. 4, a comparison between the observed data and model results is presented for other key water quality constituents: NH~, N O ~ - + N O ; , orthophosphate, salinity, chlorophyll a (total) and dissolved oxygen in the surface layer at five different locations between Kinston and New Bern. The modeling framework reproduces seasonal trends of algal growth and nutrient dynamics. Both model results and data indicate the orthophosphate was present in sufficient supplies for phytoplankton growth throughout the year. Nitrogen supply (NH~" and NO2 + N O ; ) prior to blue-green algal blooms (before July) appeared sufficient. During the bloom
The model was subsequently tested to see how its predictions fit the 1984 data. The same kinetic constant and coefficient values used in the 1983 model analysis were used for the 1984 analysis. Only exogenous variables such as river flow, light extinction coefficient, average daily surface light intensity, temperature and the mass transport patterns were changed in accordance to 1984 conditions. The results of the model analysis are presented in Fig. 5. In general, the model results match the observed data very well. The results of model analyses are encouraging, since the 1984 hydrologic pattern was quite different than that exhibited in 1983. Although data are not available to partition phytoplankton into the four functional groups, no blue-green algal blooms were observed during the 1984 field sampling program (in fact blue-green algae were virtually absent in 1984) while nutrient concentrations in 1984 were similar to the 1983 conditions. Figure 6 shows that the model is able to mimic the 1985 conditions very well using the same kinetic coefficient values. Again, algal enumerations were not made in 1985, but no blooms were observed. The calibration analyses for 1984 and 1985 data further substantiate the validity of the modeling framework. Additional insights on
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The Neuse Eutrophication Model has been calibrated using 3 years of data (1983, 1984 and 1985), to substantiate the validity of the modeling framework. Independent estimates of the exogenous variables such as freshwater flows, preliminary mass transport patterns, boundary conditions and environmental conditions have been derived from the data to minimize uncertainties. Model kinetic coefficients derived from literature values from other studies have been validated via the model calibration process. It should be pointed out that model calculations are not intended to "curve fit" the data. Rather, the calibrated coefficients (e.g. algal growth kinetics) are obtained through a series of model sensitivity runs with reasonable and narrow ranges of their values derived from the literature and other estuaires modeling studies. The modeling framework is designed to mimic the seasonal trends of algal growth and nutrient dynamics and has been shown to accomplish the
task, particularly reproducing the seasonal trend of blue-green algal blooms. In model sensitivity analyses, adjusting the kinetic coefficients and constants (within their narrow ranges) to improve the calibration of a certain water quality constituents often resulted in adverse outcome of matching other water quality constituents. These were the constraints in the model calibration process which eventually led to the derivation of a unique set of model coefficients. Finally, model accuracy can further be tested by post auditing the model following the implementation of control measures (Thomann, 1982, 1987; Thomann et al., 1985). FACTORS AFFECTING BLUE-GREEN
ALGAL BLOOMS
The results from model calibration runs can be examined to provide insight into the potential of blooms. Factors affecting blue-green algal blooms in the lower Neuse River in 1983, 1984 and 1985 are summarized along with model results in Fig. 7. First, upstream boundary conditions as entered into the model are presented. They include daily average flows
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and nutrient supply (NH~, NOi- + N O ; , and orthophosphate concentrations) at Kinston on a timevariable basis covering these 3 years. Temperature (in surface segments) and light extinction coefficients "n the water column are shown in Fig. 7. Light extinction coefficients are due to detrital materials only. Additional attenuation of light due to algal selfshading is added in the model using calculated phytoplankton biomass to establish total light extinction in the water column. Finally, time-variable growth rates of the blue-green algae at New Bern affected by
temperature, light, nutrient and salinity conditions in the water column are shown in Fig. 7. These are model calculated growth rates which can be related to model calibration results shown in Figs 3-6. River flow is one of the key factors determining the establishment and maintenance of a blue-green algal bloom (Paerl, 1987). Christian et el. (1986) studied an empirical relationship between river flow and Microcystis blooms in the Neuse River. In this study, the effect of flow was dearly demonstrated in 1983 when the bloom period was distinguished by low
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blue-green algae; and the effect of salinity on algal growth. In addition, each of the two layers in the water column is further divided into five longitudinal segments to account for the longitudinal concentration gradients of the water quality constituents. Water quality constituents simulated by the model are individual chlorophyll a concentrations associated with the four algal groups, organic nitrogen, ammonia nitrogen, nitrite and nitrate nitrogen, organic phosphorus, orthophosphate, dissolved oxygen and salinity. Biochemical, biological and chemical interactions between the water quality constituents are incorporated into the model to quantify phytoplankton growth and death, algal species competition, nutrient uptake and recycling and photo-
Modeling blue-green algal blooms synthetic reproduction and respiration of oxygen by algae. A large data base consisting of water quality data in 1983, 1984 and 1985 was used for model development, calibration and verification. The modeling framework is able to mimic the seasonal trend of phytoplankton nutrient dynamics in the system. The model results confirm a hypothesis that initiation of the blue-green algal blooms in the lower Neuse River is strongly regulated by summer river flow under present excessive (for growth) nutrient conditions. While post auditing o f the model following the implementation of eutrophication control measures is necessary to substantiate its predictive ability, the Neuse Eutrophication Model can be used as a planning tool to examine the spectrum of responses of the system which may occur under various planning alternatives, including nutrient control and flow control. The water quality trends and relative impacts associated with control alternatives, rather than actual predictions, should be used to address the management questions outlined in the beginning of the paper. Acknowledgements--The authors are grateful to the Soap and Detergent Association for their support for this modeling study. Funding for field data collection, laboratory nutrient and chlorophyll a determinations was provided by the University of North Carolina Water Resources Research Institute, Project No. 70025 and North Carolina Sea Grant, Projects R/MER-I and R/MER-5. REFERENCES
Auer M. T. and Canale R. P. (1980) Phosphorus uptake dynamics as related to mathematical modeling at a site on Lake Huron. J. Great Lakes Res. 6, 1-7. Bierman V. J. and Doland D. M. (1986) Modeling of phytoplankton in Saginaw Bay: I. Calibration phase. J. envir. Engng Div. Am. Soc. civ. Engrs 112, 400-414. Canale R. P. and Vogel A. H. (1974) Effect of temperature on phytoplankton growth. J. envir. Engng Div. Am. Soc. civ. Engrs 100, 231-241. Christian R. R., Bryant W. L. and Stanley D. W. (1986) The relationship between river flow and Microcystis aeruginosa blooms in the Neuse River, North Carolina. Water Resources Research Institute of the University of North Carolina Report No. 223. Di Toro D. M., O'Connor D. J. and Thomann R. V. (1971) A dynamic model of the phytoplankton population in the Sacramento-San Joaquin Delta. Adv. Chem. Ser. American Chemical Society 106, 131-180. Filardo M. J. (1984) Phytoplankton ecology and dynamics in the James River Estuary, Virginia, U.S.A. Ph.D. dissertation submitted to the Old Dominion University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy. Fogg G. E. (1969) The physiology of an algal Nuisance. Proc. R. Soc. B. 173, 175-189. Fulton R. S. III and Paerl H. W. (1987) Effects of colonial morphology o n z o o p l a n k t o n utilization of algal resources during blue-green algal (Microcystis aeruginosa) blooms. LimnoL Oceanogr. 32, 634-644. Gentile J. M. (1971) Blue-grcen and green algal toxins. In Algal Toxins (Edited by Kadis S. and Ciegler A.), pp. 27-66. Academic Press, New York. Gorham P. R. and Carmichael W. W. (1979) Phytotoxins from blue-green algae. Pure appl. Chem. 52, 165-174.
905
HydroQual Inc. (1981) Water quality analysis of the Patuxent River. Report prepared for the State of Maryland. HydroQual Inc. (1987) Development of a coupled hydrodynamic/water quality model of the eutrophication and anoxia processes of the Chesapeake Bay. Report submitted to the U.S. EPA. Lung W. S. (1986) Assessing phosphorus control in the James River Basin. J. envir. Engng Div. Am. Soc. civ. Engrs 112, 44-60. Lung W. S. and O'Connor D. J. (1984) Two-dimensional mass transport in estuaries. J. Hydraul. Engng Div. Am. Soc. civ. Engrs 110, 1340-1357. Lung W. S. and Paerl H. W. (1986) Modeling the blue-green algal bloom in the Neuse River Estuary. Department of Civil Engineering Report No. UVA/-53106/CE86/101. Morris A. W., Bale A. J. and Howland R. J. M. (1982) Chemical variability in the Tamar Estuary south-west England. Estuarine Coast. Shelf Sci. 14, 649~61. O'Connor D. J. and Lung W. S. (1981) Suspended solids analysis of estuarine systems. J. envir. Engng Div. Am. Soc. cir. Engrs 107, 101-120. O'Connor D. J., Di Toro D. M. and Thomann R. V. (1975) Phytoplankton models and eutrophication problems. In Ecological Modeling (Edited by Russell C. S.), pp. 149-208. Resources for the Future Inc., Washington, D.C. Paerl H. W. (1987) Dynamics of blue-green algal (Microcyst& aeruginosa) blooms in the lower Neuse River, North Carolina: causative factors and potential controls. University of North Carolina Water Resources Research Institute Report UNC-WRRI-87-229. Paerl H. W., Bland P. T., Blackwell J. H. and Bowles N. D. (1984) The effect on the potential of blue-green algal (Microcystis aeruginosa) bloom in the Neuse River Estuary, N.C. North Carolina Sea Grant Working Paper 84-I. Pennock J. R. (1983) Regulation of chlorophyll distribution in the Delaware Estuary by short term variability in vertical stratification and suspended sediment concentration. EOS Trans. Am. geophys. Union 64, 1041. Porter K. G. and Orcutt T. (1980) Nutritional adequacy, manageability, and toxicity as factors that determine the food quality of green and blue-green algae for Daphnia. In Special Symp. IlL Am. Soc. Limnol. Oceanogr. The Evolution and Ecology of Zooplankton (Edited by Kerfoot W. C.), pp. 269-281. Reynolds C. S. and Walsby A. E. (1975) Water blooms. Biol. Rev. 50, 437-481. Sharp J. H., Sulberson C. H. and Church T. M. (1982) The chemistry of the Delaware Estuary. General considerations. Limnol. Oceanogr. 27, 1015-1028. Tedder S. W., Sauber J., Ausley J. and Mitchell S. (1980) Working paper: Neuse River Investigations 1979. Division of Envir. Management, N.C., Dept. of Natural Resources and Community Development, Raleigh, N.C. Thomann R. V. (1982) Verification of water quality models. J. envir. Engng Div. Am. Soc. cir. Engrs 108, 923-940. Thomann R. V. (1987) System analysis in water quality management--a 25 year retrospect. In System Analysis in Water Quality Management (Edited by Beck M. B.), pp. 1-14. Pergamon Press, Oxford. Thomann R. V. and Fitzpatrick J. J. (1982) Calibration and verification of a mathematical model of the eutrophication of the Potomac Estuary. Prepared by HydroQual Inc. for the Government of the District of Columbia. Thomann R. V., Jaworski N. J., Nixon S. W., Paerl H. W. and Taft J. (1985) The 1983 algal bloom in the Potomac Estuary. Report prepared for the Potomac Strategy State/EPA Management Committee. U.S. EPA (1985) Rates, Constants, and Kinetics Formulations in Surface Water-Quality Modeling, 2nd Edition. EPA/600/3-85/040.
0043-1354/88 $3.00+0.00
Copyright © 1988PergamonPress plc
MODELING BLUE-GREEN ALGAL BLOOMS IN THE LOWER NEUSE RIVER Wu-SENG LUNG1 and HANS W. PAERL2 tDepartment of Civil Engineering, University of Virginia, Charlottesville, VA 22903 and 2Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City, NC 27514, U.S.A. (Received January 1987; accepted in revisedform January 1988) Abstract--Over the past decade, segments of the lower Neuse River between Kinston and New Bern, N.C. revealed alarming symptoms of advanced eutrophication, culminating in the appearance and persistence of nuisance blue-green algal blooms. A mathematical model of the lower Neuse River has been developed to help evaluate control alternatives. The model includes differentiation between four phytoplankton groups: diatoms, green algae and blue--green algae (nitrogen fixing and non-nitrogen fixing). Other water quality constituents modeled included nitrogen and phosphorus components and dissolved oxygen. The water column is partitioned into two layers and the associated mass transport pattern is incorporated. The model is able to mimic the seasonal trends of algal biomass, nutrient and dissolved oxygen levels observed in 1983, 1984 and 1985, each year having a different hydrologic pattern. Comparisons of model calibration results suggest that freshwater flow and associated mixing conditions in the river play a major role in initiating and maintaining blue-green blooms when sufficient nutrient supplies are present. Key words--blue-green algae, eutrophication, estuary, multiple phytoplankton groups
INTRODUCTION
The commercially and recreationally important Neuse River drains approx. 25% of North Carolina's land area, flowing through the Piedmont and Coastal Plain regions (Fig. 1). The river's basin supports urban centers (Hillsborough, Raleigh-Durham, Goldsboro, Kinston, New Bern), agriculture and industry. The past decade has witnessed an alarming rate of eutrophication accompanying accelerated nutrient inputs in the basin (Tedder et al., 1980; Paerl, 1987). The most ominous symptoms of enhanced eutrophication are spring-summer-fall blooms of nuisance blue-green algal genera (Microcystis, Oscillatoria, Anabaena and Aphanizomen) which at times can coat the river with green paint-like scums. On average, such blooms can be expected at a frequency of once every 2 or 3 years. Years exhibiting prolonged (1-2 months) low summer flow rates following high spring runoff periods have supported maximal bloom activity over the past decade (Paerl, 1987). Blooms are often persistent. During 1983, for example, much of the lower Neuse River was plagued with Microcystis blooms over a 4-month (June-September) period. Blue-green algal blooms of this magnitude and duration can lead to radical and long lasting alterations in water quality (Reynolds and Walsby, 1975) and trophic (food chain) characteristics (Porter and Orcutt, 1980). Periods of complete dissolved oxygen depletion (anoxia) in river bottom waters and sediments are known to exist under surface blooms in stagnant sections of the Neuse River (Paerl, 1987), leaving such portions of the water column and sediments uninhabitable for desirable fauna and flora.
Blue-green algal bloom species are generally of poor food value for herbivorous zooplankton and larval fish (Porter and Orcutt, 1980; Fulton and Paerl, 1987), leading to both a lack of effective utilization of blue--green algal biomass and to alterations at the grazer (and possibly higher consumer) level of aquatic food chains (Fogg, 1969; Porter and Orcutt, 1980). Toxic effects, including death, have been reported among inhabitants and consumers (including cattle, domestic pets and man) of waters affected by nuisance blooms (Gentile, 1971; Gorham and Carmichael, 1979). Lastly, foul odors, tastes and undersirable aesthetic values are common results of blooms (Fog,g, 1969; Paerl, 1987). Recent conditions on the Neuse River have included most of these forms of water quality deterioriation (Paerl, 1987). Questions arising during consideration of management options have included: (1) What are the factors and their interrelationships that cause blue-green algal blooms in the lower Neuse River? (2) Would major reductions of nutrient inputs (either from point or nonpoint sources or both) to the lower Neuse River help to arrest the occurrence and persistence of nuisance blue-green algal blooms? (3) If so, what magnitude of nutrient input cutbacks would be required to control and ultimately eliminate the nuisance blue-green algal blooms? (4) What other management options have potential for controlling the blooms? To help address these questions, a mathematical model of the lower Neuse River has been developed. The modeling effort focuses on a better under895
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MODELDESIGN The following features are included in the model: (1) Multiple functional groups of phytoplankton (diatoms, green algae, non-nitrogen fixing and nitrogen fixing blue-green algae). (2) Two-layer mass transport in tidal and estuarine portions of the lower Neuse River to characterize the surface-dwelling blue-greens in the surface layer. (3) Time-variable simulation (tidally averaged) of seasonal phytoplankton and nutrient dynamics, incoporating seasonal variations of mass transport. Like many estuarine eutrophication models (Lung, 1986; HydroQual Inc., 1987; Thomann and Fitzpatrick, 1982), the Neuse Estuary eutrophication model is based on the principle of conservation of mass. The modeling framework developed in this study is made up of three components--transport due to freshwater flow and dispersion, the kinetic interaction between variables and external inputs. Descriptions of these components are presented below.
Model segmentation and mass transport
The study area from Kinston to New Bern (the portion of the river exhibiting repeated blooms) was divided into 18 segments, the first 8 being the riverine portion of the model, segments 9-13 being the surface layer and segments 14-18 the bottom layer of the tidal portion of the river [Fig. 2(a)]. Each segment was considered completely mixed. Figure 2(a) shows such a two-layer mass transport pattern in the estufine portion on a tidally averaged basis. The upstream flows in the bottom layer occurred during the summers of 1983 and 1985 when low freshwater flows caused salinity intrusions. In 1984 an upstream flow pattern was not necessary for the model because the increased flow in the summer pushed salinity downstream of the study area, thereby eliminating the saline bottom layer. Model variables and kinetics
Eleven state variables are incorporated into the modeling framework [Fig. 2(b)]. They were chosen to characterize the seasonal phytoplankton and nutrient dynamics with the context of available water quality data. Principal kinetic interactions for nutrient cycles, dissolved oxygen and four algal functional groups are also incorporated in the model. Since a complete description of the model kinetics can be found in another document (Lung and Paerl, 1986), only the
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Fig. 2. Neuse River Estuary water quality model. salient features of the model's kinetics are described in the following paragraphs. The kinetics are similar to those in the multi-class phytoplankton model for the Saginaw Bay (Bierman and Doland, 1986). Orthophosphate is utilized by algae for growth. Phosphorus is recycled from the phytoplankton biomass pool to organic phosphorus and orthophosphate through non-predatory mortality. Organic phosphorus is converted to orthophosphate via microbial-mineralization and hydrolysis at a temperature dependent rate. Zooplankton populations encountered during this study were very low in biomass and hence contributed little to nutrient recycling (Paerl et al., 1984; Fulton and Paerl, 1988). Ammonia and nitrate are used by phytoplankton for growth. Nitrogen is recycled from algal biomass and generally follows pathways similar to phosphorus. Organic nitrogen is converted to ammonia via hydrolysis and mineralization at a temperature dependent rate, while ammonia is converted to nitrate (nitrification) at a temperature dependent rate.
Dissolved oxygen is coupled to several other system variables. The sources of oxygen considered included physical reaeration and evolution by phytoplankton photosynthetic production. Sinks of dissolved oxygen are algal and bacterial respiration; oxidation of detrital carbon, nitrogen, phosphorus and carbonaceous material from waste effluents and nonpoint discharges; as well as sediment oxygen demands. The reaeration process is formulated in such a way that during dissolved oxygen supersaturation periods, oxygen can be lost to the air. Algal growth and death kinetics are formulated individually for each algal group. Growth rates are directly related to temperature from data available for temperate climates. Differences in the way temperature influences growth rates among the different algal groups as determined by Auer and Canale (1980) and Canale and Vogel (1974) were used for the model. Phytoplankton growth rates are also dependent on light intensity. Growth rates increase up to a satur-
Wu-SENG LUNG and HANSW. PAERL
898
ating condition; greater light intensities may lead to decreases in growth rates. To account for variations in light energy available to phytoplankton due to depth and time of day, such effects on algal growth rates are incorporated into the model (Di Toro et al., 1971). Growth rates are also a function of nutrient concentrations up to saturation. Such a relationship is described by a Michaelis-Menton kinetics (Di Toro et al., 1971). When both nitrogen and phosphorus are utilized, growth rates are assumed, as they are in this work, to be proportional to the product of the Michaelis-Menton expressions for each of the nutrients (U.S. EPA, 1985). The Michaelis-Menton constant values usually range from 5 to 25/~gl ~ for nitrogen and from 1 to 5 # g l ~ for phosphorus, depending on species. In the model, different Michaelis-Menton constant values are assigned to different phytoplankton functional groups. There is a general consensus that most freshwater algal species exhibit decreased viability when encountering saline waters (Paerl et al., t984; Morris et al., 1982; Sharp et al., 1982; Pennock, 1983). In the model, salinity effect is quantified as follows: growth rates are not affected by salinity until the salinity level in the water column rises to 1 part per thousand (ppt); when the salinity reaches 2 ppt, growth rates are reduced to 40% of the normal growth rate in freshwater. These threshold salinity values are based on observations showing a narrow range of salinity tolerance for freshwater algal species (Filardo, 1984). Decreases in algal biomass concentrations in the water column are brought about through four processes: algal respiration, death, grazing and algal settling. Algal death includes grazing by zooplankton and cell destruction through bacterial attack, disease, physical damage, seasonal mortality following climate and nutrition changes or other mechanisms. Settling rates of algae are specified in units of m d l, and are internally converted to d -~ units on a segment by segment basis, according to segment depth.
MODEL CALIBRATION
Calibration procedure
The first step in model calibration is assigning appropriate values of transport and reaction coefficients to the model. These coefficients may be determined from a fundamental analysis relating to the specific processes, as may be accomplished in the case of the hydrologic or hydrodynamic terms. Thus, the two-layer transport pattern in the lower Neuse River attributable to density driven currents is derived analytically from hydrodynamic principles as described in the next paragraph. Other model coefficients are reported in the literature, for instance, biological and chemical kinetic terms. In any case, assuming a range of these kinetic coefficient values is
known, a best estimate is made of each, the model is run and the output is compared to the data. Invariably, successive adjustments are required to obtain a "reasonable fit" of the model and data (O'Connor et al., 1975). Many algal growth kinetic coefficients, nutrient limitation formulations and algal biomass stoichiometry constants have been well documented (U.S. EPA, 1985). Further, more narrow ranges of kinetic coefficients are available from recent eutrophication modeling studies of the Potomac Estuary (Thomann and Fitzpatrick, 1982; Thomann et al., 1985), the James Estuary (Lung, 1986; HydroQual Inc., 1981) and the Chesapeake Bay (HydroQual Inc., 1987). As a result, relatively narrow ranges of kinetic coefficients and stoichiometry constants considerably expedite the calibration process for this model and generate a unique set of model coefficients and constants. Derivation o f model input
During the summer of 1983 and part of the summer in 1985, freshwater flows in the lower Neuse River were relatively low, resulting in salinity intrusions into the study area. A simplified method developed by Lung and O'Connor (1984) was employed to derive the time variable, two-layer transport patterns [Fig. 2(a)] to be incorporated into the model using data on freshwater flows (from the U.S. Geological Survey surface water records) and the salinity distribution of the lower Neuse River. The derived mass transport patterns were eventually validated by reproducing the salinity distribution in the lower Neuse River on a time-variable basis. Such an effort was not needed for the 1984 calibration as salinity did not reach the study area in that year. The model coefficients associated with algal growth were derived using data obtained by the Institute of Marine Sciences, University of North Carolina. Measured temperatures were averaged for each layer. Surface light intensity was obtained from the field data. The photoperiod, as a function of time of the year, was derived from climatological data at Kinston. Measurements of light intensity at different depths in the water column were used to determine light extinction coefficients. The light extinction coefficient was modified to account for the selfshading effects of the algal biomass. The resulting value was entered into the model as the light extinction coefficient due to detrital materials. Total light extinction for diatoms and green algae also included the shading effect owing to the blue-green algae, which periodically formed surface dwelling blooms. Other model parameters and coefficients related to algal growth and nutrient interactions were derived from laboratory and field empirical estimates reported in the literature (U.S. EPA, 1985). The values used in the Neuse River model are summarized in Table 1. Values used in recent modeling studies on other estuarine systems are also presented in the table for comparison.
Modeling blue-green algal blooms Table Parameter
899
1. Major phytoplanktonand nutrient kinetics coefficients
Units
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Michaclis constant (P) Michaelis constant (N)
gg 1-~ gg I-i
Carbon/chlorophyll Nitrogen/chlorophyll Phosphorus/chlorophyll Oxygen/chlorophyll Organic N hydrolysis rate Organic P conversion rate Nitrification rate
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Neuse Estuary§ 350 (Diatoms, greens) 250 (blue-greens) 2.0 (diatoms) 1.8 (greens) 1.3 (blue-greens) 0.05 0.05 1.50 (diatoms, greens) 0.05(non-N fixing blue-greens) 0.19 (N fixing blue-greens) 5.0 25 (diatoms, greens) 15 (non-N fixing blue-greens) 0 (N fixing blue-greens) 50 7 1 133 0.1 0.1 0.05
*HydroQual Inc. (1987). fThomann and Fitzpatrick (1982). ~:Lung (1986). §This study (Lung and Paerl, 1986).
Finally, water quality data from North Carolina Division of Environmental Management (NCDEM) were used to develop the upstream boundary conditions at Kinston and downstream boundary conditions at New Bern. In addition, tributary input from two tributaries, Contentnea Creek and Swift Creek, and wastewater loading rates for the Weyerhauser paper mill in Vanceboro were also derived from NCDEM data and incorporated into the model.
period, ammonia and nitrate levels were significantly reduced by the non-nitrogen fixing blue-greens. While seasonal variations of dissolved oxygen are reproduced by the model, daily fluctuations reflected in the data are not expected to be matched by the model. The two-layer mass transport pattern reproduces temporal and spatial salinity distributions very well, suggesting that the mass transport pattern employed is valid.
Model calibration of 1983 data
Model calibration of 1984 and 1985 data
Figure 3 shows the comparison between model results and measured chlorophyll a levels in four algal groups in 1983 for the surface layer of the water column at four locations in the blooms area. The blue-green algal blooms, more or less dominated by the non-nitrogen fixing blue-green algae, are closely mimicked by the model in terms of their seasonal trends and magnitudes of biomass levels. During blue-green algal blooms, diatoms do not thrive, most likely because salinity reduces their growth rates and the surface dwelling blue-green algae reduce the amount of light penetrating the water column. In Fig. 4, a comparison between the observed data and model results is presented for other key water quality constituents: NH~, N O ~ - + N O ; , orthophosphate, salinity, chlorophyll a (total) and dissolved oxygen in the surface layer at five different locations between Kinston and New Bern. The modeling framework reproduces seasonal trends of algal growth and nutrient dynamics. Both model results and data indicate the orthophosphate was present in sufficient supplies for phytoplankton growth throughout the year. Nitrogen supply (NH~" and NO2 + N O ; ) prior to blue-green algal blooms (before July) appeared sufficient. During the bloom
The model was subsequently tested to see how its predictions fit the 1984 data. The same kinetic constant and coefficient values used in the 1983 model analysis were used for the 1984 analysis. Only exogenous variables such as river flow, light extinction coefficient, average daily surface light intensity, temperature and the mass transport patterns were changed in accordance to 1984 conditions. The results of the model analysis are presented in Fig. 5. In general, the model results match the observed data very well. The results of model analyses are encouraging, since the 1984 hydrologic pattern was quite different than that exhibited in 1983. Although data are not available to partition phytoplankton into the four functional groups, no blue-green algal blooms were observed during the 1984 field sampling program (in fact blue-green algae were virtually absent in 1984) while nutrient concentrations in 1984 were similar to the 1983 conditions. Figure 6 shows that the model is able to mimic the 1985 conditions very well using the same kinetic coefficient values. Again, algal enumerations were not made in 1985, but no blooms were observed. The calibration analyses for 1984 and 1985 data further substantiate the validity of the modeling framework. Additional insights on
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The Neuse Eutrophication Model has been calibrated using 3 years of data (1983, 1984 and 1985), to substantiate the validity of the modeling framework. Independent estimates of the exogenous variables such as freshwater flows, preliminary mass transport patterns, boundary conditions and environmental conditions have been derived from the data to minimize uncertainties. Model kinetic coefficients derived from literature values from other studies have been validated via the model calibration process. It should be pointed out that model calculations are not intended to "curve fit" the data. Rather, the calibrated coefficients (e.g. algal growth kinetics) are obtained through a series of model sensitivity runs with reasonable and narrow ranges of their values derived from the literature and other estuaires modeling studies. The modeling framework is designed to mimic the seasonal trends of algal growth and nutrient dynamics and has been shown to accomplish the
task, particularly reproducing the seasonal trend of blue-green algal blooms. In model sensitivity analyses, adjusting the kinetic coefficients and constants (within their narrow ranges) to improve the calibration of a certain water quality constituents often resulted in adverse outcome of matching other water quality constituents. These were the constraints in the model calibration process which eventually led to the derivation of a unique set of model coefficients. Finally, model accuracy can further be tested by post auditing the model following the implementation of control measures (Thomann, 1982, 1987; Thomann et al., 1985). FACTORS AFFECTING BLUE-GREEN
ALGAL BLOOMS
The results from model calibration runs can be examined to provide insight into the potential of blooms. Factors affecting blue-green algal blooms in the lower Neuse River in 1983, 1984 and 1985 are summarized along with model results in Fig. 7. First, upstream boundary conditions as entered into the model are presented. They include daily average flows
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and nutrient supply (NH~, NOi- + N O ; , and orthophosphate concentrations) at Kinston on a timevariable basis covering these 3 years. Temperature (in surface segments) and light extinction coefficients "n the water column are shown in Fig. 7. Light extinction coefficients are due to detrital materials only. Additional attenuation of light due to algal selfshading is added in the model using calculated phytoplankton biomass to establish total light extinction in the water column. Finally, time-variable growth rates of the blue-green algae at New Bern affected by
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Fig. 6. Model calibration (nutrients, salinity, chlorophyll a and dissolved oxygen) of 1985 data. high river flows eliminated conditions favorable for the blue-green algae. This was substantiated by a model sensitivity run with the 1984 nutrient supply and the 1983 flow condition. Almost identical blooms as observed in 1983 were calculated. This further implies the significance of flow as a key factor in determining the system response. The 1985 conditions were different from those in 1983 and 1984 in that a near drought condition developed in early April (Fig. 7). Assisted by favorable light conditions (not excessive light extinction), a healthy population of diatoms and green algae
developed in late spring of 1985 and continued into mid summer. Storm events in August not only significantly lowered phytoplankton biomass, but also prevented blue-green algal blooms. As a result, a blue-green algal bloom did not occur in 1985 (Fig. 6) even though blue-green algal growth rates were slightly higher than 1983. The hydrologic condition (flow) once again played a crucial role in preventing blue-green blooms. It should be stressed that the growth rates of the blue-green algae are more or less the same prior to integrating the flow for the 3 years studied (Fig. 7).
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Fig. 7. Nutrient supply, environmental conditions and growth rate of blue-green algae in the lower Neuse River. When the seasonal flow condition is integrated, the model yields different responses between 1983 and 1984. This is a good indication that freshwater flow conditions are directly responsible for the occurrence of blue-green algae blooms in the lower Neuse River. SUMMARY AND CONCLUSIONS A eutrophication model has been developed to simulate blue-green algal blooms in the lower Neuse River, N.C. and to address eutrophication control in the system. Important features of the model include four functional groups of phytoplankton: diatoms, greens, non-nitrogen fixing blue-green algae and nitrogen fixing blue-green algae; a two-layer mass transport pattern to characterize the surface dwelling
blue-green algae; and the effect of salinity on algal growth. In addition, each of the two layers in the water column is further divided into five longitudinal segments to account for the longitudinal concentration gradients of the water quality constituents. Water quality constituents simulated by the model are individual chlorophyll a concentrations associated with the four algal groups, organic nitrogen, ammonia nitrogen, nitrite and nitrate nitrogen, organic phosphorus, orthophosphate, dissolved oxygen and salinity. Biochemical, biological and chemical interactions between the water quality constituents are incorporated into the model to quantify phytoplankton growth and death, algal species competition, nutrient uptake and recycling and photo-
Modeling blue-green algal blooms synthetic reproduction and respiration of oxygen by algae. A large data base consisting of water quality data in 1983, 1984 and 1985 was used for model development, calibration and verification. The modeling framework is able to mimic the seasonal trend of phytoplankton nutrient dynamics in the system. The model results confirm a hypothesis that initiation of the blue-green algal blooms in the lower Neuse River is strongly regulated by summer river flow under present excessive (for growth) nutrient conditions. While post auditing o f the model following the implementation of eutrophication control measures is necessary to substantiate its predictive ability, the Neuse Eutrophication Model can be used as a planning tool to examine the spectrum of responses of the system which may occur under various planning alternatives, including nutrient control and flow control. The water quality trends and relative impacts associated with control alternatives, rather than actual predictions, should be used to address the management questions outlined in the beginning of the paper. Acknowledgements--The authors are grateful to the Soap and Detergent Association for their support for this modeling study. Funding for field data collection, laboratory nutrient and chlorophyll a determinations was provided by the University of North Carolina Water Resources Research Institute, Project No. 70025 and North Carolina Sea Grant, Projects R/MER-I and R/MER-5. REFERENCES
Auer M. T. and Canale R. P. (1980) Phosphorus uptake dynamics as related to mathematical modeling at a site on Lake Huron. J. Great Lakes Res. 6, 1-7. Bierman V. J. and Doland D. M. (1986) Modeling of phytoplankton in Saginaw Bay: I. Calibration phase. J. envir. Engng Div. Am. Soc. civ. Engrs 112, 400-414. Canale R. P. and Vogel A. H. (1974) Effect of temperature on phytoplankton growth. J. envir. Engng Div. Am. Soc. civ. Engrs 100, 231-241. Christian R. R., Bryant W. L. and Stanley D. W. (1986) The relationship between river flow and Microcystis aeruginosa blooms in the Neuse River, North Carolina. Water Resources Research Institute of the University of North Carolina Report No. 223. Di Toro D. M., O'Connor D. J. and Thomann R. V. (1971) A dynamic model of the phytoplankton population in the Sacramento-San Joaquin Delta. Adv. Chem. Ser. American Chemical Society 106, 131-180. Filardo M. J. (1984) Phytoplankton ecology and dynamics in the James River Estuary, Virginia, U.S.A. Ph.D. dissertation submitted to the Old Dominion University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy. Fogg G. E. (1969) The physiology of an algal Nuisance. Proc. R. Soc. B. 173, 175-189. Fulton R. S. III and Paerl H. W. (1987) Effects of colonial morphology o n z o o p l a n k t o n utilization of algal resources during blue-green algal (Microcystis aeruginosa) blooms. LimnoL Oceanogr. 32, 634-644. Gentile J. M. (1971) Blue-grcen and green algal toxins. In Algal Toxins (Edited by Kadis S. and Ciegler A.), pp. 27-66. Academic Press, New York. Gorham P. R. and Carmichael W. W. (1979) Phytotoxins from blue-green algae. Pure appl. Chem. 52, 165-174.
905
HydroQual Inc. (1981) Water quality analysis of the Patuxent River. Report prepared for the State of Maryland. HydroQual Inc. (1987) Development of a coupled hydrodynamic/water quality model of the eutrophication and anoxia processes of the Chesapeake Bay. Report submitted to the U.S. EPA. Lung W. S. (1986) Assessing phosphorus control in the James River Basin. J. envir. Engng Div. Am. Soc. civ. Engrs 112, 44-60. Lung W. S. and O'Connor D. J. (1984) Two-dimensional mass transport in estuaries. J. Hydraul. Engng Div. Am. Soc. civ. Engrs 110, 1340-1357. Lung W. S. and Paerl H. W. (1986) Modeling the blue-green algal bloom in the Neuse River Estuary. Department of Civil Engineering Report No. UVA/-53106/CE86/101. Morris A. W., Bale A. J. and Howland R. J. M. (1982) Chemical variability in the Tamar Estuary south-west England. Estuarine Coast. Shelf Sci. 14, 649~61. O'Connor D. J. and Lung W. S. (1981) Suspended solids analysis of estuarine systems. J. envir. Engng Div. Am. Soc. cir. Engrs 107, 101-120. O'Connor D. J., Di Toro D. M. and Thomann R. V. (1975) Phytoplankton models and eutrophication problems. In Ecological Modeling (Edited by Russell C. S.), pp. 149-208. Resources for the Future Inc., Washington, D.C. Paerl H. W. (1987) Dynamics of blue-green algal (Microcyst& aeruginosa) blooms in the lower Neuse River, North Carolina: causative factors and potential controls. University of North Carolina Water Resources Research Institute Report UNC-WRRI-87-229. Paerl H. W., Bland P. T., Blackwell J. H. and Bowles N. D. (1984) The effect on the potential of blue-green algal (Microcystis aeruginosa) bloom in the Neuse River Estuary, N.C. North Carolina Sea Grant Working Paper 84-I. Pennock J. R. (1983) Regulation of chlorophyll distribution in the Delaware Estuary by short term variability in vertical stratification and suspended sediment concentration. EOS Trans. Am. geophys. Union 64, 1041. Porter K. G. and Orcutt T. (1980) Nutritional adequacy, manageability, and toxicity as factors that determine the food quality of green and blue-green algae for Daphnia. In Special Symp. IlL Am. Soc. Limnol. Oceanogr. The Evolution and Ecology of Zooplankton (Edited by Kerfoot W. C.), pp. 269-281. Reynolds C. S. and Walsby A. E. (1975) Water blooms. Biol. Rev. 50, 437-481. Sharp J. H., Sulberson C. H. and Church T. M. (1982) The chemistry of the Delaware Estuary. General considerations. Limnol. Oceanogr. 27, 1015-1028. Tedder S. W., Sauber J., Ausley J. and Mitchell S. (1980) Working paper: Neuse River Investigations 1979. Division of Envir. Management, N.C., Dept. of Natural Resources and Community Development, Raleigh, N.C. Thomann R. V. (1982) Verification of water quality models. J. envir. Engng Div. Am. Soc. cir. Engrs 108, 923-940. Thomann R. V. (1987) System analysis in water quality management--a 25 year retrospect. In System Analysis in Water Quality Management (Edited by Beck M. B.), pp. 1-14. Pergamon Press, Oxford. Thomann R. V. and Fitzpatrick J. J. (1982) Calibration and verification of a mathematical model of the eutrophication of the Potomac Estuary. Prepared by HydroQual Inc. for the Government of the District of Columbia. Thomann R. V., Jaworski N. J., Nixon S. W., Paerl H. W. and Taft J. (1985) The 1983 algal bloom in the Potomac Estuary. Report prepared for the Potomac Strategy State/EPA Management Committee. U.S. EPA (1985) Rates, Constants, and Kinetics Formulations in Surface Water-Quality Modeling, 2nd Edition. EPA/600/3-85/040.