phytoplankton dynamics in a seasonal estuaryfreshwater and estuarine species. dinoflagellates have...

Phytoplankton dynamics in a seasonal

estuary

Terence Chan

This thesis is presented for the degree of Doctor of Philosophy of Environmental Engineering of The University of Western Australia, Centre for Water Research, Environmental Engineering, 2006.

Abstract

The Swan River is a highly seasonal estuary in the south-west of Western Australia.

Salinity may vary from fresh to marine at various times throughout the estuary,

depending mostly on the intensity of freshwater discharge. There are occasional

problematic dinoflagellate blooms which have spurred investigation of the dynamics of

the phytoplankton community. The objective of this research was to examine how

phytoplankton biomass and species' successions are influenced by the multiple

variables in the aquatic ecosystem, and, if possible, to determine the dominant factors.

Physical and chemical characteristics at nine sites in the estuary were examined over

three years and related to phytoplankton community biomass and succession. The

three major phytoplankton groups, diatoms (Bacillariophyta), dinoflagellates

(Dinophyta) and chlorophytes (Chlorophyta), are strongly separated temporally by

season, and spatially by site along the Swan River estuary, according to variations in

flow and salinity. Diatoms exhibit the widest range of maximum potential growth

rates and occur under a wide range of discharges as a result of successions between

freshwater and estuarine species. Dinoflagellates have the lowest growth rates, and

occur only at very low discharges. Chlorophytes are intermediate in their potential

growth rates, and are restricted to freshwater conditions. Freshwater discharge

strongly affects the estuary residence time available for different phytoplankton taxa to

grow, according to their potential rates of cell growth. This factor is the strongest

predictor of phytoplankton behaviour in the estuary. The discharge also influences

succession between marine, estuarine and freshwater phytoplankton taxa according to

the extent that intrusion of marine water into the estuary is hindered.

In the Swan River estuary, nutrients appear to be less important than flow and salinity

in regulating phytoplankton succession and biomass, though seasonally averaged data

reveal a significant correlation between dissolved inorganic nitrogen concentrations in

winter and phytoplankton biomass in the following spring. It is highly likely that

anthropogenic effects on freshwater discharge to Australian estuaries have had a

significant impact on composition and biomass of phytoplankton communities.

The four principle phytoplankton groups were modelled with a three-dimensional

coupled hydrodynamic-ecological numerical model, ELCOM-CAEDYM. The

modelled area extended from the estuary mouth to the major confluence 60 km

upstream. Trends in physical parameters and nutrient concentrations in the estuary

were reasonably well replicated, however hypoxia in the near-bed waters was poorly

simulated, with attendant difficulties in modelling of sediment nutrient release.

Despite this problem, the annual phytoplankton succession was successfully

reproduced, though model simulations produced consistently lower variability in

biomass. Factors that may have contributed to this uniformity may include the absence

of higher trophic levels in the model, paucity of measured tributary boundary data, and

smoothing of phytoplankton patchiness at the scale of the model grid. Comparisons of

phytoplankton nutrient limitation simulations with experimental observations from

field bioassays require further investigation, but reinforce findings that nutrients may

only limit phytoplankton biomass when there is a convergence of favourable

hydrological and hydrodynamic conditions.

The Swan River estuary has undergone substantial hydrological modifications from

pre-European settlement. Land clearing has increased freshwater discharge up to 5-

fold, while weirs and reservoirs for water supply have mitigated this increase and

reduced the duration of discharge to the estuary. Nutrient loads have increased

approximately 20-fold from pre-European levels. The individual and collective

impacts of these hydrological changes on the Swan River estuary were examined using

the hydrodynamic-ecological numerical model. The simulation results indicate that

despite increased hydraulic flushing and reduced residence times, increases in nutrient

loads are the dominant perturbation, producing increases in the frequency and biomass

of blooms by both estuarine and freshwater phytoplankton. By comparison, changes in

salinity associated with altered seasonal freshwater discharge have a limited impact on

phytoplankton dynamics. Reductions of nutrient inputs into the Swan River estuary

from its catchment will provide a long-term improvement in water quality but

manipulations of freshwater discharge have the potential to provide a provisional

short-term remediation measure allowing at least partial control of phytoplankton

bloom potential and eutrophication.

Statement of originality

This thesis consists of a series of papers, both published and intended for publication.

In all papers, the first author performed the work, analysis, writing and presentation.

The second author provided supervision and review of the work, and any subsequent

authors provided additional advice and inspiration, but the material consists of the first

author’s own ideas and interpretations.

Acknowledgments

I wish to firstly thank my supervisor, David Hamilton, for his unrelenting patience,

support, encouragement and inspiration. I also thank Barbara Robson for her constant

good advice and programming savvy. Malcolm Robb, from the Water and Rivers

Commission, is gratefully acknowledged for his support and provision of data. Thanks

go to Jorg Imberger and Murugesu Sivapalan at the Centre for Water Research for

their motivation and patience, as well as the other academics and postgraduate students

for their support and encouragement. I would also like to thank Mike Grace and Barry

Hart and everyone at the Water Studies Centre for being so understanding in the final

stages. Finally, of course, much thanks to my friends and family for the necessary

sanity-checks.

Table of Contents

1 Introduction....................................................................................................... 11

1.1 Motivation ................................................................................................. 11

1.2 Thesis overview......................................................................................... 14

1.3 References ................................................................................................. 15

2 Literature Review.............................................................................................. 17

2.1 Physical factors.......................................................................................... 18

2.1.1 Freshwater discharges ........................................................................ 18

2.1.2 Tides.................................................................................................. 19

2.1.3 Salinity .............................................................................................. 20

2.1.4 Dissolved Oxygen .............................................................................. 21

2.1.5 Light .................................................................................................. 23

2.1.6 Temperature....................................................................................... 23

2.1.7 pH...................................................................................................... 24

2.2 Nutrients.................................................................................................... 24

2.2.1 Nitrogen............................................................................................. 25

2.2.2 Phosphorus ........................................................................................ 26

2.2.3 Nutrient ratios and limitation.............................................................. 27

2.3 Phytoplankton............................................................................................ 28

2.3.1 Phytoplankton blooms........................................................................ 30

2.4 Study site................................................................................................... 32

2.5 References ................................................................................................. 36

3 Analysis of the effects of physico-chemical factors on the Swan River estuary

phytoplankton succession and biomass in the field .................................................... 40

3.1 Abstract..................................................................................................... 40

3.2 Introduction............................................................................................... 41

3.2.1 Study Site .......................................................................................... 43

3.3 Methods .................................................................................................... 45

3.4 Results ...................................................................................................... 46

3.4.1 Salinity .............................................................................................. 46

3.4.2 Phytoplankton composition................................................................ 47

3.4.3 Physical influences ............................................................................ 49

3.4.4 Nutrients............................................................................................ 51

3.4.5 Seasonal averages .............................................................................. 53

3.5 Discussion................................................................................................. 54

3.5.1 Physical influences ............................................................................ 54

3.5.2 Nutrients............................................................................................ 59

3.5.3 Seasonal averages .............................................................................. 61

3.5.4 Recent developments ......................................................................... 61

3.6 Acknowledgments ..................................................................................... 62

3.7 References................................................................................................. 63

3.8 Figures ...................................................................................................... 67

4 Three-dimensional modelling of processes controlling phytoplankton dynamics in

the Swan River estuary ............................................................................................. 83

4.1 Abstract..................................................................................................... 83

4.2 Introduction............................................................................................... 83

4.2.1 Study site........................................................................................... 86

4.3 Methods .................................................................................................... 88

4.3.1 Numerical Model............................................................................... 88

4.3.2 Model input data and analysis ............................................................ 89

4.4 Results and Discussion .............................................................................. 93

4.4.1 Calibrated parameters......................................................................... 93

4.4.2 Physical results and validation............................................................ 94

4.4.3 Ecological results and validation ........................................................ 95

4.5 Acknowledgments ................................................................................... 107

4.6 References ............................................................................................... 108

4.7 Tables...................................................................................................... 112

4.8 Figures..................................................................................................... 118

5 Scenario modelling with a 3D hydrodynamic-ecological model to investigate the

impacts of hydrological changes on phytoplankton dynamics in the Swan River estuary

........................................................................................................................ 135

5.1 Abstract ................................................................................................... 135

5.2 Introduction ............................................................................................. 135

5.3 Study site................................................................................................. 137

5.3.1 General Description ......................................................................... 137

5.3.2 Post-European modifications............................................................ 139

5.4 Methods................................................................................................... 140

5.5 Results..................................................................................................... 144

5.5.1 Increased flow in the absence of tributary impoundments................. 145

5.5.2 Pre-European watershed................................................................... 145

5.5.3 Pre-European watershed without flow reduction............................... 147

5.5.4 Pre-European watershed without nutrient reduction.......................... 147

5.6 Discussion ............................................................................................... 148

5.7 Conclusions ............................................................................................. 151

5.8 Acknowledgments ................................................................................... 152

5.9 References............................................................................................... 152

5.10 Figures .................................................................................................... 155

6 Conclusions .................................................................................................... 167

6.1 Suggestions for future work..................................................................... 171

6.2 References............................................................................................... 174

APPENDIX I: Modelling phytoplankton succession and biomass in a seasonal West

Australian estuary ................................................................................................... 176

Introduction ........................................................................................................ 176

Methods.............................................................................................................. 177

Results and discussion ........................................................................................ 178

Acknowledgments .............................................................................................. 179

References .......................................................................................................... 180

Figures................................................................................................................ 181

APPENDIX II: Reply to examiners’ reports............................................................ 183

APPENDIX III: Additional figures for Chapter 3 “Analysis of the effects of physico-

chemical factors on the Swan River estuary phytoplankton succession and biomass in

the field”................................................................................................................. 206

APPENDIX IV: Additional data for Chapter 4 “Three-dimensional modelling of

processes controlling phytoplankton dynamics in the Swan River estuary”. ............ 212

Chapter 1. Introduction

11

1 Introduction

1.1 Motivation

In recent decades, there has been an apparent increase in the frequency and intensity of

phytoplankton blooms in coastal as well as limnic environments world-wide (e.g.

Bodeanu and Usurelu 1979; Lam and Ho 1989; Smayda 1990). This increase has been

associated with and attributed to increases in anthropogenic sources of nutrients to

many aquatic systems (e.g. May 1981; Malone et al. 1988; Hallegraeff 1993). These

trends have also been noted in the Swan River estuary in south-west Western Australia

(Hosja and Deeley 1994; Atkins 1995).

Most phytoplankton blooms in the Swan River are of nuisance level though more

serious problems have occurred on occasions. Phytoplankton blooms reduce the

recreational and aesthetic amenity of the estuary and the biodiversity of its planktonic

community (Chretiennot-Dinet 2001; Kononen 2001). The very high biomass of a

large bloom can also lead to deoxygenation of the water column, with associated

effects on the estuarine ecology, including highly visible effects such as fish kills (e.g.

Mitchell and Burns 1979; Day et al. 1989; Smayda 1990; Hallegraeff 1993). Certain

species of algae also produce toxins, and with the high concentrations of cells in

blooms, infested waters can be toxic to livestock or recreational river users (e.g. Olson

1949; Gorham 1964; May 1981). Although toxins have rarely been detected in

phytoplankton in the Swan River, deoxygenation associated with blooms has occurred

on several occasions (Hosja and Deeley 1994). There is concern that the Swan River

may be a microcosm of the global situation in which increasing nutrient concentrations


12

underlie greater frequency and intensity of phytoplankton blooms (Hamilton and

Turner 2001). These findings provide the impetus for this study; to improve

understanding and predictive capabilities for phytoplankton populations in the Swan

River estuary.

The aim of the present research is to identify the processes involved in the succession

of phytoplankton and the development of blooms in the Swan River estuary. This

research will facilitate estuary management techniques that aim to prevent potentially

dangerous and ecologically harmful phytoplankton blooms in the Swan River, while

more generally, it should assist in the understanding of eutrophication-related water

quality problems and phytoplankton succession in estuaries.

Only relatively recently have the problems associated with eutrophication been widely

recognised in the context of coastal and estuarine systems (Eliot and de Jonge 2002).

Even now, however, there are relatively few interdisciplinary models of coupled

physical-biogeochemical processes in estuaries. This shortcoming may be attributed to

three main factors: (1) extensive data requirements of these models, (2) the complex

nature of benthic-pelagic coupling in estuaries (Geyer et al. 2000; Eyre 1993), and (3)

the large vertical and horizontal gradients of physical and biogeochemical variables

(Hofmann 2000).

Estuaries have strong environmental gradients, and physico-chemical conditions in

estuaries are often subject to large spatial and temporal variations. This variability

affects the structure and dynamics of biological communities in estuaries (Attrill and

Rundle 2002). Conceptual understanding of eutrophication and phytoplankton


13

dynamics that has been developed in well studied systems subject to far less inherent

variation, may not be directly applicable to estuaries. Boynton et al. (1982) give an

extensive review of past estuarine studies, though almost solely from North America,

and note the commonalities of environmental gradients, though other authors (e.g.

Harris 1995) also note the difficulty in characterizing any common significant

relationships, based on Australian aquatic ecosystems.

The primary aim of this study is to investigate the phytoplankton dynamics and the

mechanisms of bloom formation in the Swan River estuary. Specifically, we are

interested in understanding the role of:

� The physical effects of water movement and physical exchange at the estuary

boundaries, as well as the direct effects of changes in salinity, dissolved oxygen

(DO), pH, temperature and turbidity on phytoplankton dynamics.

� The aquatic chemistry of the estuary, taking into account the complex feedback

mechanisms with biological communities, particularly how changes in nutrient

concentrations affect phytoplankton and the other transformations of nutrients

that influence their concentration.

� The effects of seasonal changes in physico-chemical conditions on succession

and biomass of different phytoplankton taxa, particularly those species with

bloom potential.

A numerical modelling tool was also applied in order to develop a predictive capacity

for phytoplankton biomass and succession, and to test hypotheses about the role of

different processes in the dynamics of phytoplankton populations. The model also

provided an excellent tool with which to integrate the many complex physico-chemical


14

and biological processes operating to influence the water quality of the Swan River

estuary.

1.2 Thesis overview

The main body of this thesis is comprised of a series of three complementary scientific

papers (Chapters 3-5). The chapter titles have been changed from those of the original

published papers to better reflect the material content in relation to the thesis as a

whole. A fourth published paper is included as Appendix I. Each chapter includes an

introduction, review of background literature, and methodology, and out of necessity

involves some degree of repetition, particularly with respect to descriptions of the

study site. Chapter 2 provides additional background information about the study site

and presents a review of literature on estuarine processes and phytoplankton dynamics.

Chapter 3 is an analysis of physical, chemical and biological field data from the Swan

River estuary with respect to potential effects on phytoplankton dynamics. This

chapter was published as “Effects of freshwater flow on the succession and biomass of

phytoplankton in a seasonal estuary”, Marine and Freshwater Research, volume 52,

pp. 869-884, 2001. Chapter 4 describes the hydrodynamic-ecological numerical

modelling of the estuary’s ecosystem and its major phytoplankton taxa, the calibration

and validation of the model, and some of the insights gained in the modelling process.

This chapter will be modified slightly for submission to Ecological Modelling.

Chapter 5 further uses the numerical model to explore a number of scenarios and the

individual and collective impacts of hydrological changes to the catchment. This

chapter was published as “Impacts of hydrological changes on phytoplankton

succession in the Swan River, Western Australia”, Estuaries, volume 25(6B), pp.


15

1306-1415, 2002. Overall conclusions are presented in Chapter 6 along with

suggestions for further research.

The final paper included as Appendix I is “Modelling phytoplankton succession and

biomass in a seasonal West Australian estuary”, published in Verhandlungen der

Internationale Vereinigung für Limnologie, volume 28, pp. 1086-1088, 2001. This

paper describes some of the preliminary modelling of a domain restricted to the upper

reaches of the Swan River estuary. Appendix II is the candidate’s reply to the

examiners’ reports, with supporting material in Appendix III and Appendix IV.

1.3 References

Atkins, R. (1995). The Swan and Canning Rivers Cleanup Program: Action for the Future. The Swan River Trust,

Perth.

Atrill, M.J., and Rundle, S.D. (2002). Ecotone or Ecocline: Ecological Boundaries in Estuaries. Estuarine,

Coastal Shelf Science 55: 929-936.

Bodeanu, N. and M. Usurelu. (1979). Dinoflagellate blooms in Romanian Black Sea coastal waters. In ‘Toxic

Dinoflagellate Blooms : proceedings of the Second International Conference on Toxic Dinoflagellate

Blooms, Key Biscayne, Florida, October 31-November 5, 1978.’ (Eds: D.J. Taylor, and H.H. Seliger)

pp.151-154. (Elsevier, Amsterdam).

Boynton, W.R., Kemp, W.M., and Keefe, C.W. (1982). A comparative analysis of nutrients and other factors in

influencing estuarine phytoplankton production. In: ‘Estuarine Comparisons.’ (Ed: V.S. Kennedy) pp. 69-

90. (Academic Press, New York).

Day, J.W., Hall, A.S., Kemp, W.M. and Yanez-Aranciba, A. (1989). Estuarine phytoplankton. In ‘Estuarine

Ecology.’ (Ed: J.W. Day.) (John Wiley and Sons, New York).

Eliot, M. and de Jonge, V.N. (2002). The management of nutrients and potential eutrophication in estuaries and

other restricted water bodies. Hydrobiologia 475: 513-524.

Eyre, B. 1993. Nutrients in the sediments of a tropical north-eastern Australian estuary, catchment and nearshore

coastal zone. Australian Journal of Marine & Freshwater Research 44(6): 845-866.

Geyer, W.R. and Farmer, D.M. (1989). Tide-induced variation of the dynamics of a salt wedge estuary. Journal of

Physical Oceanography 19: 1060-1072.

Gorham, P.R. (1964). Toxic algae. In ‘Algae and Man.’ (Ed: D.F. Jackson) pp. 307-336. (Plenum Press, New

York).

Hallegraeff, G.M. (1993). A review of harmful algal blooms and their apparent global increase, Phycological

Reviews 13. Phycologia 32(2): 79-99.


16

Hamilton, D.P., and Turner, J.V. (2001). Integrating research and management for an urban estuarine system: the

Swan-Canning estuary, Western Australia. Hydrological Processes 15: 2383-2386.

Harris, G.P. (1995). The ecological basis of eutrophication - are Australian waters different from those overseas?

AWWA Water 22(2): 9-12

Hofmann, E.E. (2000). Modeling for estuarine synthesis. In ‘Estuarine Science, a Synthetic Approach to Research

and Practice’, (Ed: J.E. Hobbie.) pp. 129-148. (Island Press: Washington D.C.).

Hosja, W., and D. Deeley. (1994). Harmful phytoplankton surveillance in Western Australia. Waterways

Commission Report No 43.

Lam, C.W.Y. and Ho, K.C. (1989). Red tides in Tolo Harbour, Hong Kong. In ‘Red Tides: Biology,

Environmental Science and Toxicology.’ (Eds: T. Okaichi, D.M. Anderson, and T. Nemoto) pp. 49-52.

(Elsevier Science Publishing, Co., New York).

Malone, T.C., Crocker, L.H., Pike, S.E., and Wendler, B.W. (1988). Influences of river flow on the dynamics of

phytoplankton production in a partially stratified estuary. Marine Ecology Progress Series 48:235-249.

May, V. (1981). The occurrence of toxic cyanophyte blooms in Australia. In ‘The water environment: algal toxins

and health.’ (Ed: W.W. Carmichael) (Plenum Press, New York).

Mitchell, S.F. and Burns, C.W. (1979). Oxygen consumption in the epilimnia and hypolimnia of two eutrophic,

warm-monomictic lakes. New Zealand Journal of Marine and Freshwater Research 13: 427-441.

Olson, T.A. (1949). History of toxic plankton and associated phenomena. Algae-laden water causes death of

domestic animals; nature of poison. Sewage Works Engineering 20(2): 71.

Smayda, T.J. (1990). Novel and nuisance phytoplankton blooms in the sea: evidence of a global epidemic. In

‘Toxic marine phytoplankton.’ (Eds: E. Graneli, B. Sundstrom, L. Edler, and D.M. Anderson) pp. 29-40.

(Elsevier Science Publishing, N.Y.).

Chapter 2. Literature review

17

2 Literature Review

An estuary can be defined as

"a semi-enclosed coastal body of water which has a free connection with the

open sea and within which sea water is measurably diluted with fresh water

derived from land drainage" (Cameron and Pritchard 1963).

This broad definition encompasses a range of systems wherein density driven

circulation and mixing result in the complex interaction of physical, chemical and

biological components. Estuaries are subject to flow, tides and inputs which are

continually changing, each combination of variables producing a unique system (Dyer

1973). As with many hydrologic systems, estuaries are often associated with fertile

waters and lands, transport routes and water supply. As a result they are centres for

human development, and are subject to associated developmental pressures and

changes. Research into these systems is vital to provide an understanding that can

allow us to avoid damage to the human and natural environment.

Research on water bodies has historically focused on lakes (Wetzel 1983) and river

systems (Hynes 1970). Ongoing research into estuaries such as Chesapeake Bay in

Maryland, USA, St Lawrence Bay in Canada, and San Francisco Bay in California,

USA, have remedied this situation somewhat, providing complementary data sets to

counter-balance the limnological bias (e.g. Boynton et al. 1982; Anderson 1986;

Marshall and Alden 1990; Cloern 1996). However, there remain several areas in

which scientific understanding and monitoring of estuarine systems lags behind that of

inland waters or the open ocean.


18

Anthropogenic effects on ecosystems have increased progressively, and the

consequences of urbanization and development of catchments have produced a general

decline in water quality of estuaries (Boynton et al. 1982; Nixon and Pilson 1983).

This decline is expressed in part in the development of algal blooms, though the

occurrence of blooms is a coalescence of many interacting physical, chemical and

biological processes. Some background to the processes and their relevance to the

current study are discussed in this chapter.

2.1 Physical factors

Biological processes in estuaries are affected by physical forcings on a variety of

scales (Cloern 1996). These forcing factors include salinity, tides, flow, light,

dissolved oxygen (DO), temperature and pH. Some of the important physical factors

and their role in phytoplankton dynamics are discussed in the following subsections.

2.1.1 Freshwater discharges

Flow is one of the most important factors influencing estuaries (Dyer 1973). Its effect

is also one of the most difficult to assess due to its unpredictability (D’Elia et al. 1992).

Inputs and outputs, turbulence, kinetic energy, stratification and mixing in an estuary

are all dependent on freshwater inflow (D’Elia et al. 1992; Cloern 1996). Although the

main source of flow variation, particularly in warm climates, is precipitation,

anthropogenic effects are also important. Damming and withdrawal (i.e. via bores) can

significantly reduce flows in a system and alter their timing, while urban and

agricultural development of catchments, through changes in vegetation, irrigation and

diversion of flow, may substantially alter hydrology.


19

The annual discharge of the Swan-Canning system is highly variable. In the past, peak

flow in the Avon has varied from 1 x 108 m3 yr-1 to almost 15 x 108 m3 yr-1 (Hillman et

al. 1995). Seasonal variability is also high. The Mediterranean climate of hot, dry

summers and cool, wet winters results in the occurrence of approximately 70% of

average annual rainfall between June and September. River flow typically lags the

rainfall by about one month, although this lag time is much reduced during periods of

high flow (Thompson and Hosja 1996). Up to 95% of flow usually occurs between

May and October (Douglas et al. 1996). This seasonality is a dominant feature of the

system’s hydrology (Stephens and Imberger 1996).

2.1.2 Tides

Waves arising from tides propagate from the seaward boundary of an estuary, resulting

in oscillating currents which greatly affect circulation, turbulence, and mixing.

Attenuation of the tidal wave occurs as it propagates upstream. The interaction of

relatively constant tidal forcing with seasonal flow patterns, results in seasonal

estuarine circulation patterns and salinity (Dyer 1973).

The Swan-Canning system is a microtidal estuary (Burling 1994). Microtidal estuaries

occur when the tidal amplitude is too low to alter the physical conditions of the

estuary; this is generally defined as tidal amplitudes of less than 2 m. Tidal amplitudes

are affected by global topography, where propagation of a tidal wave is influenced by

landmasses, and dissipation of tidal energy and amplitude by ocean-bed bathymetry

(Dyer 1973). Local topography is also highly significant, particularly when there are

islands in a water body, or when it is enclosed within bays and estuaries. At the mouth

of the Swan River, spring tide is approximately 0.65 m in amplitude, while neap tide is


20

approximately 0.2 m (Burling 1994). In this microtidal regime, atmospheric pressure

systems can have a significant influence, producing variations in water level of up to

0.3 m on a time-scale generally several times longer than the astronomical tide

(Burling 1994). The tidal excursion in the Swan River estuary (i.e. the distance

upstream and downstream that the salt-wedge moves over a tidal cycle) is 2 to 4 km.

The regime is mainly diurnal in summer and winter, with smaller semi-diurnal tides

occurring in spring and autumn (Thurlow et al. 1986; Douglas et al. 1996).

2.1.3 Salinity

Salinity has important physical implications on estuarine circulation due to its effect on

density. For example, a difference of 1 psu salinity increases density as much as a

temperature difference of 5 to 8 °C. Chemical and biological processes are also

affected by elevated concentrations of ions from seawater (Wetzel 1983).

Salinity in estuaries results from the intrusion of seawater which then mixes with water

arising from catchment runoff. Seawater has a salinity of approximately 35 psu

(Stumm and Morgan 1981), while catchment runoff is generally fresh, though in the

Swan River catchment it may range from 0-5 psu due to leaching of soil solutes,

which also reflects advancing soil salinization (Thurlow et al. 1986). The range of

salinities experienced in the Swan River estuary is thus a result of the interaction of

tides and catchment discharge. The salinity cycle in the estuaries of the south-west of

Western Australia is highly seasonal, depending on the seasonal rainfall and river

discharge as well as tidal flushing and evaporation (Spencer 1956; Hodgkin 1987;

Stephens and Imberger 1996). Surface salinity ranges from 5 psu or less to seawater

salinity, with the location of the gradient in the middle or lower estuary in winter, and


21

in the upper estuary during summer (Hodgkin 1987; Douglas et al. 1996; Kurup et al.

1998).

The salinity cycle timescale is long in comparison to the semi-diurnal to fortnightly

periods that typify the dominant salinity cycle in macro- and meso-tidal estuaries

(Dyer 1973; Kurup et al. 1998). The lack of turbulence under microtidal conditions

allows density stratification for significant periods of the year, and the formation of the

so-called "salt wedge" (Geyer and Farmer 1989; Newton 1996; Douglas et al. 1996).

After the peak flow in spring (in September-October), freshwater forms a surface layer

over the denser seawater (Debler and Imberger 1996). The saltwater intrusion

propagates upstream as a wedge during the dry summer months, oscillating with the

tide (van Senden 1991). Despite the small tidal excursion, the low-lying position of

the estuary on the Swan Coastal Plain allows extensive landward propagation of the

salt wedge (Thurlow et al. 1986). The onset of autumn rainfall and the resulting

"flush" displaces the wedge within about two weeks (van Senden 1991), however,

localized deep sites may still retain pockets of saline water.

2.1.4 Dissolved Oxygen

Oxygenation of the water column can occur through laminar diffusion from the surface

boundary, shear induced mixing processes such as wind, tide or river flow, convective

mixing, large scale circulations, wind-wave action, boating activity and photosynthetic

production by autotrophs, including phytoplankton (Wetzel 1983; Balls et al. 1996).

Dissolved oxygen (DO) is also affected by temperature and salinity, which control the

saturation concentration of DO in water (Wetzel 1983). It is consumed by all

organisms for respiration, including decay of detrital material by bacteria (Kemp et al.


22

1992), and by chemical processes (e.g. hydrogen sulfide, H2S, oxidation) in the

sediments. As oxygenation processes occur near the surface, while decay usually

occurs on the bed beneath the water column, DO stratification may take place,

particularly if there is a density gradient through the water column.

Localised pockets of deeper water, particularly in regions where dense saline water is

trapped, may accumulate particulate material. Here oxygen can become depleted, and

water quality may deteriorate. In the Swan River these sites are resistant to flushing

until flows are particularly high (Douglas et al. 1996; Kurup and Hamilton 2002).

Water quality records for the Swan River from 1962 to 1985 (Thurlow et al. 1986) also

indicate that biochemical oxygen demand (BOD) is usually less than 5 mg L-1. Levels

of >10 mg L-1 were mostly associated with high algal concentrations in the upper

estuary.

Recent monitoring indicates that DO levels in near-bed waters in the Swan are

regularly less than ~ 4 mg L-1, which may have adverse impacts on aquatic life

(Douglas et al. 1996; Thompson et al. 1996). Hypoxia (DO < 2 mg L-1) and anoxia (0

mg L-1 DO) can play an important role in the release of phosphate from its bound state

in the sediments (Mortimer 1971; Stumm and Morgan 1981; Maher and DeVries

1994), and in ammonium accumulation (Nixon and Pilson 1983; Kemp et al. 1990;

Cooper and Brush 1993). This nutrient release may play a key role in the development

of algal blooms in estuaries (Webb and D’Elia 1980; Cloern 1996). The eventual

collapse and decomposition of algal blooms are also an important positive feedback

mechanism for low oxygen levels.


23

2.1.5 Light

Shallow coastal environments are often turbid and nutrient rich. In these

circumstances, phytoplankton growth is likely to be limited primarily by light

availability (Wetzel 1983; Marshall and Nesius 1996; Gilbes et al. 1996; Cloern 1996).

As well as the regular daily and seasonal cycles of incident light, water column

irradiance is affected by suspended particles, phytoplankton, and colour. The

attenuation of light of the wavelengths required for phytoplankton growth, i.e.

photosynthetically active radiation (400 to 700 nm), depends on the absorbance of the

water itself, yellow substance (gilvin) arising from humic substances, suspended

matter, and phytoplankton (Kirk 1994).

In the Swan River estuary, water clarity is highest just before winter rains (April-May),

and reaches a nadir in August (Thompson 1998). Thompson (1998) also found that

light climate ranged considerably along the estuary except during the period of peak

clarity, with lower water clarity in the upper reaches.

2.1.6 Temperature

Temperature is important in any biological process. The so-called “Q10 rule” predicts

that growth rates will double for every increase in temperature of 10º C (Eppley 1972).

The photosynthetic response of phytoplankton to temperature has been demonstrated

in numerous studies (e.g. Platt and Jassby 1976; Davison 1991). Phytoplankton also

have preferred temperature ranges outside of which they will grow sub-optimally and

die at an enhanced rate (Geider 1998). In the Swan River, surface water temperatures

from 10 to 30º C (Thompson 1998) suggest temperature will have a significant

influence on phytoplankton dynamics.


24

2.1.7 pH

The pH of estuaries is controlled mainly by the carbonate equilibrium (Wetzel 1983).

Low pH is associated with high levels of dissolved organic matter. High pH is rare in

estuaries due to their connection with the sea (Wetzel 1983). Biological factors affect

pH via the use of CO2 in photosynthesis, raising the pH near the surface, and the

generation of CO2 in respiration, lowering the pH near the bed. Nitrification and

sulfide oxidation also decrease pH in bottom waters, while denitrification raises pH

(Wetzel 1983).

The annual variation in pH in the upper Swan appears to be small, with mean pH about

8 at both the surface and bed (Douglas et al. 1996). In comparison, a southern

tributary to the Swan, the Canning River, has reduced buffering by seawater due to a

weir, and pH is thus much more variable. Phytoplankton activity may raise pH to 9.5

at the surface during blooms, while pH is about 7 in the deeper holes (Thompson et al.

2003).

2.2 Nutrients

Nitrogen (N) and phosphorus (P) are the major nutrients of interest in aquatic systems.

In estuaries, they are supplied mainly in runoff from the catchment, and may

accumulate in the estuarine sediments for later recycling. The importance of

groundwater in providing these nutrients is currently being investigated (e.g. Brunke

and Gonser 1997), but has been found to be significant in the Swan River estuary

(Linderfelt and Turner 2001). Nitrogen enters estuary systems in a variety of organic

forms as well as in more bioavailable inorganic forms as ammonium (NH4+), nitrite


25

(NO2�� ) and nitrate (NO3

�� ). Phosphorus is mainly supplied in bound organic form,

often adsorbed to particulate matter, and in inorganic form as orthophosphate (PO43 �� ).

Urban and agricultural development of catchment areas has led to increased nutrient

inputs to watercourses. Eutrophication (i.e. nutrient pollution) occurs when this input

exceeds the rate of assimilation by primary producers, and nutrients can accumulate

(May 1981; Nixon and Pilson 1983; Smayda 1990; Melkonian 1995). Periods when

light and temperature are sub-optimal for phytoplankton growth augment this nutrient

accumulation, though water quality deterioration may not be evident during these

periods.

The cycling of these nutrients is important in understanding phytoplankton dynamics.

Details of nutrient cycling relevant to this study are included below.

2.2.1 Nitrogen

Nitrogen enters an estuary though surface inflows, groundwater, and fixation of

atmospheric nitrogen by cyanobacteria, and can be removed by sedimentation or

denitrification as well as outflows and tidal exchange. Inorganic forms (mainly

ammonium and nitrate) can be assimilated by aquatic organisms. Biologically

mediated chemical cycling between the inorganic forms also occurs, i.e.

ammonification of nitrate to ammonium, nitrification of ammonium to nitrate, and

denitrification as shown below:

NH4+ + 3/2 O2 � H+ + NO2

� + H2O nitrification by Nitrosomonas

NO2� + 3/2 O2 � NO3

� further oxidation by Nitrobacter

NO3� � NO2

� � N2O � N2 denitrification by Pseudomonas, etc.


26

Most of the total nitrogen in the Swan-Canning system is bound as particulate matter

in organic or inorganic compounds, or is present as refractory dissolved organic

matter, and is generally unavailable for use by the biota. Most of the readily available

forms of nitrogen occur in low concentrations in the water column and porewater

(Wetzel 1983; Valiela 1995).

In near-bed waters, where light limits plant uptake, anoxia and hypoxia can cause

accumulation of ammonium by preventing nitrification (e.g. Chen et al. 1979; Nixon

and Pilson 1983; Balls et al. 1996; Riccardi and Mangoni 1996; Alvarez-Salgado et al.

1996). Rochford (1974) found that oxidation of ammonium to nitrate occurred only at

> 5 % DO saturation (~0.7 mg L-1). Prevention of nitrification also hinders production

of nitrate required for loss of nitrogen to the atmosphere via denitrification.

Hydrodynamic regimes producing stratification that reinforces hypoxia are thus

important in determining the concentrations and distribution of different forms of

nitrogen. It has also been noted that under stratified conditions, a significant

proportion of phytoplankton primary production is recycled by bacterial breakdown of

organic compounds, resulting in regeneration of ammonium (e.g. Alvarez-Salgado et

al. 1996; Eyre and Twigg 1997).

2.2.2 Phosphorus

Phosphorus enters estuaries via inflow and groundwater. A small proportion (often <

10%) occurs as orthophosphate, which is the only form directly available for plant

assimilation. Under oxidizing conditions orthophosphate is removed from the system

through reaction with cations such as iron and calcium, and precipitation as insoluble


27

compounds (Wetzel 1983; Maher and DeVries 1994). Adsorption to particulate clays,

carbonates and hydroxides also occurs (Wetzel 1983). Once sedimentation of such

particles has occurred, re-entry to the water column is governed by oxygen supply at

the sediment-water interface and the transport of water between sediments, pore water

and the water column (Mortimer 1971; Maher and DeVries 1994). Phosphorus in

particulate organic forms (such as phytoplankton detritus) will also tend to sink to the

estuary bed, where it may either be resuspended or consolidated over time.

Phosphorus may thus accumulate in sediments until anoxic conditions occur. Once

released from its adsorbed or complexed state, the availability of phosphorus to

phytoplankton depends upon diffusion in order to reach the water column from the

porewater (Wetzel 1983). Under hypoxia and stratification, the hypolimnion may

accumulate phosphate (Rochford 1974; Bulleid 1984). Release of phosphates from

colloids also depends on pH (Eyre and Twigg 1997).

2.2.3 Nutrient ratios and limitation

Although historically it has often been assumed that primary production in aquatic

ecosystems is limited by phosphorus availability, most recent studies generally indicate

nitrogen limitation is more common in estuaries, especially in summer (Ryther and

Dunstan 1971; Marshall and Alden 1990; D’Elia et al. 1992; Schöllhorn and Granéli

1996; Thompson and Hosja 1996), with possible phosphorus limitation in spring

(Marshall and Alden 1990; Thompson and Hosja 1996). A high correlation with low

and high salinity has also been found for P- and N-limited production respectively

(Valiela 1995).


28

Water and sediment hypoxia may be followed closely by increases in phosphate and

ammonium concentrations (Bulleid 1984; Anderson 1986) and subsequent

phytoplankton growth. Bulleid (1984) concluded that the rate at which nutrients are

regenerated at the sediment water interface depends on the stability of the water

column. An intrusion of saltwater which contributes to stratification may also result in

increased dissolved nutrients (phosphate and ammonium) due to density displacement

of nutrient rich porewater (Anderson 1986).

2.3 Phytoplankton

In estuaries, the dominant phytoplankton groups are generally diatoms

(bacillariophyta), dinoflagellates and chlorophytes (Day et al. 1989). The different

groups vary widely in appearance, physiology, and dynamics (Capblancq and Catalan

1994). Additionally, each group is sufficiently varied that different species may range

in cell size from around 2 �m up to 2 mm in diameter (Banse 1976; Snoeijs et al.

2002). Generally however, dinoflagellates are relatively large, e.g. length of

Scripsiella ~ 25 �m, while diatoms and chlorophytes are smaller, e.g. Skeletonema ~

13 �m and Chlamydomonas at around 12 �m (Griffin 2000). However, formation of

multicellular colonies is a complicating factor, with some diatoms sometimes forming

colonies, while dinoflagellates are usually solitary (Peperzak et al. 2003), as is

Chlamydomonas (Agusti and Philips 1992), the dominant chlorophyte in the Swan

River.

In general, diatoms grow quickly and settle or decompose rapidly. They are easily

digestible by grazers, and have high nutritional value (Griffin et al. 2001). They are

non-motile, and non-nitrogen-fixing. A defining factor is their requirement for silica,


29

which they use in construction of highly differentiated cell walls. They may be

unicellular or colonial and are comprised of both freshwater and marine species

(Dodge 1973). Overall, diatoms are generally regarded as relatively benign in most

aquatic systems.

In contrast, dinoflagellate proliferations may be problematic, often being toxic or

inedible to zooplankton, and they may form “red-tides” (Schöllhorn and Granéli 1993).

Dinoflagellates are usually unicellular flagellates and motile, allowing them to

accumulate into dense aggregations, which may give them a competitive advantage by

allowing access to elevated nutrients in the near-bed region, and elevated light levels in

surface waters (Malone et al. 1988). Most dinoflagellates are marine, although there

are some freshwater species. Defining features include two flagella and a transverse or

spiral girdle (Dodge 1973). Next to diatoms, they are the most numerous primary

producers in coastal waters.

Chlorophytes are a large group of phytoplankton, usually found in freshwaters (Wetzel

1983). They are morphologically diverse and may be motile with multiple flagella.

They may be unicellular, colonial or filamentous (Matto and Stewart 1984).

Phytoplankton distribution is highly variable on all scales of time and space. In

estuaries, temporal distribution (i.e. succession) is affected greatly by the abrupt

abiotic seasonal influences. Spatially, variation occurs longitudinally through the

estuary, from the fresh upper reaches, toward the saline oceanic part of an estuary, as

well as laterally and vertically through the water column. Estuarine phytoplankton are

directly affected by many physical forcing factors, including salinity, temperature,


30

light availability, wind, and tides (Day et al. 1989). Indirectly, effects may also occur

as a result of these factors influencing the chemical (especially nutrient) environment

of the phytoplankton.

2.3.1 Phytoplankton blooms

Phytoplankton blooms may be defined as:

"transient departures from quasi-equilibrium when primary productivity

temporarily exceeds the losses and transports and the population grows rapidly

and reaches exceptionally high biomass" (Cloern 1987).

It is difficult to identify a particular benchmark biomass to define "bloom", as blooms

must be considered relative to local background levels. Between species, the numbers

of cells involved in a bloom may also differ due to the size of phytoplankton cells or

their tendency to occur in multicellular colonies (Capblacq and Catalan 1994).

Defined bloom concentrations are thus assigned somewhat arbitrarily.

Often, blooms occur as a sequence of changes in biomass and species composition in a

phytoplankton community. The blooms may thus be seasonal, or aperiodic. Seasonal

blooms are not confined to any specific time of year, and typically involve different

species under different seasonal conditions (Cloern 1996). A typical bloom cycle for a

temperate estuary may consist of winter-spring diatom domination, followed by

summer dinoflagellates and diatoms, and then autumn dinoflagellate blooms (Day

1989). Blooms collapse when nutrients in the water column are exhausted (Wetzel

1983).


31

Blooms are a natural phenomenon, however, it is believed that recent increases in the

frequency and intensity of blooms are a result of increased nutrient inputs from

anthropogenic sources to waterways (May 1981; Melkonian 1991; Smayda 1993).

Additional nutrients may in some cases lead to prolific phytoplankton growth, causing

severe side-effects (Wetzel 1983).

During the period of exponential growth of a bloom, the water column is likely to be

super-saturated with respect to oxygen. Following this stage, however, very high rates

of respiration by dense blooms may result in deoxygenation in the bloom region. This

is especially so when light levels are too low for phytoplankton to balance their

consumption with oxygen production by photosynthesis (Hallegraeff 1993). Once

nutrients are exhausted and the bloom collapses, subsequent bacterial degradation also

results in high oxygen consumption, and hypoxia may occur throughout the water

column (Wetzel 1983). Serious ecological consequences include the asphyxiation of

zooplankton, benthic invertebrates, and fish. These deaths contribute to a positive

feedback effect, by further using dissolved oxygen in their decay, as well as removing

herbivore regulation on primary productivity (D’Elia et al. 1992). Enhanced

eutrophication may eventually result as nutrient removal processes are inhibited or

sediment-nutrient processes are affected (Douglas et al. 1996). Blooms of specific

taxa such as dinoflagellates, the "red-tide" producing species, also have the potential

for human fatalities via bioaccumulation of toxins in fish and shellfish (Gorham 1964;

Bodeanu and Usurelu 1979; Nielsen 1996).


32

2.4 Study site

The Swan River estuary is located in the south-west of Western Australia (Figure 4-1).

The system is the focal point of the city of Perth (31º 56’ S, 115º 51’ E). It receives

water from the Avon and Swan Coastal Catchments, which have a total area of

121,000 km2, and support 1.4 million people (Atkins 1995). The estuary is a

mesotrophic, microtidal salt-wedge estuary with a Mediterranean climate of high

seasonal and interannual variability.

The soil of the Swan Coastal Plain catchment is formed largely of depositional

material (Bettenay 1977). Further upstream, the Avon River occupies the Darling

Plateau, formed of gneisses and granites underneath shallow depositional material.

Land use in the Avon Catchment is largely agricultural, while the lower Swan Coastal

Catchment is approximately half agriculture (47%), but with significant urban (18%),

open space (31%), and industrial (4%) areas (Swan River Trust 1999).

Relatively little of the original catchment vegetation remains in the Swan-Canning

catchment. Clearing, agriculture and urbanization have altered most of the coastal

plain. Some tuart (Eucalyptus gomphocephala), jarrah (Eucalyptus marginata) and

marri (Eucalyptus calophylla) woodland remains, with additional heath and scrub areas

(Thurlow et al. 1986).

The Avon River contributes approximately 60% of the Swan River’s flow (Hamilton et

al. 2001) and becomes the Swan River at its confluence with Wooroloo Brook. Other

major tributaries include the Dale, Mortlock and Brockman Rivers, and Toodyay

Brook which flow into the Avon River upstream of the coastal plain. Ellen Brook and


33

the Helena and Canning Rivers flow directly into the Swan River on the coastal plain.

Smaller, seasonal tributaries include Bennett, Susannah and Jane Brooks on the Swan,

and Yule, Ellis, and Bickley Brook on the Canning. Urban storm-water drains may

also contribute significantly to the freshwater and nutrient loads of the estuary (Peters

and Donohue 2001, Donohue et al. 2001). Coastal plain rainfall is also a significant

source of fresh water to the system (Donohue et al. 2001). A large portion of

freshwater flow in the Swan and Canning Rivers is extracted or impounded upstream

of Mundaring and Kent Street Weirs respectively.

Development and settlement of the Swan-Canning catchment has drastically altered

nitrogen and phosphorus availability in the river. Clearing of trees, drainage of

wetlands, changes in land use, use of fertilizers, and increased domestic and industrial

wastes have all enhanced nitrogen, phosphorus and carbon inputs. Fertilizers are a

major input, and are readily bioavailable, while clearing has allowed easier export

from catchment to watercourse (Deeley et al. 1993; Peters and Donohue 2001).

Nutrient studies have focused on the situation in the Swan River.

The majority of nutrients are carried with the major inflows from the Avon River

(approximately 35% of the total phosphorus load to the estuary) and Ellen Brook (30%

of the phosphorus load). Phosphorus from the Avon is mostly chemically bound to

particles, in contrast to the dominance of soluble phosphorus from Ellen Brook

(Donohue et al. 1994; Peters and Donohue 2001). With the first autumn-winter flush

of the catchment, nutrient levels increase. Nitrogen increases are particularly marked,

primarily as nitrate in the freshwater inflow (Douglas et al. 1996), and possibly as

ammonium and nitrite in groundwater flow (Douglas et al. 1996; Linderfelt and Turner


34

2001). The effect of stratification on nutrient cycling and distributions is also of

interest in the Swan River. Douglas et al. (1996) found ammonium concentrations at

the bottom of the deep pool near Ron Courtney Island (Figure 3-1) could be an order

of magnitude higher than those at the surface, while nitrate concentrations are low

throughout the water column.

Phytoplankton productivity in the Swan River has been demonstrated to be nitrogen

limited (John 1987; Thompson and Hosja 1996; Douglas et al. 1996). Nitrogen

limitation appears to be pronounced in summer, and may be up to 30 times greater than

the potential phosphorus limitation (Thompson and Hosja 1996). From June to

September, however, Thompson and Hosja (1996) found nitrogen and phosphorus

were approximately equal in potential to limit phytoplankton growth.

John (1987) identified 79 genera of diatoms (including species of the genus

Skeletonema, Cyclotella, Chaetoceros, Entomoneis and Nitzschia) in the Swan River

estuary. Other major groups include dinoflagellates (e.g. Prorocentrum, Gyrodinium,

Oxyrrhis, Scrippsiella, Peridinium and Katodinium), chlorophytes (e.g.

Chlamydomonas), cryptophytes (e.g. Cryptomonas), euglenophytes (e.g. Euglena) and

chrysophytes (e.g. Apedinella).

Seasonal phytoplankton blooms in the upper Swan have exceeded 106 cells mL-1, and

collapse of these blooms has coincided with hypoxia and significant kills of fish and

benthic invertebrates (Hosja and Deeley 1994). Most recently, in autumn of 2003, a

dinoflagellate bloom of Karlodinium micrum caused a large part of the upper reaches

of the Swan River to become anoxic, resulting in strong odours and fish kills (Swan


35

River Trust 2003). Other bloom species in the Swan include the dinoflagellates

Gymnodinium simplex which has occurred at up to 2 x 106 cells mL-1 (February 1994),

Prorocentrum minimum (up to 2 x 106 cells mL-1, October 1988 and February 1992)

and Prorocentrum dentatum (3 x 105 cells mL-1, May 1993).

Although toxins have previously rarely been detected in the Swan River system (Hosja

and Deeley 1994), a number of potentially toxic dinoflagellate species occur. These

include Gonyaulax catenella, Gonyaulax acatenella, Gonyaulax monilata, Gonyaulax

tamarensis, Gymnodinium breve and Prorocentrum minimum (Schantz 1981).

There has been an apparent decline in the water quality of the Swan River over recent

decades, with increasing debris, rubbish, dead fish, phytoplankton blooms, and

nutrients being reported (Atkins 1995), although there have been some signs of

improvements over the past few years with the implementation of the Swan-Canning

Cleanup Program (Swan River Trust 1999). Recent phytoplankton blooms (e.g.

Robson and Hamilton 2003), resulting in fish kills in the upper Swan River and health

warnings in the Swan and Canning, have brought public attention to the water quality

in the estuary, and spurred the search for prevention of problem blooms.

There are strong population pressures within the region and immediately surrounding

the estuary. This provides potential problems for management of the estuary as well as

a challenge to maintain recreational, aesthetic, conservational and fishing values for

Perth and beyond.


36

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Riccardi, N. and Mangoni, M. (1996). Chemical consequences of oxygenation in a shallow eutrophic lake studied

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Robson, B.J., and Hamilton, D.P. (2003). Summer flow event induces a cyanobacterial bloom in a seasonal

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Chapter 3. Field data analysis

40

3 Analysis of the effects of physico-chemical factors

on the Swan River estuary phytoplankton

succession and biomass in the field

T. U. Chan and D. P. Hamilton

Mar. Freshwater Res., 52, 869-884. 2001.

3.1 Abstract

Physico-chemical factors affecting phytoplankton succession and dynamics are examined in

the upper Swan River estuary, Western Australia. Freshwater discharge affects the residence

time available for different phytoplankton taxa to grow, according to their different rates of

cell growth. It also influences succession between marine, estuarine and freshwater

phytoplankton taxa according to the extent that it hinders intrusion of marine water into the

estuary. The three major phytoplankton groups, diatoms, dinoflagellates and chlorophytes,

are strongly separated temporally by season, and spatially along the Swan River estuary

according to flow and salinity. Diatoms exhibit the widest range of maximum potential

growth rates and occur under a wide range of discharges as a result of alternation between

freshwater and estuarine species. Dinoflagellates, dominated by relatively few brackish water

species, have the lowest growth rates, and occur only at very low discharges. Chlorophytes,

dominated by Chlamydomonas globulosa, are intermediate in their potential growth rates, and

are restricted to freshwater conditions. In the Swan River estuary, nutrients appear to be less

important than flow and salinity in regulating phytoplankton succession and biomass,

although seasonally averaged data reveal a significant correlation between dissolved

inorganic nitrogen concentrations in winter and phytoplankton biomass in the following

spring. It is highly likely that anthropogenic effects on freshwater discharge to Australian

estuaries have had a significant impact on composition and biomass of phytoplankton

communities. Control of freshwater discharge thus has the potential to have a significant


41

impact on species assemblages in estuaries and may allow at least partial control of

phytoplankton bloom potential and eutrophication.

3.2 Introduction

The development of phytoplankton blooms in estuaries is closely linked to advection

and mixing rates (Cloern 1996; Eldridge and Sieracki 1993), availability of nutrients

(Egge and Asknes 1992; Ornolfsdottir et al. 2004), light (Cloern 1987), temperature

(Nielsen 1996), grazing rates and the interactions amongst these factors (Marshall and

Alden 1990). The effect of growth limiting nutrients on phytoplankton has been a

specific focus of many studies (e.g. Fisher et al. 1988; D’Elia et al. 1992; Cooper and

Brush 1993), particularly in view of links between recent worldwide increases in the

frequency and intensity of blooms in estuaries and elevated nutrient inputs (May 1981;

Smayda 1990; Melkonian 1991).

Nutrients enter estuaries in runoff from the catchment (Jordan et al. 1997) and may be

recycled in the water column, transformed to atmospheric forms (Seitzinger 1988),

transported to coastal waters, or temporarily or permanently bound in estuarine

sediments. Internal recycling of nutrients from the sediments may occur under anoxic

conditions, which are often associated with density stratification in estuaries (Bulleid

1984; Anderson 1986; D’Elia et al. 1992).

Phytoplankton growth is also affected by the specific hydrodynamic conditions of

estuaries. For example, blooms may be initiated by changes in density-driven

circulation, carrying accumulated phytoplankton biomass landward, or by saltwater

intrusions allowing germination of phytoplankton cysts from the bed (Malone et al.

1988; Figueiras and Pazos 1991; Cloern 1996). The stability of buoyancy fronts


42

occurring at salt wedge interfaces may also promote growth, and produce zones of

accumulation for phytoplankton (Cloern and Nichols 1985; Franks 1992). Under

micro-tidal conditions, the stratification which develops encourages blooms by

reducing turbulent diffusive loss of phytoplankton cells from the euphotic zone (Koseff

et al. 1993) and by deepening the euphotic zone (Cloern 1996). But at the most

fundamental level, flow directly controls phytoplankton growth via flushing and the

advection of cells from the estuary to the ocean.

It is the interaction of advection with growth and loss rates of phytoplankton which

results in a given phytoplankton biomass in the water column. Bloom development

requires that net growth be faster than the hydraulic residence time (Alpine and Cloern

1992). Thus the relative growth rates of different taxa of phytoplankton are important

in the species succession under various hydrodynamic regimes (Malone et al. 1988).

The roles of flow and nutrients in phytoplankton development, however, are not

generally independent. Inflows provide nutrients for blooms (Jassby et al. 1993; Eyre

and Twigg 1997; Thompson 1998), but increased flushing may prevent accumulation

of high biomass despite high growth rates.

Numerical models are one way to capture the interactions amongst the major factors

controlling phytoplankton bloom development. However, the extensive data

requirements of most interdisciplinary ecological models, including bathymetric,

meteorological, hydrological, tidal and water quality data, generally preclude their

routine use, and limits their application for routine management questions. Thus the

simplified approach we propose here may be highly suitable as a means to predict and

control the taxa and biomass of phytoplankton in estuaries.


43

In this study, we quantify the importance of freshwater discharge to estuaries in

controlling response of phytoplankton communities. Our hypothesis is that flow

regime dictates whether or not a bloom can occur according to growth rate of the

relevant phytoplankton taxa (Alpine and Cloern 1992), while nutrient availability may

govern the potential size of the bloom (Mallin et al. 2004). From a management

viewpoint, one of the aims of this study is to provide some understanding and guidance

in control of problematic blooms, particularly the way in which the flow regime may

be used to regulate species composition and biomass of phytoplankton.

3.2.1 Study Site

The Swan River estuary (Figure 3-1, 31° S, 115° W) receives water from the Avon and

Swan Coastal catchments, which have a total area of 121,000 km2 and where 1.4

million people reside (Thompson and Hosja 1996). The Avon River contributes

approximately 60% of flow to the Swan River, and there are several other major

tributaries and urban drains. Freshwater flow to the Swan River is attenuated to some

extent by extractions for water supply, with impoundments such as Canning River and

Mundaring Weir, which restrict saltwater intrusion or act as reservoirs for water supply

respectively.

The climate of the catchment is Mediterranean, with hot, dry summers, and mild, wet

winters. Rainfall is highly seasonal, with more than 90% occurring between April and

October (Hillman et al. 1995). Flow is similarly skewed, and lags rainfall by about one

month (Thompson and Hosja 1996). Under low flow conditions in summer, the

estuarine portion of the Swan River can extend up to 60 km upstream of the ocean

(Spencer 1956). The lower 20 km of the estuary are generally wide and moderately


44

deep, with some lateral constrictions. This downstream region is generally well flushed

by tidal sloshing, and few water quality problems have been documented (Stephens

and Imberger 1996), although a major cyanobacterial bloom comprised of Microcystis

aeruginosa occurred in this region in February 2000 (Hamilton, 2000). Upstream, the

estuary becomes narrow, shallow and poorly flushed. Water quality problems, in

particular phytoplankton blooms and hypoxia, are frequent in the upper reaches

(Thompson and Hosja 1996; Hamilton et al. 1999).

Research and management undertaken into phytoplankton blooms in the Swan River

estuary have focused on the role of nutrients (John 1994; Thompson and Hosja 1996;

Thompson 1998). There is pronounced nitrogen limitation, especially in summer when

nuisance blooms occur, but approximately equal potential for limitation by nitrogen or

phosphorus from June to September (Thompson and Hosja 1996).

Nutrient loads and concentrations increase with the onset of the first rains of the wet

season in late autumn or early winter. Increases in nitrogen are particularly prominent,

occurring primarily as nitrate in the freshwater inflow (Peters and Donohue 2001).

Thompson (1998) found that rainfall events leading to increased nitrate levels in the

estuary promote subsequent phytoplankton blooms, and suggested that rainfall is

critical in the occurrence of blooms.

Phytoplankton succession in the upper Swan River estuary is highly seasonal, and

follows a typical temperate estuarine cycle. Freshwater diatoms (e.g. Cyclotella,

Nitzschia) dominate the winter phytoplankton community. The largest bloom usually

occurs in spring, dominated by fast growing chlorophytes (Chlamydomonas). With the


45

salt-wedge intrusion, chlorophytes are succeeded by slower growing marine diatoms

(e.g. Skeletonema) and dinoflagellates (Prorocentrum, Gymnodinium and Gyrodinium)

in summer and autumn (Day 1989; John 1994; Thompson and Hosja 1996).

3.3 Methods

From October 1994 to July 1998, data were collected on a weekly basis by the Water and Rivers

Commission of Western Australia, at nine sites along a 30 km stretch of the Swan River estuary, from

Blackwall Reach up to Success Hill Reserve just above the confluence with Helena River (Figure 3-1).

Vertical profiles were taken at 0.5 m intervals for salinity, dissolved oxygen (DO) and temperature with

a Hydrolab Datasonde multiprobe logger. Secchi disk depths (Zsd) were also recorded, and euphotic

depths (Zeu) were estimated according to Zeu = -Zsd ln(0.01)/1.44 (Kirk 1994).

Water samples were taken at the surface, 1 m depth, and bottom (0.5 m from the bed) by pumping water

to the surface for distribution into pre-washed 500 mL polyethylene containers. Samples were

immediately divided in two and one sub-sample (100 mL) of each pair was filtered through 0.45 µm

cellulose nitrate filter paper before placing both subsamples, and filter paper (protected from light), on

ice. Ammonium was analysed on filtered samples by reaction with phenol, hypochlorite and sodium

nitroferricyanide before measurement of absorbance at 640 nm (Greenberg et al. 1992, Standard Method

4500-NH3). Nitrate + nitrite was analysed using reaction with cadmium in acidic solution before

addition of N-(1-napthyl) ethylenediamine dihydrochloride and measurement of absorbance at 540 nm

(Greenberg et al. 1992, Standard Method 4500-NO3). Filterable reactive phosphorus (FRP) samples

were analysed by reacting filtered samples with ammonium molybdate, potassium antimonyl tartrate

and ascorbic acid, and measuring absorbance at 880 nm (Greenberg et al. 1992, Standard Method 4500-

P). Total nitrogen (TN) analysis was carried out on unfiltered samples by digesting with alkaline

persulphate and then analysing as for nitrate. TP samples were initially digested with sulphuric acid and

potassium persulphate and then analysed as for FRP.


46

The filter paper was ground and chlorophyll was extracted in aqueous acetone solution. The

fluorescence of the extract before and after acidification with HCl was then measured, and converted to

chlorophyll a (Greenberg et al. 1992, Standard Method 10200-H).

Phytoplankton counts were made from depth-integrated triplicate water samples, taken with a

polyethylene hosepipe sampler to within 0.5 m of the bed or to 6 m depth. Samples were preserved with

Lugol’s solution at a ratio of 1:100. Counts and identification to genus or family level were carried out

on 1 mL subsamples, to 300 cells or ten grids on a Sedgwick Rafter Cell at 125 to 200 times

magnification.

Daily flow data was taken from Water and Rivers Commission Regional Services gauges and was

summed for the five major tributaries, representing more than 98% of the drainage area (Peters and

Donohue 2001). The tributaries included Avon River, Ellen Brook, Helena River, Jane Brook, and

Susannah Brook.

3.4 Results

3.4.1 Salinity

Surface and near-bed salinities are plotted for the period October 1994 to July 1998 for

the 9 stations sampled over the lower 35 km of the Swan River estuary (Figure 3-1).

Each coloured grid-square in Plate 3-I and in Plate 3-II represents a data point

corresponding to a station sampled in the weekly run. Smoothing of this data was not

performed, to avoid potentially misleading effects. There were 12 separate occasions

over the 3.5 year period when the weekly run was not conducted. These data were

filled using a simple linear interpolation between the adjacent weekly runs at identical

stations. There was one period when the missing data extended for a significant period,

as indicated by the darkened region in the lower estuary from winter 1997 to autumn


47

1998 (Plate 3-I b). In the upper reaches, the salinity pattern typically varies from fresh

(salinity < 4 psu) during the winter rains (July-September), to substantial vertical

stratification in spring and summer, and near-marine salinities in late summer and

autumn (salinity > 30). A particularly dry year is evident in 1997 when surface

salinities below the Narrows remained at 10-20 even in the wettest part of the year,

compared with 5-10 at similar times in the previous two years. Similarly, in 1997 high

near-bed salinity persisted throughout the winter, indicating freshwater discharges

were insufficient to fully flush salt water located in the water column below the

Narrows. Occasional runoff events in summer and autumn are evident as intermittent

decreases of salinity at the water surface in some regions (Plate 3-I a).

Substantial stratification of salinity though the water column (> 10 difference from

surface to bed) is restricted to a short period at the start of the winter rains, when flow

recommences, and a longer period in spring when the salt wedge propagates upstream

as freshwater flow recedes.

3.4.2 Phytoplankton composition

Spatial and temporal variations in the distribution of the three dominant phytoplankton

groups are illustrated in Plate 3-II a-c. Diatoms form blooms (densities above an

arbitrary threshold of 10,000 cells mL-1) throughout the year, in both the lower and

upper estuary (Plate 3-II a). In the lower reaches, blooms of Skeletonema costatum

occur in the winter and spring, and move upstream with the salt wedge during spring.

In the upper reaches, S. costatum and, occasionally, Thalassiosira sp. blooms occur

during early summer, while freshwater dinoflagellates (Cyclotella and Nitszchia sp.)

appear intermittently at other times.


48

The seasonal succession of phytoplankton in the upper estuary is characterized by a

freshwater chlorophyte bloom in spring (Plate 3-II b), which is almost invariably

dominated by Chlamydomonas globulosa. This bloom regularly exceeds 50,000 cells

mL-1, and is generally associated with the peak annual biomass, measured as

chlorophyll a concentration (Plate 3-II d). Chlorophyte blooms are confined to the

Upper Swan River estuary, upstream of the salt wedge, and occur over a relatively

short period of time, usually less than 5 weeks.

Summer diatom blooms follow the spring chlorophyte bloom in the upper reaches and

are generally succeeded by dinoflagellate blooms in late summer (Plate 3-II c). The

dinoflagellate blooms are dominated by one or more of the following species:

Prorocentrum minimum and P. dentatum, Gymnodinium simplex, Gyrodinium

uncatenum and Scripsiella spp.

During the monitored period, all phytoplankton blooms except those of estuarine

diatoms, such as S. costatum, were restricted to the upper estuary, above the Narrows

(Figure 3-1) where we focused the remainder of the study. In this region, comparisons

of the seasonal and relative abundance of phytoplankton taxa reveal significant

differences (Plate 3-II e). Diatom cell counts remain relatively constant through time

while those of all other groups vary seasonally and show pronounced reductions during

the high-flow winter period. Dinoflagellates and chlorophytes are the two other

dominant phytoplankton groups, with chlorophyte peaks associated with spring, after

the peak tributary discharge, and dinoflagellate peaks generally during summer and

autumn. Cryptophytes, chrysophytes, euglenophytes, dictyophytes and rapidophytes

generally account for only a small percentage of the total cell count.


49

The cell counts were analysed for correlations with flow, and surface and near-bed

salinity, temperature, ammonium, nitrate, DIN (NH4-N + NO3-N), FRP, TN and TP, as

well as against the surface-to-bed differences in salinity.

3.4.3 Physical influences

Total cell densities were not significantly correlated with freshwater discharge, but the

phytoplankton groups were clearly separated according to discharge (Figure 3-2). Cell

densities of diatoms peak at low flows, but moderate densities continue to occur at

flow rates up to 10,000 ML d-1. By contrast, at discharges above 1000 ML d-1 cell

counts of all other phytoplankton groups are negligible. Chlorophyte blooms are

restricted to a flow range from 40 ML d-1 to 1000 ML d-1 and dinoflagellate blooms to

flows less than 15 ML d-1.

The majority of the total annual flow occurs from the end of June to the end of

September (Figure 3-3a), while peak flows usually occur in July or at the beginning of

August. Flows were approximately 3 times lower in 1997 than in 1995 or 1996.

Figure 3-3a shows the relationship between freshwater discharge and salinity at 1m

depth at one station (Nile St) in the upper estuary. There is a general inverse

relationship between these parameters (R2 = 0.60, p < 0.01). There is also hysteresis

over the annual seasonal cycle (Figure 3-3b), where for a given discharge, salinity is

substantially higher in autumn-winter than in spring when the salt wedge intrudes more

slowly back up the estuary.


50

Diatom blooms occur over the widest range of 1m salinities, from 4 to 25 (Figure 3-4),

and a wide range of flow rates are associated with the occurrence of these blooms.

Dinoflagellate blooms occur at salinities from 10 to 29 and chlorophyte blooms are

restricted to salinities of less than 6. Comparison of phytoplankton taxa against near-

bed salinities gave similar results, but with near-bed salinities generally slightly

elevated over corresponding surface values for a given cell count.

Phytoplankton cell densities were not significantly correlated with temperature at 1m

depth (Figure 3-5), although there were ranges of temperature in which different

groups tended to predominate. It is not possible to isolate the effects of temperature,

however, as temperature and discharge are inversely correlated (R2 = 0.45, p < 0.01),

and temperature and salinity are also correlated (R2 = 0.14, p < 0.01). The

differentiation between groups was less clear for temperature than for flow (Figure

3-2), however, or for salinity (Figure 3-3).

Phytoplankton cell densities were not significantly correlated with euphotic depth

(Figure 3-6). Blooms generally occurred only when euphotic depths were between 1

and 3 m, but phytoplankton groups showed no differentiation on the basis of photic

depth. Surface mixed layer depths were estimated from the salinity profiles from the

depth where salinity varied more than 1 between the 0.5m measurements in the vertical

profile. The mixed layer depths were at a minimum of 0.5 to 1m in late summer to

autumn, when maximum photic depths also tended to occur. The ratio of surface mixed

layer depth (Zm) to euphotic depth (Zeu) is generally small (< 2) and the potential for

light limitation is considered to be low (Scheffer 1998). The exception was the first

annual winter flush when the ratio occasionally exceeded 4.


51

3.4.4 Nutrients

Phytoplankton blooms are confined almost entirely to low surface DIN levels (< 0.2

mg L-1, or 1.4x101 µmol) relative to the range of DIN concentrations recorded (Figure

3-7a). By contrast, blooms are more widely scattered over the range of measured

surface FRP concentrations (0 to 0.18 mg L-1, 0 to 5.8 µmol, Figure 3-7c). The trend is

similar for near-bed DIN (Figure 3-7b) and FRP (Figure 3-7d) concentrations, but with

a wider spread of nutrient concentrations, particularly for FRP.

There is little separation of the different phytoplankton groups with respect to levels of

DIN in the surface or near-bed (Figure 3-7a-b). However, large blooms of

dinoflagellates (> 30,000 cells mL-1) occur only when concentrations of FRP exceeded

0.05 mg L-1 (1.6 µmol).

The relationship between phytoplankton and nutrient loading (Figure 3-8) is similar to

that of phytoplankton and flow. The conversion to loading was performed by

interpolating nutrient concentrations to a daily timestep and multiplying by the

measured daily flow rates, and summing at appropriate monthly, seasonal and annual

intervals. This transformation aligns data along the axes, indicating that high

concentrations of nutrients are associated with high flows, and low cell counts.

Dinoflagellate blooms occur at low loadings (0 to 1 kg DIN d-1, and 0 to 0.5 kg FRP

d-1), while chlorophyte blooms occur at higher loadings of 1 to 200 kg DIN d-1 and 0.2

to 30 kg FRP d-1. Diatom blooms are more broadly spread with respect to nutrient

loadings, and occur from 0 to 1500 kg DIN d-1 and 0 to 100 kg FRP d-1.


52

Figure 3-9a-b show initial peaks in DIN concentration which coincide with the first

substantial freshwater into the estuary. FRP concentrations (Figure 3-9c-d) are more

constant over time, but have the highest concentrations well before the first flush.

In late summer to autumn, when flow rates were low (< 50 ML d-1), there was a wide

range of variation in surface and near-bed FRP and DIN concentrations (Figure 3-10).

The first major annual flush at the beginning of winter reaches ~ 5000 ML d-1; this

corresponds to a residence time of about 1 day in the upper Swan River estuary. The

highest dissolved nutrient concentrations (FRP > 0.1 mg L-1, 3.2 µmol, DIN > 1.5 mg

L-1, 1.1x102 µmol) and variability of concentrations occur during low to moderate

flows of up to 5000 ML d-1, particularly near the bed. At high flow rates (> 5000 ML

d-1), typical of winter, surface DIN and FRP concentrations are weakly related to flow

(R2 = 0.209, p < 0.01 and R2 = 0.211, p < 0.01 respectively), and for near-bed waters,

only DIN is weakly related to flow (R2 = 0.275, p < 0.01). For flows < 5000 ML d-1,

flow is weakly related to DIN (R2 = 0.258, p < 0.01 at the surface, and R2 = 0.237, p <

0.01 at the bed), but not to surface or near-bed FRP. Increases in DIN with winter

flows consisted mostly of increased nitrate+nitrite, while increases in late summer to

autumn were predominantly due to increased ammonium. Near-bed waters tended to

have slightly higher ammonium concentrations than corresponding surface samples.

Peak loads in DIN correspond to peaks in both concentration and flow (Figure 3-9).

Peaks in FRP load correspond largely to flow peaks. Surface and near-bed differences

in nutrient concentration are dampened by conversion to load.


53

3.4.5 Seasonal averages

Phytoplankton cell counts for all stations in the upper Swan River estuary were

averaged over seasons. The water column nutrient concentrations were also averaged

seasonally. Some repeated annual trends are evident (Figure 3-11). Peaks of DIN occur

in winter (July-September) and minima occur in summer (January-March). Nitrate

concentrations are significantly correlated with flow (R2 = 0.72, p < 0.01), while DIN

is more weakly related to flow (R2 = 0.50, p < 0.01), reflecting the absence of a

significant relationship between ammonium and flow. Annual cycles of FRP are less

clear, but minima usually occur in winter or spring, while peaks occur in summer or

autumn (Figure 3-11b).

Peak cell counts consistently occur in spring, generally lagging the peak flow and peak

DIN by one season. While there was no relationship between individual taxa cell

counts and discharge in the corresponding season, there was a highly significant

relationship (R2 = 0.88, p < 0.01) between counts of chlorophytes and flow in the

preceding season (e.g. winter flow vs. spring cell counts). The correlation between

nitrate concentration and chlorophyte density, lagged by one season, was also

significant (R2 = 0.46, p < 0.01), reflecting the close relationship of nitrate and flow

described previously. Diatom numbers generally peak one to two seasons after the

annual flow peak, and dinoflagellates two seasons afterward, but correlations between

flow and these two phytoplankton groups were not significant. Correlations between

the different phytoplankton groups were also not significant.


54

3.5 Discussion

3.5.1 Physical influences

River flow is the most robust single predictor of phytoplankton bloom dynamics in the

Swan River estuary. It affects biomass physically by flushing cells from the estuary, as

well as controlling the salinity gradients to which cells are exposed. Under high

discharges, not even the fastest growing phytoplankton taxa have doubling rates great

enough to allow bloom formation. Under lower discharges, specific phytoplankton

groups may be favoured; both directly, based on the relative rates of advection and cell

multiplication, and indirectly, through interrelated physico-chemical factors,

particularly salinity.

Laboratory growth rates cited in the literature for different phytoplankton taxa

correspond generally to the in situ trends observed in this study (Figure 3-2). Diatoms

have the widest range of maximum growth rates, from 0.4 doublings day-1 (Wheeler et

al. 1974) up to 5 doublings day-1 (Eppley et al. 1971; Furnas 1991). Growth rates of

Skeletonema costatum are intermediate, at about 2 doublings day-1 (Fogg 1966). Thus

diatoms occur across the widest range of flow rates as indicated in Figure 3-2.

Measured growth rates of Chlamydomonas spp., the dominant chlorophyte in the Swan

River estuary, are between 0.5 (Wheeler et al. 1974) and 3.8 doublings day-1

(Jorgensen 1979). Dinoflagellates have the lowest magnitude and narrowest range of

growth rates, from 0.3 doublings day-1 (Gymnodinium sp.; Bjornsen and Kuparinen

1991) to 0.7 doublings day-1 (Prorocentrum sp.; Eppley et al. 1971; Chang and

Carpenter 1991).


55

On the basis of freshwater discharge, residence times for the upper Swan River estuary

between Nile St and Success Hill (Figure 3-1) range from <0.2 days during peak

winter flow to more than a year during summer. The typical residence time of ~ 0.3

days corresponding to mid-winter (July-August) is lower than the time required by any

of the phytoplankton taxa to double biomass, so biomass remains low. When residence

times increase to >1 day at the end of autumn or beginning of spring, diatoms blooms

typically begin to occur. For residence times of 3 to 7 days, typically in spring as

winter flow subsides but when the water column is still fresh, chlorophytes dominate.

This range of residence time also occurs at the end of autumn, but only briefly as

discharge usually increases rapidly with the onset of the annual winter rains.

Dinoflagellate blooms only occur at very low flows, with the associated long residence

times (on the order of months based on freshwater discharge) providing the time

required for cell densities to reach bloom levels.

During spring and summer the salt wedge propagates upstream and there is a transition

from advection dominated by river inflow to domination by tides. The time for

flushing due to tides was calculated using a tidal prism (Dyer 1997) based on tidal

amplitudes and excursions. The tidal prism is the three-dimensional shape of the

oceanic water within a river or estuary as it moves up the channel. The tides

correspond to a minimum of 4 days to flush the upper reaches. Tides would therefore

begin to dominate flushing in the estuary during spring. However, this calculation

overestimates mixing in the intertidal region, does not take into account re-entry of

water previously discharged on the ebb tide, and ignores the relatively localized effect

of the tides at the downstream end of the upper estuary. Residence times under summer

low-flow conditions are therefore likely to be typically on the order of several weeks.


56

The transition to tide-dominated advection is thus likely to occur in summer and

correspond with the transition to slower-growing dinoflagellates.

In addition to the direct flushing of cells by flow, there are a number of associated

factors that affect phytoplankton succession and biomass, including recirculation,

turbulence, stratification, water clarity, salinity, and nutrient availability.

Under stable, moderate discharge regimes, the position of an intruding salt wedge is

maintained in an estuary, and there is a convergence zone which entraps and

accumulates phytoplankton biomass (Peterson et al. 1975). Cloern et al. (1983)

developed a conceptual model combining this circulation pattern with a phytoplankton

kinetics model to explain the development of annual phytoplankton blooms in a

particular region of north San Francisco Bay. In the Swan River estuary, however, the

extreme nature of the annual discharge cycle results in the wedge either being rapidly

pushed out of the upper reaches (as in winter), or undergoing a progressive net

upstream propagation (Kurup et al. 1998). This would tend to prevent the

establishment of a stationary null zone on seasonal timescales. Plate 3-IIa - c do not

show a favoured location for occurrence of diatom or dinoflagellate blooms. The

annual chlorophyte blooms, however, tend to occur relatively regularly in spring from

about 5-20km upstream of the Narrows, although the similarity in the bloom position

in 1996 and 1997 does not reflect variations in flow and salt wedge position between

these two years. Chlorophyte blooms are thus unlikely to be associated with a stable

convergent zone via the mechanism described by Cloern et al. (1983). However, the

convergent zone at the salt wedge interface may still be important in nutrient and

phytoplankton dynamics in the estuary and should be explored further.


57

Flow may indirectly affect phytoplankton composition and biomass through its effect

on turbulence. Sherman et al. (1998) found that turbulence provides an advantage to

diatoms via resuspension of sinking cells, allowing them to dominate at high flows.

The reduced numbers of chlorophytes and dinoflagellates at higher flows (Figure 3-2),

presumably when turbulence is greater, may also provide anecdotal evidence to

support the intolerance to turbulence of these two groups (Gibson and Thomas 1995;

Hondzo and Lyn 1999). Turbulence may also result in changes in the light regime that

phytoplankton experience.

Salinity may regulate phytoplankton growth via osmotic and ionic stress and

associated changes in cellular ionic ratios (Kirst 1989). Kondo et al. (1990) found that

under brackish conditions, many blooms (including Skeletonema costatum,

Prorocentrum minimum and Cyclotella spp.) were more strongly controlled by salinity

than by temperature.

Our results (Figure 3-3) reflect the salinity tolerances of the different phytoplankton

groups found in the literature (Kirst 1989). Diatoms occur over a wide range of

salinities, with tolerances of the cosmopolitan coastal-estuarine S. costatum, for

example, ranging from at least 7 to 26 (Ravail and Robert 1985; Tadros and Johansen

1988). Diatom blooms occur in the Swan River estuary at salinities from 4 to 28, with

an apparent decline in bloom frequency at intermediate salinities of 7 to 12. These

salinities may correspond to where there is a transition between freshwater and

estuarine species, although we were not able to confirm this due to limited

identification to species level. Marine and estuarine dinoflagellate salinity tolerances


58

found in the literature range from about 10 to 34 (e.g. Gymnodinium spp., Nielsen

1996), which is a little higher than the 7 to 29 range under which dinoflagellate blooms

occur in the Swan River estuary. Limited identification to species level precluded

exploration of transitions between freshwater, estuarine or marine species, but there

were few freshwater dinoflagellates. The relationship of chlorophyte biomass to

salinity indicated that freshwater species (usually Chlamydomonas globulosa) are

dominant, and only form blooms when the salinity is less than 6.

Phytoplankton succession and blooms were not related to salinity stratification. The

interrelated processes of salinity stratification, hypoxia and sediment nutrient release in

the Swan River (Douglas et al. 1996) did not produce obvious transitions from non-

motile to motile phytoplankton, as might have been expected from the competitive

advantages conferred on flagellated species under stratified conditions and low

turbulence (Eppley et al. 1977; Hamilton et al. 1999).

Both stratification and river discharge may also affect the light climate in estuaries.

Wetsteyn and Bakker (1991) found reduced turbidity and increased light penetration

resulted in increases in chlorophyll a in the Oosterschelde Estuary in the Netherlands

under reduced flow conditions, despite concurrent reductions in nutrient

concentrations. Similarly, in San Francisco Bay, Cloern (1991) attributed the increase

in phytoplankton primary production with increased discharge to the positive effect of

flow on stratification and increased light exposure of phytoplankton above the

pycnocline. In the upper Swan River estuary, water clarity is maximal in autumn, just

before the annual rains (Thompson 1998), and incident light peaks during summer

(Hillman et al. 1995). However, peak phytoplankton biomass occurs in spring and


59

summer, and does not increase as the surface mixed layer depth to euphotic depth ratio

increases (Figure 3-6), as may be expected if light was limiting (Richardson et al.

1983). There is also no clear separation of the different phytoplankton groups

according to light regime, with all blooms occurring at euphotic depths of 1 to 3 m.

Light limitation may potentially account for reduced biomass for a period of about 3

weeks in early winter, when the Zm:Zeu ratio exceeded 4 (Talling 1971). This period

corresponded to the first flush of high turbidity water and deepening mixed layer

depth. Long-term records of turbidity, light, and mixed layer depth, together with algal

physiological studies, are required in conjunction with measurements of the factors

examined in this study, in order to differentiate the direct effects of flow and the

indirect effects on stratification and light on phytoplankton bloom development.

3.5.2 Nutrients

Vertical density stratification is closely linked to flow in the Swan River estuary

(Kurup et al. 1998). After the freshwater flush in winter, the net movement of the salt

wedge in the estuary is generally upstream until the next annual rains occur. The

subsequent extended period of low flow conditions and concomitant stratification in

summer and autumn encourage development of hypoxia (Malone et al.1988), and

associated changes, including sediment release of ammonium and phosphorus

(Douglas et al. 1996), which may stimulate phytoplankton blooms. Peak inorganic

nutrient concentrations, for example, occurred near the bed under the reduced flows of

summer and autumn (Figure 3-10b and d). During this time the DIN pool is dominated

by ammonium, whereas in winter nitrate is dominant.


60

Nitrogen is the limiting nutrient in the Swan River during summer, and may be up to

20 times more limiting than phosphorus (Thompson 1998). However, there is little

separation of the different phytoplankton groups with respect to DIN concentrations, at

either the surface or near-bed (Figure 3-7a and b), which suggests that factors other

than nutrients are controlling phytoplankton succession. Thompson (1996) found that

maximum nutrient concentrations occur in winter (June-August), which does not

coincide with the peak biomass later in the year (October-December). The lack of a

relationship between the ultimate size of phytoplankton blooms and maximum nutrient

availability is thus likely to be due to influence by another factor such as flow.

Most of the observed dinoflagellate blooms occurred at higher concentrations of FRP

than diatom or chlorophyte blooms (Figure 3-7c and d). During summer and autumn,

when dinoflagellate blooms occur, inability of phytoplankton to assimilate phosphate

due to extreme nitrogen limitation (Thompson 1998) is likely to result in unassimilated

phosphorus, and produce the observed increases in water column FRP.

The similarity in the relationships of phytoplankton cell counts to nutrient loading

(Figure 3-8) and phytoplankton to flow (Figure 3-2) are expected, as flow varies across

six orders of magnitude, whereas nutrient concentrations vary by less than three orders

of magnitude. The extreme nature of the flow regime disguises the effects of nutrients

on phytoplankton cell counts and biomass.

Traditional concepts of phytoplankton bloom regulation are derived from models for

standing waters (e.g. Harris 1986) that are based on the concept that nutrients regulate

biomass. In the case of the Swan River estuary, there is no clear relationship between


61

DIN, the most frequently limiting nutrient, and cell numbers. The direct and indirect

influences of physical factors on biomass as well as feedbacks between nutrient

assimilation and biomass clearly complicate predictive relationships in estuaries.

3.5.3 Seasonal averages

The seasonal averages of nutrients, discharge and phytoplankton cell counts indicate

only three clearly related variables. Inter-relationships of flow, nitrate, and

chlorophytes (lagged by one season) suggest that nutrients (nitrate in this case) carried

into the system in winter flows partly determine the magnitude of subsequent spring

chlorophyte blooms. It is hypothesized that nitrogen stored from the winter nitrogen

load in estuarine sediment is released under hypoxic conditions the following spring,

enhancing productivity of phytoplankton. However, once nitrogen enters the biota in

spring, tracking its fate becomes more complex due to changes in its form and

location, with multiple pathways (sediments, water column, phytoplankton), and

differing timescales affecting its cycling. This complex processing may disguise

relationships with the diatoms and dinoflagellates, which dominate in the subsequent

1-2 seasons.

3.5.4 Recent developments

The first toxic cyanobacterial bloom was recorded in the Swan River in February 2000

in response to a major rainfall event on January 22, which resulted in relatively fresh

conditions (salinity < 6) throughout the entire surface layer (3-4m) of the estuary

(Water and Rivers Commission, unpublished data). The combination of freshwater,

high temperatures, and irradiance, resulted in a major bloom of Microcystis aeruginosa

(Hamilton 2000). The relatively low growth rate usually attributed to this species (Orr


62

and Jones 1998) indicates that the rapid reduction in flow following the rainfall event

led to a period when the Microcystis cells could grow at close to exponential levels

under near-optimal growth conditions and low salinities, without being flushed out of

the estuary. This event was very unusual as the presence of large amounts of

freshwater are usually associated with high rates of flushing, and fast-growing

chlorophytes generally dominate as the flows recede.

The results from this study suggest that flow is a key determinant of phytoplankton

succession and bloom formation. This may be critical to eutrophication in estuaries,

especially where reduced flows caused by human intervention are likely to result in

increased incidence of bloom forming species. This change may be of particular

relevance to Australian estuaries where much of the freshwater discharge is diverted

for a range of human uses (Davies and Kalish 1994). Understanding the role of flow

may also provide a useful method of manipulating phytoplankton succession and

controlling nuisance blooms in estuaries.

3.6 Acknowledgments

We thank the Water and Rivers Commission for data provided for this study. In

particular we thank, Malcolm Robb for provision of the data, Sarah Grigo and Vas

Hosja at the Phytoplankton Ecology Unit for performing the cell counts, and Ben

Boardman and Kathryn McMahon for information on the collection and status of the

data. The authors also thank Dr Barbara Robson and Dr Ben Hodges for their reviews

of earlier versions of the manuscript, and Professor John Beardall and two anonymous

reviewers for contributing later reviews of the manuscript.


63

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67

3.8 Figures

Figure 3-1. The Swan River estuary and monitoring sites


68

Figure 3-2 to Figure 3-8 all use: Diatoms �, dinoflagellates �, chlorophytes �, cryptophytes �, cyanophytes �, and chlorophyll a x.

Figure 3-2. Flow versus phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary (October 6, 1994 to June 29, 1998). Curves were manually fitted to link the upper bounds of the data in order to denote the flow regimes under which different phytoplankton groups dominate: from left to right, dinoflagellates ( - - ), chlorophytes ( - - - ), and diatoms ( ).


69

Figure 3-3. Freshwater discharge to the Swan River estuary, summed for the Avon River, Ellen Brook, Helena River, Jane Brook, and Susannah Brook for the period January 1995 to July 1998, (a) plotted over time, with corresponding 1 m salinity (- - x - -) at the (arbitrarily chosen) Nile St monitoring station, and (b) plotted against 1m salinity at the Nile St monitoring station. The arrow and curved line indicate the general trend of decrease in salinity with progressively greater flows in autumn (�) and then winter (�), and the line shows the relationship of flow to salinity for the spring (x) samples (Salinity = -2.5 x ln(Flow) + 24.0, R2 = 0.94).


70

Figure 3-4. Surface salinity versus phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary. Curves were manually fitted to link the upper bounds of the data in order to denote the different salinity regimes for each phytoplankton group: from left to right chlorophytes ( - - - ), diatoms ( ), and dinoflagellates ( - - ).

Figure 3-5. Temperature versus phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary (October 6, 1994 to June 29, 1998).


71

Figure 3-6. Euphotic depth versus phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary. Note that data for euphotic depth were measured less frequently than for other parameters.


72

Figure 3-7. Nutrient concentrations versus phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary. (a) Surface DIN, (b) near-bed DIN, (c) surface FRP, and (d) near-bed FRP.


73


74

Figure 3-8. Nutrient loadings vs. phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary. (a) Surface DIN, (b) near-bed DIN, (c) surface FRP, and (d) near-bed FRP.


75


76

Figure 3-9. Nutrient concentrations (——) and loadings ( - - - ) to the upper Swan River estuary from January 1995 to July 1998. (a) Surface DIN, (b) near-bed DIN, (c) surface FRP, (d) near-bed FRP.


77

Figure 3-10. Flow vs. nutrient concentrations in the upper Swan River estuary. (a) Surface DIN (i) For flows <5000 ML d-1 (circles), DIN = 7.4x10-4 x Flow + 0.21, R2 = 0.26, p < 0.01, n=775. (ii) For flows >5000 ML d-1 (diamonds), DIN = 1.7x10-4 x Flow + 1.14, R2 = 0.21, p < 0.01, n=28. (b) Near-bed inorganic nitrogen. (i) For flows <5000 ML d-1 (circles), DIN = 8.2x10-4 x Flow + 0.22, R2=0.24, p < 0.01, n=773. (ii) For flows >5000 ML d-1 (diamonds), DIN = 1.4x10-4 x Flow + 0.68, R2

= 0.28, p < 0.01, n=21. (c) Surface FRP. For flows <5000 ML d-1 (circles), no significant relationship. For flows >5000 ML d-1 (diamonds), FRP = -5.0x10-6 x Flow + 0.11, R2 = 0.21, p < 0.01, n=28. (d) Near-bed FRP, no significant relationship.


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Figure 3-11. Seasonal averages in the upper Swan River estuary (1995 to autumn 1998). (a) Flow (��), nitrate concentrations (�), and chlorophyte cell counts (�). (b) Flow (��), nitrate (�), DIN (x), FRP (*), and ammonium (�) concentrations. (c) Flow (��), diatoms (�), dinoflagellates (x) and chlorophyte (�) cell counts. (d) Flow (��), total cells (�), and chlorophyll a (�).


79

Plate 3-I. Weekly salinity along the Swan River estuary, showing seasonal and longitudinal variation of (a) surface salinity, and (b) near-bed salinity. The mouth of the estuary is at -15000m. Weeks where data was not collected were linearly interpolated from adjacent weeks. The period where data was not collected in the lower estuary is shown by the darkened region from mid-1997 (winter) to the beginning of 1998 (autumn).


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Plate 3-II. Phytoplankton cell counts and biomass in the Swan River estuary (October 6, 1994 to June 29, 1998) showing variations for (a) Diatoms, (b) Chlorophytes, (c) Dinoflagellates, (d) chlorophyll a concentration, and (e) log of cell densities averaged over the upper estuary stations. Note that the order of phytoplankton groups in the legend corresponds to the order in which they are plotted.


81


82

Chapter 4. Hydrodynamic-ecological modelling

83

4 Three-dimensional modelling of processes

controlling phytoplankton dynamics in the Swan

River estuary

T. Chan, D.P. Hamilton, and B.J. Robson

4.1 Abstract

The biomass of four major phytoplankton groups in the Swan River estuary was simulated

with a three-dimensional (3D), coupled hydrodynamic-ecological numerical model.

Medium- and long-term variations in biomass of four phytoplankton groups were captured

with the model simulations though the strong short-term variations in observed biomass were

less predictable. Advection was a strong determinant of phytoplankton succession and

biomass, and played an important role in distributions of other environmental parameters that

influenced phytoplankton growth, particularly salinity and nutrients. Simulations of

phytoplankton physiological parameters indicated that nutrients only occasionally had a

strong regulatory effect on phytoplankton biomass. Comparison of nutrient limitation in the

model with experimental observations using bioassays, indicates that quota modelling may be

a useful means of examining and differentiating nitrogen and phosphorus limitation on

phytoplankton growth.

4.2 Introduction

Coupled hydrodynamic-ecological numerical models are a useful way of integrating

the complex interactions amongst the many factors that control phytoplankton bloom

development (Sheng 2000). A primary drawback to their application, however, is the

detailed data requirements that generally include bathymetric, meteorological,

hydrological, tidal, and water quality inputs. Nevertheless, comprehensive


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environmental reporting requirements (Spitz et al. 1998; Havens and Aumen 2000) and

improvements in aquatic instrumentation (Dickey 1991), together with continued

improvements in desktop computing power, have greatly increased the availability of

detailed input data required for the models as well as the capacity to calibrate and

validate their output.

The majority of aquatic ecosystem modelling has been applied to lacustrine

environments, where hydrodynamics may be simplified to a one-dimensional

approximation to examine only vertical variations (Bierman 1976; Canale et al. 1976).

These models have tended to focus on isolated aspects of an ecosystem, such as

nutrient dynamics (e.g. Chapra 1977) or organism behaviour (Eppley et al. 1971;

Canale and Vogel 1974). Succeeding models were more holistic, incorporating

knowledge of ecosystem biogeochemical processes and interactions, but were often

restricted spatially as a result of simple hydrodynamic representations (e.g. Fasham et

al. 1990; Everbecq et al. 2001).

The high spatial variability of estuaries and advances in knowledge and computing

power have resulted in a progression from one-dimensional (1D) models (Nassehi and

Williams 1986; Savenije 1986) to 2D (Henry et al. 1984; Falconer and Owens 1984;

Hearn and Hunter 1988; Hsu et al. 1998) and 3D models, to adequately simulate

transport and hydrodynamic processes. Additionally, synthesis of fluid dynamics and

biogeochemistry in estuaries is complex, because of the need to combine marine and

freshwater dynamics, couple benthic and pelagic processes (Geyer et al. 2000; Eyre

1993), and resolve strong ecological gradients in both vertical and horizontal

directions. This complexity has resulted in few truly interdisciplinary physical-


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biogeochemical models of estuaries (Hofmann 2000). In addition, many existing

estuarine models focus on tidal flushing and interaction of lower estuarine reaches with

offshore coastal dynamics (Kremer and Nixon 1978), while many aspects of the

influence of fluvial dynamics in upper estuary reaches remain largely unexplored.

The position of phytoplankton in the food web, and the adverse effects of

phytoplankton blooms on estuarine water quality and biota mean that understanding

their dynamics is crucial in managing eutrophication (Paerl 1988; Reynolds et al

2000). Phytoplankton primary production transforms energy and inorganic materials

into organic materials, with significant implications not only for phytoplankton

biomass, but also for cycling of oxygen, carbon dioxide, nutrients, trace elements,

suspended matter, and other organisms (Cloern 2001). Apparent worldwide increases

in the frequency and intensity of phytoplankton blooms (Hallegraeff 1993) have

necessitated more vigilant management and have been the motivation for modelling

studies to simulate the effect of different management techniques (e.g. Jorgensen et al.

1986; Hearn and Robson 2000). A primary goal of these studies is to predict

phytoplankton bloom dynamics, including biomass, frequency and timing of blooms.

However, understanding the succession of different phytoplankton taxa can be critical

to defining bloom dynamics, though relatively few models differentiate the

phytoplankton assemblage as individual classes or species.

The objectives of this study were to apply a numerical model of hydrodynamic and

ecological processes in order to understand the factors driving seasonal and inter-

annual succession of phytoplankton in a microtidal estuary which is strongly influenced

by seasonal fluvial inputs. Our main interest is in the role of nutrient limitation,


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advection, and salinity on phytoplankton succession and biomass. Model simulations

include validation not only against phytoplankton distributions (Chan and Hamilton

2001) but also against the nutrient limitation bioassay results of Thompson (1998).

4.2.1 Study site

The Swan River estuary (31.9°S, 115.9°W, Figure 4-1) receives water from a

catchment with a total area of 121,000 km2 where 1.4 million people reside. The

climate of the catchment is Mediterranean, with hot, dry summers, and mild, wet

winters. More than 90% of rainfall occurs between April and October (Hillman et al.

1995), and flow is similarly skewed, but lags rainfall by about one month (Thompson

and Hosja 1996). The Avon River catchment (area ~ 119,500 km2) contributes around

60% of flow to the Swan River, with the remaining contributions from numerous

natural and regulated tributaries and urban drains (Peters and Donohue 2001).

Freshwater flow to the system is decreased by extractions for water supply, with

impoundments that restrict saltwater intrusion and act as reservoirs for water supply.

The lower 20 km of the estuary are generally wide and moderately deep, with some

lateral constrictions. This region is flushed by tides, and has few persistent water-

quality problems (Stephens and Imberger 1996). The remaining 20-60 km,

constituting the upper estuary, are narrow, shallow and generally poorly flushed.

Phytoplankton blooms and hypoxia are frequent occurrences in the upper reaches

(Thompson and Hosja 1996; Hamilton et al. 1999; Thompson 2001).

A body of research into phytoplankton bloom dynamics in the Swan River estuary has

focused on the role of nutrients (John 1994; Thompson and Hosja 1996; Thompson


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1998). Thompson and Hosja (1996) demonstrated with bioassays that there is strong

potential for nitrogen limitation from spring to autumn, when nuisance blooms occur.

The remaining high discharge period is characterized by similar potential for nitrogen

or phosphorus limitation, though temperature, residence time and light are generally

unfavourable for development of substantial biomass (Thompson and Hosja 1996;

Chan and Hamilton 2001).

Nutrient loads and concentrations increase at the start of the rainy season in late

autumn or early winter when large pulses of nitrate occur in freshwater inflows (Peters

and Donohue 2001). Thompson (1998) found that rainfall events leading to increased

nitrate concentrations in the estuary appeared to initiate phytoplankton blooms, while

Chan and Hamilton (2001) found a positive correlation between winter nitrate loads

and spring chlorophyte biomass.

Phytoplankton succession in the Swan River estuary is highly seasonal (Chan and

Hamilton 2001), but follows a general temperate estuarine cycle. Freshwater diatoms

(e.g. Cyclotella, Nitzschia) dominate the winter phytoplankton community. The

largest bloom generally occurs when chlorophytes (mostly Chlamydomonas) dominate

in spring. Chlorophytes are succeeded by marine diatoms (e.g. Skeletonema) and

dinoflagellates (Prorocentrum, Gymnodinium and Gyrodinium) in summer and autumn

(John 1994; Thompson and Hosja 1996).


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4.3 Methods

4.3.1 Numerical Model

The numerical model used to simulate the physical and biogeochemical processes in the Swan River

estuary is a three-dimensional hydrodynamic model (Estuarine and Lake Computer Model; ELCOM)

coupled at each time step with an ecological model (Computational Aquatic Ecosystem Dynamics

Model; CAEDYM). The physical model, ELCOM, has been developed for simulating transport and

hydrodynamics in estuaries where there is significant stratification and where there are multiple inflows

(including groundwater sources) and tidal boundaries, and includes the effects of wind stress, and

surface heat exchange. The simulation method solves the three-dimensional Reynolds-averaged,

unsteady, hydrostatic, Boussinesq, Navier-Stokes and scalar transport equations on a Cartesian mesh.

The hydrodynamic algorithms are a semi-implicit, finite-difference approach based on a second-order

Euler-Lagrange advection scheme for momentum, with an implicit solution of the free surface evolution.

Scalar transport uses a conservative discretization of a flux-limiting third-order method. Turbulence

modelling uses a mixed-layer approach in the vertical and a constant eddy viscosity in the horizontal. A

detailed description of the hydrodynamic model can be found in Hodges et al. (2000).

ELCOM passes the physical model variables (primarily salinity and temperature) to CAEDYM for

modification of ecological state variables at each time step, while CAEDYM passes the water quality

variables to ELCOM to compute the advective and dispersive transport processes.

The ecological model, CAEDYM, simulates the major biogeochemical processes influencing water

quality, including primary and secondary production, nutrient cycling and oxygen dynamics, (for details

see Robson and Hamilton 2004). The uncoupled ecological model has previously been applied to other

systems (e.g. Romero et al. 2002), as well as to one location in the Swan River, where interactions

amongst different phytoplankton taxa and zooplankton grazers were examined (Griffin et al. 2001). In

this study, zooplankton grazing was considered to be of secondary importance to the effects of advection

and transitions between freshwater and brackish conditions (Chan and Hamilton 2001) and was

therefore accounted for only indirectly as part of the phytoplankton loss term. The estuarine biota were


89

represented in the model simulations by four phytoplankton groups constituting the major taxa observed

in the estuary: marine diatoms, dinoflagellates, freshwater diatoms and chlorophytes. Each taxon

competes for nutrients through explicitly modelled uptake of nitrogen and phosphorus from the water

column, and also for light, through the effect of shading.

The various functions that control rates of phytoplankton growth and loss are included in Table 4-1 (and

further described in Table 4-2) and an overview is presented here. A salinity limitation function

decreased freshwater diatom and chlorophyte growth rates and enhanced respiration when salinity

increased above a threshold value assigned to each group. Conversely, growth of dinoflagellates and

marine diatoms declined with decreasing salinity below a threshold value. Light limitation was

modelled as per Webb et al. (1974), with a modification for photoinhibition incorporated for freshwater

diatoms but not for the other phytoplankton groups. Nutrient limitation was modelled with an internal

quota for both nitrogen and phosphorus, using a slight modification of the Droop (1973) model, with

nutrient uptake rates dependent on both internal and external concentrations. The temperature limitation

function had an Arrhenius dependence of growth up to a temperature threshold where inhibition

occurred (see Griffin et al. 2001).

4.3.2 Model input data and analysis

Data inputs for the ELCOM-CAEDYM application to the Swan River estuary included forcings at the

free surface, open (ocean) boundary and several inflow boundaries, fixed inputs of initial conditions and

bathymetry, as well as water column data for calibration and validation. These inputs are described in

more detail below.

4.3.2.1 Bathymetry

Bathymetric data were obtained from the Department of Transport (Western Australia) at 20x20m

resolution over the entire estuary domain from Fremantle to just above confluence with Helena River

(Figure 4-1). This bathymetry was averaged to the resolution of the Cartesian coordinates applied in the

model.


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A method was adopted to ‘straighten’ the Swan River in order to resolve the estuary domain in three-

dimensions, while producing model computational times approaching those of a 2D model (Hodges and

Imberger 2001). This procedure allowed a great reduction in the number of cells required to encompass

the estuary domain, without compromising the performance of the hydrodynamic model. The

momentum equations in the model were manipulated to capture the effects of curvature while

simultaneously straightening grid cells positioned at mid-width along the length of the estuary (for

details see Hodges and Imberger 2001). Model grid resolutions varied from 320m to 1000m along-

river, 40m to 100m across-river, and 0.4m to 2m with depth. Particular care was taken in averaging

depth and cross-sectional area at constrictions along the estuary; the Narrows (site 3), Blackwall Reach

(site 1), and the mouth at Fremantle (Figure 4-1).

A 5 km long x 3 km wide ocean buffer was added to the downstream boundary at Fremantle. The

purpose of the buffer was to reproduce some of the dynamics of exchanges and re-entry between the

ocean and the estuary and avoid excessive influence by tidal boundary forcing on water quality within

the estuary, given the limited data available at the open boundary. The upstream domain extended to the

confluence of the Swan River with Ellen Brook, approximately 10 km above the most upstream estuary

sampling site. The extent of this domain enabled tributary flow and composition measurements to

provide all of the tributary boundary conditions for the estuary model.

4.3.2.2 Meteorological data

Three-hourly meteorological data obtained for Perth Airport (Figure 4-1) from the Australian Bureau of

Meteorology were interpolated within the model to provide 10-minute data required for each model

time-step. These data included wind speed and direction, precipitation to 1mm accuracy, total cloud

cover estimated in octals, and air temperature and dew point temperature to 0.1ºC accuracy. Relative

humidity was calculated from dry bulb and dew point temperature data. Solar insolation (shortwave

radiation) data were recorded at 10-minute intervals at the Caversham AQ Meteorological Station

approximately 4 km from the upper estuary.


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4.3.2.3 Flow data

Daily flow inputs to the model were from daily stage heights and established discharge height versus

flow relationships for Avon River at Walyunga, Ellen Brook, Henley Brook, Helena River, Jane Brook,

Susannah Brook, Upper Canning at Nicholson Rd Bridge, Mill Street Main Drain, Yule Brook, Bickley

Brook, and Southern River. Additional inflow data were obtained for the main urban drains adjoining

the modelled domain; Bayswater Main Drain, Mt Lawley Main Drain and South Belmont Main Drain.

The latter data were available from Water Corporation records and the former data from Waters and

Rivers Commission records. Inflow at the Canning River boundary was obtained by summing gauged

flow from its main tributaries; Canning at Nicholson Rd Bridge, Mill Street Main Drain, Yule Brook,

Bickley Brook, and Southern River.

Flows from ungauged catchments, and from portions of catchments downstream of gaugings, were

calculated by extending the areal runoff coefficient for the nearest gauged catchment to the ungauged

catchment (see Table 4-3). This was applied for Upper Swan, St Linds Creek, Perth Airport North and

South, Central Belmont, South Perth, Maylands, Claisebrook, and the Perth Central Business District

and to partially ungauged catchments of Munday Brook, Ellis Brook, Helm St, Southern River, Lower

Canning, and Bull Creek. Direct rainfall on the estuary was calculated based on the surface area of open

water and rainfall data from Perth airport (Figure 4-1).

Groundwater inflows represent a small but potentially important source of freshwater to the estuary,

particularly in the low-flow period of summer-autumn. Linderfelt and Turner (2001) applied a two-

dimensional, vertically integrated finite element groundwater flow model (FeFlow; Diersch 1996) to

simulate groundwater inflows to the upper estuary. They used the model to determine inflows at 30-

70m intervals along both shores on a bimonthly basis for one year. We summed the FeFlow output into

six sections on the north shore, and five sections on the south shore and linearly interpolated the

bimonthly output to provide daily groundwater flows for input to each groundwater boundary cell of

ELCOM-CAEDYM.


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4.3.2.4 Water Quality

Water quality data were collected weekly by the Water and Rivers Commission of Western Australia, at

nine sites along a 30km domain of the Swan River estuary, from Blackwall Reach to Success Hill

Reserve (Figure 4-1) from October 1994 to July 1998. Vertical profiles of salinity, dissolved oxygen

(DO), pH and temperature were taken with a Hydrolab Datasonde multiprobe logger at 0.5m depth

intervals at each site.

Water samples were also taken at the surface, 1-m depth, and bottom (0.5m from the bed) and analysed

for ammonium, nitrate + nitrite, total nitrogen, filterable reactive phosphorus, total phosphorus, and

chlorophyll a (for further details see Chan and Hamilton 2001). Silica was also analysed

spectrophotometrically following filtration (GF/C filter, nominal pore size 1 µm), reaction with

ammonium molybdate after acidification to pH 1.2 with HCl, and addition of oxalic acid to remove

molybdophosphoric acid (Greenberg et al. 1992, Standard Method 4500-SiO2). An extensive set of

nutrient concentration measurements was available in tributaries, but for ungauged catchments, nutrient

concentrations were taken to be identical to those from the nearest monitored drain.

Phytoplankton counts were made from depth-integrated triplicate water samples (for details see Chan

and Hamilton 2001) and then apportioned into diatom, dinoflagellate and chlorophyte groups. An

equivalent chlorophyll a content was estimated for each phytoplankton group (see review by Griffin et

al. 2001). We arbitrarily differentiated freshwater and marine diatoms based on a salinity of 10 psu.

An ocean salinity and temperature annual cycle variation was taken from Zaker (1995) and Stephens

(1992) for the downstream boundary condition. Oceanic nutrient concentrations were taken from

Tipping (1997), DEP (1996), and Lemmens (2003) and averaged for a constant ocean boundary

condition.

Groundwater nutrient concentrations were set to a constant value based on mean values derived from

bore measurements by Linderfelt and Turner (2001) and additional data by Eade (1996) and Davidson

(1995).


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4.3.2.5 Tidal data

Tidal elevation data were collected at 3-hourly intervals at Fremantle, Barrack St and Meadow St

stations by the Maritime Division of Transport, Western Australia. Fremantle data (Figure 4-1) were

applied at the oceanic boundary for the domain. Data at Barrack St and Meadow St were used to

validate tidal propagation (not shown). The record of tidal elevations at the various stations was not

complete, and where short periods (<1 d) of data were missing, data from the preceding day were

applied. Where longer periods were unavailable (<2% of the record), tidal elevations were generated

from comparisons between Fremantle and Barrack St that showed a mean lag of 2.5 hours and a

reduction in amplitude of 20% at Barrack St.

4.4 Results and Discussion

4.4.1 Calibrated parameters

Calibration was performed by running the model with one year of data (1995) and

adjusting the parameters to attempt to more accurately reproduce the observed data.

These parameters reflect some of the intrinsic variations in physiology associated with

different phytoplankton assemblages, and even within phytoplankton species or strains

that may be due to different life history stages or responses that are not parameterised

within the model. Additionally, spatial averaging for the grid cells used in the

modelling meant that other parameters such as phosphorus release from bottom

sediments may vary over different spatial scales from those used in the model due to

heterogeneity of sediment properties (e.g. porosity, organic matter, biochemical

oxygen demand, mineral composition). The calibrated parameters are given in Tables

4-4 and 4-5.


94

Validation of the model was carried out using field data from 1996 and 1997, using

identical parameters to those calibrated for the 1995 field data set.

4.4.2 Physical results and validation

A good agreement between simulated and observed surface and near-bed salinities was

obtained for most sites at most times of the year during 1995-1997 (Figure 4-2 to

Figure 4-4, R2 = 0.77-0.88). The match was particularly good in the upper reaches,

which were of principle concern for the ecological modelling, since phytoplankton

blooms are most commonly observed in this area. Discrepancies between field and

simulated data occurred during the period of gradual increase in salinity over autumn

(see Figure 4-2, site 9, day 100-140, 1995), with the model predicting a slightly less

saline system in the period just before the winter rains. The sudden stratification in the

upper reaches during the first flush of winter inflow (e.g. Figure 4-2, site 9, day 130-

150, 1995) was also under-predicted. Examination of the rainfall-runoff pattern during

this period revealed a substantial rainfall event on day 130, which had negligible effect

on the gauged runoff. The inaccuracy of using nearby catchments to estimate flow for

ungauged tributaries is likely to have contributed to this problem. In contrast, in the

lower reaches of the estuary, runoff was applied as a direct product of the rainfall

record, due to lack of gauging stations in this area, and it is evident that in this region

salinity discrepancies are reduced.

The high-flow winter period is simulated well in the upper reaches in all years.

However, in the lower estuary, there were periods later in each year when there was

around 5 psu discrepancy between simulated and observed surface salinities. This

difference can be attributed partly to the prescription of a generic annual cycle for the


95

ocean buffer salinity and most specifically to the fact that a vertical profile is not used

for this boundary where there is potential in winter for significant vertical salinity

gradients. The simulated recovery and propagation of the salt wedge along the bottom

after the winter rains ceased, was faster than observed in the field (e.g. Figure 4-2,

station 1, day 220-300). This can be partly attributed to tidal propagation through the

ocean boundary on the flood tide being reset to oceanic salinity rather than to the

(lower) salinity on the ebb tide that should have relected re-intrusion of brackish water.

Despite a strong interannual variability in flow, differences in salinity < 5 psu (mostly

< 2 psu) between model and field indicated conditions were replicated well enough for

some confidence in the use of the hydrodynamic model as a basis for coupled water

quality and ecological modelling.

Seasonal temperature variations are also reproduced reasonably well (e.g. Figure 4-5,

R2 = 0.80-0.85). There is little temperature stratification, but the effect of vertical

differences in temperature on the hydrodynamics of the estuary is overwhelmed by

salinity stratification in any case.

4.4.3 Ecological results and validation

4.4.3.1 Dissolved Oxygen

The seasonal pattern of dissolved oxygen (DO) concentrations in the Swan River (e.g.

Figure 4-6) is poorly captured by the model simulations (R2 = 0.1). There is an

underestimation of persistent near-bed hypoxia in the upper reaches (sites 5-9) during

summer-autumn, and significant lack of the DO stratification observed in the field.

Some of this variation can be attributed to discrepancies in phytoplankton biomass


96

between the model and field data that influence consumption and production of

oxygen. This effect may be amplified as motility of phytoplankton taxa was not

modelled, and simulated vertical phytoplankton distributions are thus more

homogenous than in the field. The over-estimation of DO in simulations of bottom

waters was not resolved simply by increasing sediment oxygen demand (SOD) since

this had the effect of decreasing DO levels in surface waters to unrealistically low

levels. The prescribed SOD thus compensated for the inability of the model to capture

the observed water column stratification in the upper reaches.

The vertical resolution used to model the upper estuary reaches may also have

contributed to the problems encountered in reproducing the intensity of stratification;

the coarser the resolution, the less accurate the calculation of vertically resolved

processes such as transfer of DO at the surface and bottom boundaries. The resolution

represented a balance between the need not to have excessively long computer run

times while at the same time attempting to adequately resolve a large and highly varied

estuary domain.

The horizontal averaging used in the model is also important in some of the vertical

differences. Each model grid cell represents an area of 100,000 m2, in which the depth

is averaged. In the field, bathymetric variations occur on a finer scale, and the depth of

profiles at most sampling sites (sites 5-9) located near mid-width in the upper reaches

is greater than the model depth at the same point (in some cases, by up to 3 m), while

at others (e.g. the Narrows, site 3) the reverse is true.


97

Sediment oxygen demand has also been observed to vary along the Swan River estuary

(Lavery et al. 2001). Heterogeneity of sediment properties is not represented in the

model, and sediment-water exchange of DO is modelled as a function of the overlying

water cell’s DO and temperature. This may be another factor limiting the accuracy of

the model with respect to simulations of water column DO.

4.4.3.2 Nutrients

Phosphorus

Field phosphate concentrations (Figure 4-7, R2 = 0.35) are similar to that modelled,

except for two main features. Firstly, at site 2 (lower estuary), the simulations show

elevated phosphate, particularly in the near-bed waters. These deeper sites of the

lower estuary were identified by Douglas et al. (1996) as areas where bottom waters

were sometimes devoid of DO and where large quantities of nutrients were released

from the bottom sediments, although this was not evident in our field data. The

elevated concentrations from site 2 also appear to affect simulations for site 3,

although to a lesser extent and coincide with the period of greatest DO stratification

(Figure 4-6). The second discrepancy occurs in the upper reaches (sites 5-9) where

phosphate is underestimated around day 50, and again around day 150. This

underestimation coincides with the inability of the model to reproduce the observed

DO stratification at these sites (see Figure 4-6). The overestimation of phosphate in

the lower reaches of the estuary and underestimation in the upper reaches, indicate the

strong influence of hypoxia on sediment phosphorus release.

Total phosphorus (TP) concentrations are comprised of phosphate, algal biomass and

particulate and soluble P. The particulate and soluble P consist of both organic and


98

inorganic constituents. Organic phosphorus from excretion by phytoplankton is

assumed to be converted rapidly to inorganic form. TP (and total nitrogen, TN) are

conserved within the modelled domain, except for (a) boundary exchanges, where

addition to and removal from the domain occurs via inflows and outflows; (b)

sedimentation of phytoplankton/particulate matter to the bed (Stokes settling); and (c)

the release of nutrients from bottom sediments (equations detailed in Table 4-1).

Variation in TP is reproduced quite well by the model (e.g. Figure 4-8, R2 = 0.21),

with the most obvious divergence an overestimation at site 2, a result of the phosphate

overestimation described previously. Periods of underestimation of TP (e.g. Figure

4-8, stations 7 and 8, around day 100 and again around day 200) do not seem to be

related to phosphate release events (Figure 4-7). It appears that these short-term TP

pulses are related to particulate inputs in inflows; the corresponding salinity time series

(Figure 4-4) shows a small freshet at day 100 at site 9, and day 200 coincides with

decreased salinities that denote the beginning of peak winter inflow. This difference

reflects limitations imposed on model simulation accuracy by the boundary conditions.

Early ephemeral rainfall events at the start of the high-rainfall winter period appear to

have little effect on streamflow, but introduce a substantial volume of diffuse runoff

from ungauged catchment areas (Kurup et al. 1998). Though the effect on flow may

be captured in the simulations by prescribing a simple rainfall-runoff model for the

ungauged catchment, this ‘first flush’ event has high concentrations of nutrients

(Douglas et al. 1996) that are difficult to predict, with estuary-wide effects on nutrient

levels.

Nitrogen

The main features of nitrate distributions (R2 = 0.13) are elevated concentrations

during the main winter inflow (Figure 4-9, days 200-300) and pulses of elevated nitrate


99

concentration at day 100 and again just before day 200 which may be related to

inflows not captured in the boundary conditions, as discussed for TP. Simulations of

ammonium concentration (Figure 4-10, R2 = 0.12) show, as with the phosphate

simulations, that ammonium is underestimated in the upper reaches (sites 5-9) during

summer-autumn. Minor overestimation at site 2 is associated with the lack of DO

stratification and resulting sediment nutrient release processes. For total nitrogen (TN)

the most significant divergence of model data from field data is from day 100-200,

when model simulations significantly underestimate concentrations (Figure 4-11, R2 =

0.15). Most of the variability can be attributed to the inorganic components, nitrate

and ammonium, both of which are similarly underestimated by model simulations for

this period (Figure 4-9 and Figure 4-10).

Recycling of nutrients from the bottom sediments appears to be a significant source of

both phosphate and ammonium. Recent measurements by Lavery et al. (2001) were

important in quantifying the maximum potential nutrient release rates used in the

sediment nutrient release model formulation (see Table 4-1 and Table 4-5), however

the problems in simulating the DO stratification, and in particular, bed hypoxia,

prevented release rates approaching those observed in the field. As with the lack of

DO stratification, the homogenous vertical distribution of phytoplankton in the model

is likely to affect nutrient distribution, as uptake occurs in near-bed waters more than

would be expected in the field. Vertical gradients of nutrients are small in the model

simulations in all years, except in the deeper reaches of the lower estuary (particularly

site 2). However, the nutrient stratification at the deepest site (site 1, Blackwall Reach)

is minimal due to damping in the simulations by the adjacent vertically homogeneous

ocean boundary condition, prescribed in the absence of profiled data.


100

4.4.3.3 Phytoplankton Biomass

Seasonal variations in biomass and the co-existence of the dinoflagellate (R2 = 0.10-

0.42) and marine (R2 = 0.31-0.54) and freshwater diatoms (R2 = 0.03-0.89) are

reasonably well replicated by the model in 1995, 1996 and 1997 (Figure 4-12 to Figure

4-14). It is notable that in 1997 the absence of a distinct freshwater diatom bloom in

early winter is captured by the model simulations.

The decay of phytoplankton following blooms or periods of high biomass is less rapid

than observed in the field data. This feature may be partly due to top-down control of

blooms, which has been found to be significant in some circumstances (Sin and Wetzel

2002). Griffin et al. (2001) examined a localized dinoflagellate bloom event at one site

(site 7, Ron Courtney Island) in the Swan River, and found that zooplankton grazing

hastened the decline of a bloom.

Additionally, there is far greater short-term (weekly to monthly) variation in biomass

in the field measurements than in the model over all years. In particular the sudden

peaks of summer dinoflagellate blooms are not well replicated in the simulations.

These blooms exhibit extremely high temporal and vertical variability (Hamilton et al.

1999). Indeed, some of the very sharp increases in biomass observed in the field data

from week to week (e.g. the peak at day 50, Figure 4-12) are not theoretically possible

if it is assumed that variations are due solely to phytoplankton growth in situ, even for

the maximum growth rates that have been measured under laboratory conditions. This

suggests that boundary conditions and external inputs play an important role in

determining phytoplankton concentrations within the estuary, and that ‘seeding’ from

sources outside of the estuary domain and not encompassed by the boundary


101

conditions, may be important in perturbations in phytoplankton populations. This is

further complicated by the limited temporal resolution of boundary conditions, which

may miss important short-term fluctuations.

Amongst the factors that influence the model results are the selected vertical and

horizontal scales of the model grid, which effectively dampen variation in simulated

environmental conditions, and phytoplankton concentrations. The fractal distribution

of field phytoplankton concentrations results in a ‘patchiness’ that is difficult to

capture in monitoring, and to reproduce with a relatively low-resolution model. There

will also be problems in trying to simulate the changes in chlorophyll a within

phytoplankton cells, which may vary more than five-fold depending on light and

nutrient history (Geider et al. 1998). Previous studies have used mechanistic models of

physiological changes within cells to simulate algal cell chlorophyll a content,

however, the additional processing power required to model this process is likely to be

prohibitive in a full ecosystem model such as ELCOM-CAEDYM. An alternative

suggested by Flynn (2003) uses an empirical relationship between environmental

parameters and the chlorophyll to biomass ratio, though divisions of phytoplankton

into physiologically broad groups smoothes much of the inherent variability in this

relationship.

Simulation of multiple phytoplankton groups allowed the shift in species composition

to be replicated and was important in allowing a reasonable prediction of biomass over

different seasons. Previous modelling efforts with phytoplankton as a single variable

have suggested the utility of multiple groups (Jorgensen et al. 1986). Interaction

between groups is also of interest where some of the groups (e.g. dinoflagellates in the


102

Swan River) are of more concern than others. In this case, an alternative approach is a

size-based phytoplankton grouping within an ecosystem model (e.g. Sin and Wetzel

2002). This approach can reduce the complexity of parameter calibration due to

availability of standard allometric relationships between size and settling-rate, and

between size and physiological factors such as growth rate, respiration, and nutrient

uptake, etc. (Moloney and Field 1989; Moloney and Field 1991; Gin et al. 1998).

However, there is also a reduction in the utility of model output, for example, where

the predicted biomass of a cell size cohort contains different phytoplankton groups

with overlapping cell sizes (e.g. marine dinoflagellates and diatoms). It is also

interesting to note that a recent study found that environmental variation (in the form

of a pulsed nutrient supply) encouraged the coexistence of multiple phytoplankton

groups (Yamamoto and Hatta 2004). This is of particular importance to estuaries such

as the Swan River where extensive temporal and spatial heterogeneity may provide the

non-equilibrium conditions required to support a diverse phytoplankton community.

Finally, there are fundamental limits of predictability due to uncertainty in the

empirical data used for model boundary conditions and validation. Hakanson et al.

(2003) found significant differences in coefficients of variation at a range of time

scales from daily to inter-annual, for phytoplankton biomass prediction. This result

indicates the relatively poor predictability of individual algal groups as well as total

biomass in rivers.

However, despite the inherent limitations in predictive application, the modelling

approach can provide useful insights into algal dynamics in the field. In particular, we


103

found that examination of the limitation values for each algal group could yield useful

information about reasons behind the development or decline of specific bloom events.

Phytoplankton limitation

The ELCOM-CAEDYM model was configured to output physiological parameters for

phytoplankton growth on a sub-daily timestep (Figure 4-15-Figure 4-18). A value

from zero to unity is prescribed for limiting factors of salinity, nitrogen, phosphorus

and light, representing maximum effect on the gross rate of growth (i.e. zero growth),

through to no effect, respectively. The Arrhenius function for temperature (see Table

4-1 for the specific formulation used) allows a limiting value of greater than one,

representing an increased growth rate above that at the reference temperature, however

only values up to unity are shown in the figures. Net advection of phytoplankton cells

into and out of the domain is calculated using the known flow rates and phytoplankton

concentrations in the model at the edge of any defined domain; in this case, the upper

reaches of the Swan River, and plotted against phytoplankton biomass in the upper

panel of each figure.

Figure 4-15 shows that the dinoflagellate population is nitrogen limited during the

bloom at day 50 (mid February), however, the reduction in biomass after the peak (day

50-100) occurs as nitrogen limitation decreases, which by itself would be expected to

result in increased growth. However, temperature imposes a stronger limitation on the

growth rate at this time. Advection to and from the domain is small and consistently

negative from days 50-100. During this summer period, advection is largely due to

tidal exchange through the domain, and net loss occurs at a relatively slow rate.

Similarly, Figure 4-16 shows the decrease in marine diatom biomass (day 0-50) with


104

the influence of advection. In contrast, there is a large pulse of freshwater diatoms

advected into the system at day 150 (Figure 4-17). A large enough proportion of this

pulse is lost to settling or death rather than being swept out at the lower boundary of

the domain such that the cumulative advected cell loss disappears off the graph. In the

case of the chlorophytes (Figure 4-18), the most notable feature is the short period of

time in which the spring bloom can occur, after the reduced advection of cells from the

winter flows (after day 300), but before salinity limitation becomes extreme (before

day 350).

Although it is evident that the time scales relating to flushing of phytoplankton from

the estuary and net growth can be a prime determinant in the occurrence of a bloom, it

is rare that this interaction can be demonstrated so directly. For example, nutrient

enrichment via the inflow can result in increased phytoplankton growth (Therriault and

Levasseur 1986), however Gilbes et al. (1996) found that despite increased nutrients

with increased inflow, chlorophyll a levels decreased. This was attributed to the

associated suspended solid levels in the inflow, limiting light availability, and

consequently algal growth, but the direct contribution of flushing was not determined,

and may have been enough to result in the observed decrease in chlorophyll a. Direct

output of all significant phytoplankton sources and sinks can allow for a far more

comprehensive understanding of phytoplankton dynamics.

Access to the simulated limitation function values for phytoplankton is useful in

determining the factors behind a specific bloom event and its decline, and make it

possible to identify extended periods of time when a particular phytoplankton taxon is


105

unlikely to bloom due to a specific factor, for example, the low salinity from day 150

to 350 which constrained the development of dinoflagellates (Figure 4-15).

Comparison with bioassays

Figure 4-19 shows a comparison between nutrient limitation in the field as measured

by Thompson (1998), and the modelled nutrient limitation value. Thompson (1998)

tested field phytoplankton samples for potential nutrient limitation by comparing

growth in control samples (with no added nutrients) with growth in samples given all

nutrients, all nutrients except for nitrate, or all nutrients except for phosphate. The

“relative nutrient limitation” calculated has been normalised against the maximum

values in order to make it possible to have direct comparisons against the limitation

function outputs from the model simulations. The simulated nutrient limitation value

for the entire phytoplankton population is calculated as the minimum of the nitrogen

limitation value and the phosphorus limitation value for each phytoplankton taxa,

weighted for the biomass of each taxon and then summed to produce an overall

limitation value for the total phytoplankton.

The seasonal pattern of nutrient limitation of model phytoplankton over the year has

similarities to that measured via bioassays in the field. The period of least limitation

from day 150-300 shows good correspondence. Given that nutrient limitation was not

specifically examined during the calibration process, the match lends some validity to

the way in which the nutrient limitation process is modelled. However, during the

summer-autumn period, field data indicate a much greater relative nutrient limitation

than in the model simulations. A number of factors can account for this discrepancy.


106

The bioassays specifically examine nutrient limitation, and not light limitation, and do

not take into account the role of temperature, salinity, or advection.

Figure 4-20 compares field bioassay measurements for the degree to which nitrogen is

potentially more limiting than phosphorus (Thompson 1998), against simulated results

using direct nitrogen and phosphorus limitation values for phytoplankton, again

weighted for biomass. The bioassay data were again normalised from zero to one for

comparison with the simulation data. In this case, the closer the ratio to unity, the

greater limitation by nitrogen is than limitation by phosphorus. The comparison shows

general agreement that nitrogen limitation is greater during the beginning and end of

the year (summer), but there is more variation in the simulated winter limitation than

observed in the field. In particular, the simulation shows a sharp increase in limitation

by nitrogen at day 200, just after the occurrence of a freshwater diatom bloom. As

noted previously, the freshwater diatom bloom is strongly driven by boundary inflows

of cells. The nitrogen limitation spike may thus be interpreted as a high influx of

diatom cells that take up much of the available nitrogen. The field data do not display

this spike, which may be due to missing the short-term influx of these cells (which

occurred on only one sampling date) as collection of phytoplankton for the laboratory

cultures occurred fortnightly during this period. It might also be expected that

discrepancies might arise due to the lesser influence of phosphorus on the

phytoplankton growth in the model, which resulted in a less rigorous calibration of the

relevant model parameters (e.g. uptake rates, maximum and minimum cell quotas,

etc.). Similarly, during the winter wet period when hydraulic residence times are very

low, the importance of nutrient limitation on phytoplankton biomass is greatly reduced,

and the calibration for this period will have been less rigorous.


107

A significant caveat to modelling is demonstrated by unexpected events such as a

major cyanobacterial bloom in the Swan River in January 2000 (Robson and Hamilton

2003). As cyanobacteria had never been observed in significant numbers previously in

the Swan River, this group was not used as one of the primary algal groups modelled,

although it was subsequently added to examine this event (Robson and Hamilton,

2004). However, due to the unforeseeable summer storm event and magnitude of the

associated freshet, events of this nature may not be reproduced. A stochastic element

to the modelling may be advisable based on our current inability to capture the

inherent variability of aquatic ecosystems and the multitude of different species that

may proliferate under a specific set of environmental conditions. As models are

directed towards output of physiological variables, however, instead of simply

attempting to minimise errors between field and simulated concentrations of state

variables, we can expect to gain far greater insight into the nature of responses and the

dynamics of phytoplankton in estuaries.

4.5 Acknowledgments

We thank the Water and Rivers Commission and Malcolm Robb for provision of data

used in this study. We also thank Dr Peter Thompson for provision of the bioassay

data used in his paper ‘Spatial and temporal patterns of factors influencing

phytoplankton in a salt wedge estuary, the Swan River, Western Australia’. Estuaries

21: 801-817, 1998.


108

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112

4.7 Tables

Table 4-1. Primary phytoplankton modelling equations in CAEDYM for phytoplankton group ‘i'. Rate of change of phytoplankton concentration 1, 4 (mg chl a m-3 d-1) ( )i

i i i i i

CR C V H

tδ µδ

= − + +

Phytoplankton growth rate 1, 2 (d-1) [ ]max ( ) ( ) min ( ), ( ), ( ), ( )i i i i i i i if T f S f N f P f I f Siµ µ=

Phytoplankton respiration and mortality rate 1, 2 (d-1)

20 ( )T

i Ri i iR k f Sϑ −=

Temperature limitation 2 ( )20( ) i id T aTi i i if T bψ ψ −−= + +

Light limitation 2 ( ) ( )( )ki

/ exp 1 / , photoinhibited (freshwater diatoms)( )

1-exp -I I , non-photoinhibited (other groups)S S

i

I I I If I

� −�= ��

Nitrogen limitation 2 min

max min

( ) 1i ii

i i i

IN INf N

IN IN IN� �

= −� �−

Phosphorus limitation 2 min

max min

( ) 1i ii

i i i

IP IPf P

IP IP IP� �

= −� �−

Salinity limitation 2 (freshwater species: chlorophytes and freshwater diatoms) ( )

( ) ( ) ( )

2

max

2 2 2max

1,

( ),

1 2

opi

i opiiopi

i opi i opi opi

S S

S Sf SS S

S S S S S Sβ

≤�� −= � >�

− − − − −��

Salinity limitation 2 (marine/estuarine species: marine diatoms and dinoflagellates)

( )( )

2

2 2

1,

( ),

2 1

opi

opiiopi

i i opi

S S

Sf SS S

S S Sβ β

>��

= � ≤� − − +� Rate of change of internal (cellular) phosphorus concentration 2, 4 (mg P (mg chl a)-1 d-1)

( )Pi Piii i IPi

i

U EIPV IP H

t Cδδ

−= + +

Rate of change of internal (cellular) nitrogen concentration 2, 4 (�g N (mg chl a)-1 d-1)

( )Ni Niii i INi

i

U EINV IN H

t Cδ

δ−

= + +

Phytoplankton phosphorus uptake 1,

2, 4 (mg P m-3 d-1) max 4max

max min 4

( ) i iPi i i i

i i Pi

IP IP POU UP C f T

IP IP K PO

� �� −= �� − + � � �

Phytoplankton nitrogen uptake 1, 2, 4 (mg N m-3 d-1) max 3 4

maxmax min 3 4

( ) i iNi i i i

i i Ni

IN IN NO NHU UN C f T

IN IN K NO NH

� �� − += �� − + + � � �

Release of phosphate from benthic sediments 3 (g m-2 day-1)

h

pHK

pH

DOKK

S

tTP

tPO bpH

b

bDOS

DOSP

��

�

�

�

−+−

++

==7

7

4

δδ

δδ

Release of nitrogen from benthic sediments 3 (g m-2 day-1)

h

pHK

pH

DOKK

S

tTN

tNH bpH

b

bDOS

DOSN

��

�

�

�

−+−

++

==7

7

4

δδ

δδ

1Uses symbols defined elsewhere in this table. 2Uses symbols defined in Table 4-4. 3Uses symbols defined in Table 4-5. 4Uses symbols defined in Table 4-6.


113

Table 4-2. Explanation of phytoplankton modelling equations in CAEDYM. Rate of change of phytoplankton concentration (mg chl a m-3 d-1)

Increases with growth rate, decreases with respiration rate and also changes according to the net flux of phytoplankton due to settling (vertically) and due to advection and mixing.

Phytoplankton growth rate (d-1) Changes as a function of the maximum growth rate under ideal conditions and limitation functions for temperature, salinity, light and nutrients (P, N and Si if diatoms).

Phytoplankton respiration and mortality rate (d-1)

Concatenates respiration, natural mortality and excretion. It is a function of the respiration rate coefficient and a temperature function, and also increases with more severe salinity limitation.

Temperature limitation Allows for inhibition at above optimal temperatures. Limitation value is 1 at standard temperature, increases up to an optimum temperature and then decreases to 0 at a defined maximum temperature.

Light limitation Exponentially decreasing curve according to incoming photosynthetically active radiation and the defined initial slope of the photosynthesis-irradiance curve. Photoinhibition occurs for freshwater diatoms above a defined light saturation value.

Nitrogen limitation Formulated to give a limitation curve dependent on the internal nutrient store relative to defined maximum and minimum internal nitrogen levels.

Phosphorus limitation Formulated to give a limitation curve dependent on the internal nutrient store relative to defined maximum and minimum internal phosphorus levels.

Salinity limitation (freshwater species: chlorophytes and freshwater diatoms)

A parabolic function with increasing limitation above an ‘optimum’ defined salinity value and up to a maximum salinity. Below the optimum the limitation value is 1.

Salinity limitation (marine/estuarine species: marine diatoms and dinoflagellates)

Mirrors freshwater salinity limitation function.. For salinities below the ‘optimum’ salinity value, the parabolic function increases with decreasing salinity.

Rate of change of internal (cellular) phosphorus concentration (mg P (mg chl a)-1 d-1)

Represents the balance of phosphorus uptake (described below) and an excretion term (release of phosphate), plus the net flux due to settling and advection and mixing.

Rate of change of internal (cellular) nitrogen concentration (�g N (mg chl a)-1 d-1)

Represents the balance of nitrogen uptake (described below) and an excretion term (release of ammonia), plus the net flux due to settling and advection and mixing.

Phytoplankton phosphorus uptake (mg P m-3 d-1)

A function of maximum uptake rate under ideal conditions, a Michaelis-Menten term (using the ambient phosphorus concentration and the half saturation constant for transfer of phosphorus into the cell), and an expression describing how close to the maximum internal phosphorus concentration the cell is. It also depends on a temperature limitation function which is the same as for phytoplankton growth (described above).

Phytoplankton nitrogen uptake (mg N m-3 d-1)

A function similar to that for phosphorus uptake, but also takes into account two species of inorganic nitrogen (ammonium and nitrate), with a preference term for ammonium.

Release of phosphate from benthic sediments (g m-2 day-1)

A function describing the change in phosphate concentration in the bottom layer of the water column dependent on maximum theoretical flux and two half saturation constants for how oxygen and pH regulate phosphate release.

Release of nitrogen from benthic sediments (g m-2 day-1)

A function similar to that for phosphate release. Sediment nitrogen release is assumed to be entirely in the form of ammonium.


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Table 4-3. Gauged and ungauged catchment areas contributing to the Swan River estuary. Flow from the closest gauged catchments was applied to estimate flow in the ungauged catchments.

Catchment name Gauged area (km2) Ungauged area (km2) Closest gauged catchment

UPSTREAM OF DOMAIN

Avon River 119035 0 - Ellen Brook 664 51.0 Ellen Brook Millendon 0 35.2 Ellen Brook Susannah 0 55.1 Ellen Brook Henley Brook 0 13.5 Ellen Brook St Linds Creek 0 11.6 Bennett/Ellen Brook Jane Brook 131.69 6 Jane Brook Blackadder Brook 0 17.6 Bennett/Ellen Brook Bennett Brook 102 10 Bennett/Ellen Brook Upper Swan 0 39.4 Bennett/Ellen Brook

UPPER SWAN Helena River 166 10 Helena River Perth Airport North 0 28.1 Hlena River Perth Airport South 0 24.6 Helena River Belmont Central 0 3.7 South

Belmont/Bayswater South Belmont 9.89 0 - South Perth North 0 20.4 South

Belmont/Bayswater Bayswater Main Drain 26.3 1 Bayswater Maylands 0 18.7 Bayswater Claisebrook 0 16.4 Bayswater CBD 0 13.5 South

Belmont/Bayswater River surface area 0 7.3 100% rainfall

LOWER SWAN South Perth Central 0 46.2 South Belmont Narrows to Fremantle 0 10.2 50% of rainfall River surface area 0 46 100% rainfall

CANNING RIVER Upper Canning 147.0 0 - Bickley Brook 21.9 0 - Munday Brook 0 51.7 Bickley Brook Ellis Brook 0 12.0 Bickley Brook Helm St 0 6 Bickley Brook Yule Brook 51.8 4 Yule Brook Mill St Main Drain 12.3 0 - South Perth South 0 10.2 South Belmont Southern River 0 149 Upper Canning Lower Canning 0 46.5 Bannister Creek Bannister Creek 23.35 0 - Bull Creek 0 42.4 Bannister Creek River surface area 0 5 100% rainfall


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Table 4-4. Calibrated phytoplankton parameters

Parameter Symbol Units Calibrated value

Maximum growth rate µmax i day-1 Estuarine diatoms 1.6 Dinoflagellates 0.7 Chlorophytes 1.5 Freshwater diatoms 1.8 Respiration rate coefficients kRi day-1 Estuarine diatoms 0.15 Dinoflagellates 0.05 Chlorophytes 0.07 Freshwater diatoms 0.1 Temperature multiplier for respiration θI [dimensionless] Estuarine diatoms 1.07 Dinoflagellates 1.06 Chlorophytes 1.03 Freshwater diatoms 1.05 Minimum internal nitrogen INmin i mg N (mg chl a)-1 Estuarine diatoms 5.0 Dinoflagellates 4.5 Chlorophytes 4.0 Freshwater diatoms 5.6 Minimum internal phosphorus IPmin i mg P (mg chl a)-1 Estuarine diatoms 0.20 Dinoflagellates 0.27 Chlorophytes 0.2 Freshwater diatoms 0.25 Maximum internal nitrogen INmax i mg N (mg chl a)-1 Estuarine diatoms 12.0 Dinoflagellates 9.3 Chlorophytes 10.5 Freshwater diatoms 7.5 Maximum internal phosphorus IPmax i mg P (mg chl a)-1 Estuarine diatoms 0.6 Dinoflagellates 0.6 Chlorophytes 1.24 Freshwater diatoms 1.0 Maximum rate of nitrogen uptake UNmax i mg N (mg chl a)-1 day-1 Estuarine diatoms 12 Dinoflagellates 1.5 Chlorophytes 4.0 Freshwater diatoms 15 Maximum rate of phosphorus uptake UPmax i mg P (mg chl a)-1 day-1 Estuarine diatoms 0.3 Dinoflagellates 0.06 Chlorophytes 0.4 Freshwater diatoms 0.2 Half saturation constant for nitrogen uptake KNi mg L-1 Estuarine diatoms 0.015 Dinoflagellates 0.052 Chlorophytes 0.03 Freshwater diatoms 0.04 Half saturation constant for phosphorus uptake KPi mg L-1 Estuarine diatoms 3x10-3 Dinoflagellates 5x10-3 Chlorophytes 1.2x10-2 Freshwater diatoms 1.0x10-2


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Table 4-4 continued


Light saturation for maximum production Iki �E m-2 s-1 Estuarine diatoms 380 Dinoflagellates 180 Chlorophytes 200 Freshwater diatoms (photoinhibited saturation) Is �E m-2 s-1 120 Maximum optimum salinity tolerance Smax i psu Estuarine diatoms 22 Dinoflagellates 26 Chlorophytes 8 Freshwater diatoms 18 Minimum optimum salinity tolerance Sop i psu Estuarine diatoms 20 Dinoflagellates 23 Chlorophytes 4 Freshwater diatoms 10 Multiplier for temperature limitation ψI [dimensionless] Estuarine diatoms 1.07 Dinoflagellates 1.1 Chlorophytes 1.06 Freshwater diatoms 1.05 Coefficient for temperature limitation ai [dimensionless] Estuarine diatoms 29.6 Dinoflagellates 32 Chlorophytes 27.4 Freshwater diatoms 26.4 Coefficient for temperature limitation bi [dimensionless] Estuarine diatoms 0.028 Dinoflagellates 0.05 Chlorophytes 0.126 Freshwater diatoms 0.049 Coefficient for temperature limitation di [dimensionless] Estuarine diatoms 4.99 Dinoflagellates 1.01 Chlorophytes 4.25 Freshwater diatoms 5.41 Chlorophyll a per cell Chla mg chl a cell-1 Estuarine diatoms 2.28x10-6 Dinoflagellates 5.03x10-6 Chlorophytes 1.09x10-6 Freshwater diatoms 2.28x10-6

Table 4-5. Calibrated water quality parameters


Maximum potential sediment flux of phosphorus SP g m-2 day-1 0.04 Maximum potential sediment flux of nitrogen SN g m-2 day-1 0.02 Half saturation constant for nitrification KNIT mg L-1 4.0 Denitrification rate coefficient KoN2 day-1 0.4 Half saturation constant for denitrification KN2 mg L-1

4.0 Aerobic organic nitrogen mineralization rate coefficient KON day-1 0.01 Aerobic organic phosphorus mineralization rate coefficient KOP day-1 0.05 Temperature multiplier for mineralization VM [dimensionless] 1.08 Temperature multiplier for denitrification vN2 [dimensionless] 1.07 Mineralization half-saturation coefficient for oxygen KMIN mg L-1 1.5


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Table 4-6. Symbols in equations (Table 4-1) not defined in Table 4-4 or Table 4-5

Chlorophyll a concentration of phytoplankton group i Ci �g chl a Release of phosphate through phytoplankton excretion EPi mg P m-3 d-1 Release of ammonia through phytoplankton excretion ENi mg N m-3 d-1 Net flux of phytoplankton group due to advection and mixing Hi mg chl a m-3 d-1 Net flux of phytoplankton group due to settling Vi mg chl a m-3 d-1


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4.8 Figures

Figure 4-1. The Swan River estuary, Western Australia


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Figure 4-2. Salinity over time in 1995, showing field and model data at the 9 sampling sites.


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Figure 4-3. Salinity over time in 1996, showing field and model data at 9 sampling locations.


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Figure 4-4. Salinity over time in 1997, showing field and model data at 9 sampling locations.


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Figure 4-5. Temperature over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-6. Dissolved oxygen over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-7. Phosphate over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-8. Total phosphorus over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-9. Nitrate over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-10. Ammonium over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-11. Total nitrogen over time in 1997, showing field and model data at 9 sampling locations.

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Figure 4-12. Chlorophyll a concentrations in the upper estuary in 1995, averaged over the six upstream sites. Total chlorophyll a is given by the total height of the shaded areas; colours indicate different phytoplankton groups; (a) in the field; (b) in ELCOM-CAEDYM.


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Figure 4-15. Dinoflagellates in the upper Swan River during 1995. Top panel: biomass (black) and advection to/from upper domain (red). Bottom panel: biomass limitation values for salinity (red), phosphorus (blue), nitrogen (green), temperature (magenta), and light (cyan).

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Figure 4-16. Marine diatoms in the upper Swan River during 1995. Top panel: biomass (black) and advection to/from upper domain (red). Bottom panel: biomass limitation values for salinity (red), phosphorus (blue), nitrogen (green), temperature (magenta), and light (cyan).

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Figure 4-17. Freshwater diatoms in the upper Swan River during 1995. Top panel: biomass (black) and advection to/from upper domain (red). Bottom panel: biomass limitation values for salinity (red), phosphorus (blue), nitrogen (green), temperature (magenta), and light (cyan).

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Figure 4-18. Chlorophytes in the upper Swan River during 1995. Top panel: biomass (black) and advection to/from upper domain (red). Bottom panel: biomass limitation values for salinity (red), phosphorus (blue), nitrogen (green), temperature (magenta), and light (cyan).

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Figure 4-19. Relative nutrient limitation in field bioassay data (normalized) compared to model nutrient limitation function output in the upper Swan River, i.e. the ratio of chlorophyll a in a control after incubation to the standing stock of chlorophyll a from the estuary (field bioassay data from Thompson 1998) , simulated (---) and field bioassay (o).

Figure 4-20. Degree nitrogen potentially more limiting than phosphorus (normalized) in the upper Swan River, i.e. ratio of chlorophyll a biomass in treatments without P to treatments without N (field bioassay data from Thompson 1998), simulated (---) and observed (o).

Chapter 5. Modelling investigation

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5 Scenario modelling with a 3D hydrodynamic-

ecological model to investigate the impacts of

hydrological changes on phytoplankton dynamics

in the Swan River estuary

T.U. Chan, D.P. Hamilton, B.J. Robson, B.R. Hodges, and C. Dallimore.

Estuaries, 25, 1406-1415. 2002.

5.1 Abstract

The Swan River estuary, Western Australia, has undergone substantial hydrological

modifications from pre-European settlement. Land clearing has increased discharge from

some major tributaries roughly 5-fold, while weirs and reservoirs for water supply have

mitigated this increase and reduced the duration of discharge to the estuary. Nutrient loads

have increased disproportionately with flow and are now approximately 20-fold higher than

pre-European levels. We explore the individual and collective impacts of these hydrological

changes on the Swan River estuary using a coupled hydrodynamic-ecological numerical

model. The simulation results indicate that despite increased hydraulic flushing and reduced

residence times, increases in nutrient loads are the dominant perturbation, producing

increases in the incidence and peak biomass of blooms of both estuarine and freshwater

phytoplankton. By comparison, changes in salinity associated with altered seasonal

freshwater discharge have a limited impact on phytoplankton dynamics.

5.2 Introduction

The ecology and biodiversity of estuarine and coastal waters in many parts of the

world are under threat from increasing anthropogenic inputs of nutrients (Nixon 1995;


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Cloern 2001). Many of these threats can be attributed directly to expansion of human

populations along riparian zones and coastal catchments (Cooper and Brush 1993).

The threats to coastal ecosystems are especially exacerbated in Australia where 80% of

the population lives within 50 km of the coast and the major land drainage basins have

undergone large-scale land clearing and hydrological modification since European

settlement (Harris 2001). Declining water quality and high rates of sedimentation are

the most obvious manifestations of nutrient enrichment and land clearing (Zann 1995).

Knowledge of the nutrient assimilative capacity of coastal and estuarine ecosystems is

essential for management and rehabilitation. Globally, current large-scale efforts to

control eutrophication are based largely on the premise that improvements in

biodiversity and water quality will be linked directly with reductions in nutrient loads

(Carpenter et al. 1998). Such assessments give only rudimentary consideration to

response times, hysteresis effects, and hydrological controls; thereby neglecting

possible non-linear responses to changes in nutrient loading (Harris 1999).

While the major focus of eutrophication management has been on nutrient control

strategies (e.g. Sewell 1982; Young et al 1996; Thompson 2003), it is also important to

consider hydrological modifications that may have an impact on the eutrophication

response (Webster et al 2000; Webster and Harris 2004). For example, on the

Australian continent, weirs and dams have contributed directly to algal blooms by

increasing residence times and stratification of the impounded waters such as on the

Murrumbidgee River in New South Wales (Sherman et al. 1998; Webster et al. 2000),

and decreasing flushing of downstream estuaries such as in the Derwent River in

Tasmania (Davies and Kalish 1994) and in Port Phillip Bay in Victoria (Webster and


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Harris 2004). However some dredging or estuary opening strategies have improved

water quality through increasing flushing with marine water, e.g. the Harvey Estuary in

Western Australia (Hearn and Robson 2000), and Wilson Inlet in Western Australia

(Ranasinghe and Pattiaratchi 2000). The complexities of the interactions amongst

freshwater flow and composition, estuary topography and hydrodynamics, and human

alterations of these features, indicate that numerical models may be important in

quantifying the hydrological responses of estuaries and the resultant changes in water

quality.

The objective of this study was to develop a quantitative understanding of the way in

which the hydrology and water quality of a Western Australian estuary, the Swan

River, have been altered by changes in watershed land use patterns and tributary

regulation associated with European settlement and development. We use a coupled

hydrodynamic-ecological model to make assessments for pre-modification and post-

modification cases, with the major focus placed on the likely changes to phytoplankton

biomass and species composition.

5.3 Study site

5.3.1 General Description

The watershed of the Swan River is large (121,000 km2) and dominated by the Avon

River watershed (120,500 km2). Rainfall varies over the watershed from ~ 900 mm yr-

1 in coastal regions to ~ 300 mm yr-1 in eastern regions. The climate can be considered

to be ‘Mediterranean’, with around 70% of rainfall confined to the winter and spring

months of June through September. Correspondingly, tributary runoff is highly


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seasonal, with little or no flow occurring in the first 4-5 months of each year in the

Avon River. Runoff from smaller tributaries is also highly seasonal, but may vary

from negligible in summer (e.g. Ellen Brook) to continuous in the case of some urban

drains (Donohue et al. 2001). Groundwater inflows, which occur mostly through the

sandy soils of the Swan Coastal Plain, vary little seasonally, and may contribute up to

10% of freshwater inputs to the estuary in summer and fall months, when flows from

surface-fed tributaries are small (Linderfelt and Turner 2001).

In summer and fall, water of marine origin intrudes up the Swan River, along the Swan

Coastal Plain, to approximately 50 km upstream of the estuary mouth at Fremantle

(Figure 5-1). In winter, rainfall and associated streamflow drives the salt wedge

seaward, occasionally close to the ocean entrance at Fremantle in very wet seasons

(Stephens and Imberger 1996). Tidal excursions of the salt wedge are typically of the

order of 1-3 km although synoptic forcing may displace the salt wedge by around 10

km, corresponding to the duration of passage of low- and high-pressure systems

(Hamilton et al. 2001).

The highly seasonal hydrology of the Swan River estuary is reflected in a well-

documented succession of phytoplankton taxa (John 1994; Thompson and Hosja 1996;

Chan and Hamilton 2001). The phytoplankton dynamics are of particular interest in

the upper estuary reaches (~20 km to 40 km from the mouth), from the constriction at

“the Narrows” up to the confluence with Helena River (Figure 5-1), as problematic

algal blooms occur frequently in this region. The high-flow period of winter and early

spring is usually dominated by freshwater diatoms, which are typically succeeded by a

short-lived bloom of freshwater chlorophytes. In summer and fall, estuarine and


139

marine species are dominant and typically show transitions between dinoflagellates

(e.g. Gymnodinium spp. and Prorocentrum spp.) and the cosmopolitan coastal diatom

Skeletonema costatum (Chan and Hamilton 2001). Blooms of dinoflagellates

(Hamilton et al. 1999) and more recently (February 2000) the blue-green alga

Microcystis aeruginosa (Hamilton 2000) are of particular concern in terms of reducing

biodiversity (Chretiennot-Dinet 2001), amenity and long-term impacts on the estuary

ecosystem (Carpenter et al. 1998; Kononen 2001).

5.3.2 Post-European modifications

The hydrology of the Swan River has undergone substantial modifications in the past

century, and it is likely that these changes have also affected phytoplankton

succession. Several dams, notably Canning Dam (Figure 5-1, location 2) and

Mundaring Weir (Figure 5-1, location 5), were constructed for water supply through

the 1900s, restricting freshwater discharges to the estuary. In their original state,

however, these tributaries (i.e. Canning River and Helena River) were unlikely to have

exerted a major influence on winter flows, which are dominated by the Avon River.

However, their relative contribution would have been greater in drier months due to

the proximity of the tributaries to the high rainfall zone near the coast and the extended

period of little or no flow in the Avon River.

In contrast to flow reductions from reservoir construction, clearing of native vegetation

is estimated to have increased flows in the Avon River by 4-5 times over the past 100

years, and has increased groundwater recharge and nutrient and sediment discharges

from the catchment (Viney and Sivapalan 2001). Clearing was particularly widespread

between 1940 and 1970. The subsequent increases in runoff prompted adoption of a


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'river training scheme', in which large sections of the Avon River were cleared of

vegetation ('ripping' of the river bank), then straightened and deepened by bulldozer

(Riggert 1978). It is now generally accepted that these modifications had a severe

impact upon the ecology of the Avon River and led to major problems with sediment

erosion and riverbank stability along many parts of the river (Harris 1996). Perhaps of

even more concern is the progressive increase in salinization, waterlogging and land

degradation in the Avon River catchment, which has resulted from clearing of remnant

vegetation and reduced water loss via evapotranspiration (Harris 1996).

5.4 Methods

A three-dimensional hydrodynamic model (Estuarine and Lake Computer Model; ELCOM) coupled

with an ecological model (Computational Aquatic Ecosystem Dynamics Model; CAEDYM) was used to

simulate physical and ecological processes in the Swan River estuary.

ELCOM has been developed to simulate hydrodynamics and transport in stratified water bodies with

spatially-varying wind stress, surface heat exchange, tidal boundaries and multiple inflows (including

groundwater sources). The simulation method solves the three-dimensional Reynolds-averaged,

unsteady, hydrostatic, Boussinesq, Navier-Stokes and scalar transport equations on a Cartesian mesh.

The hydrodynamic algorithms are a semi-implicit, finite-difference approach based on a second-order

Euler-Lagrange advection of momentum with an implicit solution of the free surface evolution. Scalar

transport uses a conservative discretization of a flux-limiting third-order method. Turbulence modelling

uses a mixed-layer approach in the vertical with constant eddy viscosities for the horizontal. Detailed

descriptions of the hydrodynamic model can be found in Hodges et al. (2000) and Hodges (2000).

CAEDYM consists of a set of subroutines containing a series of equations that describe the major

biogeochemical processes influencing water quality. These include primary and secondary production,

nutrient and metal cycling, oxygen dynamics and the movement of sediment. The equations relevant to


141

the phytoplankton model are described in detail by Griffin et al. (2001), with the exception that no

grazing by zooplankton is included in this application. Zooplankton grazing was considered in this

study to be of secondary importance relative to the effects of advection and transitions between

freshwater and brackish conditions (Chan and Hamilton 2001). The biota were represented in the model

simulations by four taxa of either freshwater and estuarine phytoplankton.

ELCOM and CAEDYM are coupled such that ELCOM simulates salinity and temperature, passing

values for these parameters to CAEDYM for modification of ecological state variables, while

CAEDYM passes the water quality variables to ELCOM for advective and dispersive processes (Figure

5-2).

In this study, the coupled model is applied to a 40 km length of the Swan River estuary, from the mouth

at Fremantle to the confluence with Helena River (Figure 5-1). The simulation grid uses an along-

channel and cross-stream coordinate system that effectively “straightens” the estuary. This approach

neglects effects of curvature in the river, which can be shown to be a second-order effect (Wadzuk and

Hodges, in press). Neglecting the river curvature significantly simplifies the model computations at the

expense of cross-channel processes. While cumulative effects of cross-channel processes are important

in sediment transport and erosion studies, the overall impact should be negligible for the residence time

and flushing rate observed in the upper Swan River. The grid cells have a longitudinal aspect ratio of

10:1, using 1000 m in the along-river direction and 100 m across-river. In the vertical direction, a grid

spacing of 0.5 m is used in the upper 7 m of the domain, increasing incrementally to 2 m in the bottom-

most layer. This paper focuses on results for the Swan River upstream of the Narrows, where the depth

is less than 6 m and is resolved with 0.5 m vertical spacing.

The bathymetry used in the model was obtained from an intensive bathymetric survey (20 m by 20 m

resolution) commissioned by the Water and Rivers Commission over the entire estuary in 1997, and

averaged to the required model grid resolution. Meteorological forcing inputs included solar radiation,

wind, air temperature, humidity and cloud cover, which were entered into the model based on 15-minute

readings taken at Perth Airport, 5 km to the south of the most upstream estuary sampling station.


142

Model boundaries were defined at the ocean entrance, where tidal elevations were prescribed at 15-

minute intervals, and at the confluence with Helena River in the upper estuary (Figure 5-1, location 5),

where daily discharge was entered as a total for the six major, gauged tributaries; Avon River, Ellen

Brook, Susannah Brook, Jane Brook, Henley Brook and Helena River, and for smaller ungauged

tributaries. The latter estimate was made by applying a rainfall runoff coefficient to each catchment area

based on the coefficient derived for the nearest gauged tributary. Other inputs included estimates of

daily groundwater discharge and recharge on the south and north shores of the estuary, based on model

simulations by Linderfelt and Turner (2001), and daily discharge from three gauged urban drains in the

upper estuary and from the Canning River in the lower reaches. Localized surface runoff adjacent to the

estuary was assumed to be 50% of the daily rainfall on the catchment of the lower estuary (Peters and

Donohue 2001). This catchment consisted of a band of land around the estuary perimeter, varying from

0.5 to 1.5 km in width. Daily rainfall was also entered directly onto the water surface of the estuary.

Water quality composition at the upper domain boundary and for the Canning River was derived from

weekly sampling at these stations. Drain composition was derived from fortnightly sampling of one of

the drains and was assumed to be identical for the other two drains, and for diffuse runoff from the

catchment. Composition measurements included salinity, temperature, dissolved oxygen, phosphate,

ammonium, nitrate, total phosphorus, total nitrogen, silica, biochemical oxygen demand and suspended

solids. Composition of groundwater inflows was based on average values from bore tests located in two

transects across the estuary (Linderfelt and Turner 2001). Field measurement of nitrate in rainfall

indicate that peak concentrations coinciding with the peak rainfall volume would result in a nitrate load

of less than 60 kg/yr. The groundwater nitrate load has been estimated at 30-60 t/yr or about 10% of the

nitrogen load in the upper reaches (Linderfelt and Turner 2001). Our calculated rainfall nitrate

contribution is, at most, 0.2% of this. As nitrate was measured at higher concentrations than other

nutrients (ammonium, nitrite, phosphate), direct rainfall was approximated as having negligible solutes.

Data from the model simulations were compared with measured vertical profiles or surface, mid-depth

and bottom samples at 9 stations along the estuary (Figure 5-1). The major focus of this study is the

upper reaches, however, where 6 of the 9 stations are located and where the majority of algal blooms are

reported (Thompson and Hosja 1996). Measured data at the estuary stations included the same

parameters as those measured for the tributaries, as well as chlorophyll a and Secchi depth. Surface (0-5


143

m) integrated cell counts were also taken at each station and differentiated to taxon level. A complete

description of the methods and additional measurements taken for the estuary samples is given in Chan

and Hamilton (2001). For use in the model, cell counts for each taxon were converted to chlorophyll a

as a measure of biomass according to chlorophyll a per cell values given in Griffin et al. (2001).

The model configuration for this study included phytoplankton parameters for their responses to light,

salinity, temperature, nitrogen, phosphorus, silica and carbon, as well as migration and settling

velocities. Additional parameters were required for oxygen exchanges and nutrient cycling. Parameters

were calibrated within the literature ranges observed for similar phytoplankton species or in other

estuarine studies. These parameters included maximum growth and respiration rates, half saturation

constants for nitrogen and phosphorus, light saturation, response to temperature, settling rates, and

salinity tolerances for each of the four phytoplankton groups. The model calibration runs involved

successive runs over one year with the aim to iteratively reduce differences between measured and

simulated variables. The primary focus of the model calibration was to reproduce the observed changes

in phytoplankton biomass and succession over a one-year simulation, but matching concentrations of

nutrients and dissolved oxygen was also an integral part of the calibration.

Four different scenarios were developed to run as separate simulations, based on past conditions in the

estuary. The effect of removing the Mundaring and Canning Weirs was simulated by adjusting Helena

River and Canning River inflows according to gauged monthly inflows to these impoundments. A pre-

European settlement scenario was simulated based on results from a watershed model that specifically

examined inflow volume and composition prior to European settlement (Viney and Sivapalan 2001).

The catchment model factors for reduction of flow (1/5th), phosphate (1/10th), total phosphorus (1/16th),

ammonium (1/4th), nitrate and total nitrogen (1/16th) in the Avon River, Ellen Brook and Helena River,

were applied to the inflow file input for the present-day case, with all other inputs remaining the same as

present. In the third scenario, flow was kept at present-day levels while incoming nutrients were

reduced to pre-European levels as described above. In the final scenario, nutrients were kept at present-

day levels, while flow was reduced to pre-European levels as above.


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5.5 Results

Water quality comparisons between field, model and scenario results, integrated over

the volume of the upper estuary, are presented for salinity (Figure 5-3), inorganic

nitrogen (Figure 5-4), and inorganic phosphorus (Figure 5-5). The measured data are

based on water column means from the six monitoring stations in the upper reaches.

Model salinity results compare well with field measurements for most of the year,

however, there is some discrepancy during fall (days 60-150), with the upper reaches

somewhat fresher than observed values. Similarly, simulations of inorganic nutrients

match field measurements except during this same fall period, when they are lower

than observed values.

Our simulation of biomass for the four primary phytoplankton groups integrated over

the volume of the upper estuary is presented in Figure 5-6, with the corresponding field

data presented in Figure 5-7. This simulation was the outcome of repeated model

calibration runs that were designed to minimize errors between measured and

simulated biomass of phytoplankton groups as well as nutrients and dissolved oxygen.

The primary difference between the measurements and simulations occurs during the

chlorophyte bloom (days 290-350), when the simulated bloom persists longer and the

biomass is higher than observed in the field. A similar, but less pronounced effect is

evident in the comparisons of dinoflagellate biomass. For both phytoplankton groups

(i.e. chlorophytes and dinoflagellates) the simulated decline of post-bloom biomass

could not be reproduced without adjusting parameters outside of literature ranges.

However, the peak biomass and seasonal succession of phytoplankton groups provides

a good predictor of what has been observed in the Swan River. An example of the


145

spatial distribution over the estuary can be seen for two selected days; day 50 in Figure

5-8(a) and day 325 in Figure 5-8(f).

5.5.1 Increased flow in the absence of tributary impoundments

The model results show that removal of impoundments and the resultant increases

predicted to occur in streamflow had a relatively small impact on the dynamics of the

Swan River estuary. In comparison to the present-day (base) case, salinity was

reduced slightly around days 110, 250, and 330 (Figure 5-3), but there was little

difference in nutrient concentrations at any time (Figure 5-4 and Figure 5-5). The

main difference in the phytoplankton community was an increase in the duration and

peak of chlorophyte biomass during the spring bloom (beginning ~ day 310, Figure

5-8(g) and Figure 5-9). Chlorophyte simulations were particularly sensitive to changes

in salinity. This was evident around days 330-340 in the scenario without tributary

impoundments, with reduced salinity allowing a greater window of opportunity for

chlorophyte populations to increase rapidly. It is evident that salinity is the critical

influence on chlorophytes at times of high biomass. However, residence time in the

upper estuary was reduced slightly with this scenario, which may also have affected

the time for chlorophyte growth potential to be realized (Chan and Hamilton 2001).

5.5.2 Pre-European watershed

Under a reduced flow (1/5th) and nutrient (1/4th to 1/16th) regime, as estimated for pre-

European settlement (Viney and Sivapalan 2001), the winter freshwater period is of

shorter duration and salinity is elevated over the base case (Figure 5-3). Inorganic

nitrogen and phosphorus concentrations are lower throughout the year. The

divergence from the base case is most pronounced in winter, when nutrient


146

concentrations reach maximal levels with the commencement of substantial seasonal

freshwater flows (Figure 5-4 and Figure 5-5). The initial concentrations of

phytoplankton and nutrients in the water column for the beginning of this scenario

were identical to those for the base case, but declined steadily through the early phases

of the simulation. The elevated level of marine diatoms near the start of this

simulation was an artifact of the relatively high initial levels of this group. In general,

the effect of reduced levels of nutrients was to reduce the biomass of all phytoplankton

groups (Figure 5-8(c), Figure 5-8(h) and Figure 5-10).

Dinoflagellates, in particular, remained at substantially lower levels throughout the

year than in the base case. The difference in the upper reaches can also be seen in

comparing the day 50 base case biomass transect in Figure 5-8(a) with that of the

scenario shown in Figure 5-8(c). This pre-European scenario would have increased

residence times in the upper estuary, provided greater opportunity for species adapted

to higher salinities to grow, and increased the likelihood of phytoplankton growth

potential being realized (Chan and Hamilton 2001). These effects, however, appear to

be outweighed by reduced levels of nutrients to support phytoplankton growth.

Freshwater diatoms are particularly disadvantaged in this scenario. When winter

inflow begins (~ day 150), simulated salinity through the water column in the upper

reaches decreases to ~13 psu but then immediately increases back to 20 psu and

remains at this level until day 200, while in the base case the upper estuary was much

fresher (< 10 psu) during this period. As a consequence, freshwater diatoms are

mostly outside of their usual salinity tolerance, and their biomass is reduced to one-


147

ninth of peak values in the base case. Similarly, chlorophytes were reduced to around

one-tenth of levels simulated in the base case (Figure 5-8(f) and (h)).

5.5.3 Pre-European watershed without flow reduction

A simulation was run with nutrient concentrations reduced as for the pre-European

simulation, but with inflows unchanged from the present-day (base) case.

Phytoplankton succession and biomass were largely unchanged from the pre-European

simulation which had both tributary flow and nutrient levels altered (Figure 5-8(f) and

Figure 5-11), indicating that reductions in nutrients were largely responsible for the

decrease in biomass over the base case. For chlorophytes, however, while peak

biomass reached only around one-third of levels in the base case, it still exceeded

levels for the pre-European scenario that had both flow and nutrients reduced (Figure

5-8(i) and Figure 5-11).

5.5.4 Pre-European watershed without nutrient reduction

In this scenario, the flow regime was set to the predicted low pre-European levels but

nutrients were set to present-day (base) levels. Salinity is unchanged for this scenario

from the other pre-European scenario (Figure 5-3), but dissolved inorganic nutrients in

the upper reaches are elevated over the base case at times of low flow. Under the low

flow case, an increase in occurrence and duration of stratified conditions produces

hypoxia that enhances sediment nutrient release. Douglas et al. (1996) observed

elevated levels of inorganic nutrients in Swan River bottom waters when hypoxia

occurred under prolonged stratification. Calibration of nutrient release rates on the

basis of these observations and of sediment oxygen uptake rates on the basis of benthic

chamber deployments near location 4 in Figure 5-1 (Herzfeld et al. 2001), provide


148

confidence that the interactions of stratification, hypoxia and sediment nutrient release

may be simulated with some certainty.

Despite the combination of increased nutrient levels and reduced flushing,

chlorophytes do not reach high concentrations in this scenario (Figure 5-8(j) and

Figure 5-12), as high salinity imposes a major constraint on biomass development.

Dinoflagellates become the dominant group (Figure 5-8(e) and Figure 5-12), benefiting

from both higher salinities and higher nutrient concentrations over the summer period.

While nutrient levels are conducive to blooms at any time of the year, the pre-

European levels of flow in winter-spring (days 200-300) are still sufficient for flushing

to prevent high levels of biomass.

5.6 Discussion

Although the phytoplankton seasonal succession and peak biomass is well represented,

the dinoflagellate and chlorophyte groups were of longer duration than observed in the

field. This difference may be attributable to zooplankton grazing, as Griffin et al.

(2001) found previously that grazing hastened post-bloom decreases of dinoflagellate

biomass. The increased duration of simulated dinoflagellate biomass in fall also

partially explains the decreased inorganic nutrients exhibited in the model at this time.

The duration of blooms modelled in the scenarios may thus also be overestimated,

however, due to the ephemeral nature of the phytoplankton blooms in most of the

scenarios, this would only be a factor in the final, low-flow, high-nutrient, scenario

(Figure 5-9).


149

Model simulations indicate that flow, salinity and nutrients are the main factors

influencing phytoplankton biomass and succession, but the influence of other factors

should also be considered. The temperature regime is unlikely to change substantially

under the different scenarios. Reduced temperatures in winter are likely to hinder the

attainment of phytoplankton growth potential, especially when peak winter flows

reduce residence times in the upper estuary to fractions of a day (Chan and Hamilton

2001) Stratification and mixing in the water column are, however, altered by the

changing flow regime between scenarios. Water column stability has implications for

the light climate experienced by the phytoplankton community (Wallace and Hamilton

2000) as well as for nutrient release from bottom sediments (Douglas et al. 1996). In

the Swan River, however, the influence of mixing on light regime is mitigated by the

relatively shallow mixed layer depths, and the potential for light limitation is

considered to be low given the relatively high water clarities that are experienced over

the periods of highest phytoplankton biomass (Chan and Hamilton 2001).

Nutrients in streamflow, and from benthic regeneration under stratification-generated

hypoxia, appear to be the most important factors influencing phytoplankton

productivity in the presence of the hydrological changes that have taken place in the

Swan River watershed since European settlement. These observations are consistent

with others on the Swan River (Thompson 1998) and on other microtidal estuaries

(Mallin et al.1993; Malone et al. 1988) although the latter two studies were in systems

with less seasonality of rainfall and more limited salinity ranges than we observe in the

Swan River estuary.


150

Although the simulations indicate that the greatest effects on phytoplankton biomass

are associated with European settlement and nutrient enrichment, salinity plays an

important role in phytoplankton succession. For example, in Chesapeake Bay, USA,

Marshall and Alden (1990) found that the oligohaline-mesohaline gradients in

estuaries were even more important than variations in nutrients in determining the

composition of phytoplankton communities.

The emphasis of this study was to examine possible changes in phytoplankton

succession due to the impact of major anthropogenic activities on the hydrology of the

Swan River. It should be noted, however, that in addition to the watershed

hydrological changes examined here, there are hydraulic changes that may also have

had a significant impact on the phytoplankton succession and biomass. In particular,

the dredging of a sandbar across the mouth of the estuary for navigation purposes

(Figure 5-1, location 1) is likely to have had an important effect in increasing exchange

of estuarine water with the ocean (Riggert 1978). Removing the sill may have

moderated the effects of increasing nutrient levels by increasing flushing, although

there may be confounding effects related to the tolerance of the various phytoplankton

groups to changes in salinity and stratification.

More recently, rapid growth of the city of Perth (Figure 5-1, location 3) has led to the

transformation of traditionally rural or natural catchments to urban catchments (e.g.

Ellen Brook, Figure 5-1, location 6). Catchment models (Sivapalan, pers. com.)

indicate corresponding increases in stormflow and more rapid response of tributary

inflows to rainfall, due to the increased fraction of impermeable surfaces in urban

areas. Furthermore, development of marinas and boat harbors in the upper estuary


151

(Figure 5-1, location 4), while not unduly influencing the hydraulic residence time of

the entire estuary, may lead to localized variations in water residence time at the scale

of the enclosure. Both types of developments are likely to adversely affect water

quality in parts of the estuary, although on what scale remains uncertain. Modelling of

such developments would be useful for identifying their impacts, and if performed

prior to the inception of development, may assist in planning for the mitigation of any

negative consequences (Hamilton and Turner 2001).

5.7 Conclusions

The coupled hydrodynamic-ecological model ELCOM-CAEDYM has been used to

simulate the effects of post-European development of catchment conditions and

tributaries on the ecology of the Swan River estuary. Phytoplankton succession and

biomass in the estuary are likely to have been affected only slightly by the changes in

hydrology due to impoundment of water in Mundaring Weir and Canning Dam. A far

greater impact is attributable to changing land use of the catchment. Increased

discharge and the associated decrease in salinity have allowed chlorophyte biomass to

increase. Increased nutrient inputs from clearing of native vegetation and expansion of

agriculture have allowed an increase in biomass of all four of the main groups of

phytoplankton and, in particular, diatoms and dinoflagellates. Model results suggest

that the pre-European phytoplankton community was very low in biomass and

dominated by chlorophytes. The dominant impact of the hydrological changes

examined in this study is the increased availability of nutrients.


152

Monitoring and prediction of the impacts of ongoing changes to the catchment of the

Swan River, such as the conversion of rural to urban catchments, is essential if the

impact of such changes on the ecology is to be properly managed.

5.8 Acknowledgments

We thank the Western Australian Estuarine Research Foundation and the Water and

Rivers Commission for funding which made this study possible. This project was also

supported through an Australian Research Council Discovery Grant (DPO211475).

We also thank the Water and Rivers Commission of Western Australia and the

Department of Transport for field data provided for this study. We acknowledge the

contributions of Paul Montagna and two anonymous reviewers for their comments on

the manuscript.

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5.10 Figures

Figure 5-1. Map of the Swan River showing the nine field monitoring sites (��), and the locations of some of the major changes that have had an impact on the estuary hydrology (1-7 described below). Note the narrow constriction (“the Narrows”) between 2 and 3, which delineates the lower basin towards the ocean from the upper reaches.

1. Fremantle Channel – dredged from ~ 2m to ~ 14m (occurred in 1892); 2. Canning River – Kent St Weir (1920s) and Canning Dam (1940); 3. Perth City – Urbanization; 4. Ascot Waters – Boat harbors and marinas (1990s); 5. Helena River – Mundaring Weir (1902); 6. Ellen Brook – agriculture (1950s) and urbanization (1990s); 7. Avon River – clearing and salinization (1900s-), river training (1958-1971).


156

Figure 5-2. Schematic of the coupling between the hydrodynamic model ELCOM and the ecological model CAEDYM.


157

Figure 5-3. Salinity integrated over the upper estuary. Comparison of baseline (1995) case against a scenario with Canning Dam and Mundaring Weir removed, and a scenario with reduced inflow corresponding to a pre-European catchment. Solid line (-) is the baseline case, -.-. is the low flow case, --- is the case without tributary impoundment and x is the field data.


158

Figure 5-4. Dissolved inorganic nitrogen (NO3+NH4) comparison. The solid line () is the baseline case, -.-. is the low flow case, --- is the case without tributary impoundment , … is the low nutrient and low flow case, and x is the field data.


159

Figure 5-5. Filterable inorganic phosphorus (PO4) comparison. The solid line () is the baseline case, -.-. is the low flow case, --- is the case without tributary impoundment , … is the low nutrient and low flow case, and x is the field data.


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Figure 5-6. Relative phytoplankton biomass (chlorophyll a) integrated over the upper estuary for the baseline (1995) case.


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Figure 5-7. Relative phytoplankton biomass (chlorophyll a) integrated over the upper estuary from the field (1995) data.


162

Figure 5-8. Along-river biomass transects from the estuary mouth at left (at the inner edge of the 5 km ocean buffer zone), up to the confluence with Helena River on the right. Dinoflagellates dominated on day 50 (February 19) for (a) the present-day (base) case, (b) the case without tributary impoundment, (c) the pre-European case, (d) the pre-European case without flow reduction, and (e) the pre-European case without nutrient reduction; and chlorophyte dominated on day 325 (November 21) for (f) the present-day (base) case, (g) the case without tributary impoundment, (h) the pre-European case, (i) the pre-European case without flow reduction, and (j) the pre-European case without nutrient reduction.


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Figure 5-9. Relative phytoplankton biomass (chlorophyll a) integrated over the upper estuary for the case with reservoirs (Mundaring Weir and Canning Dam) removed.


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Figure 5-10. Relative phytoplankton biomass (chlorophyll a) integrated over the upper estuary for the case with both inflows and nutrients reduced.


165

Figure 5-11. Relative phytoplankton biomass (chlorophyll a) integrated over the upper estuary for the case with nutrients only reduced, while inflow remains at baseline levels.


166

Figure 5-12. Relative phytoplankton biomass (chlorophyll a) integrated over the upper estuary for the case with inflow only reduced, while nutrients remain at baseline levels.

Chapter 6. Conclusions

167

6 Conclusions

Understanding of phytoplankton dynamics has progressed with the synthesis of diverse

studies in the fields of hydrodynamics, biogeochemistry and ecology, in both marine

and freshwater ecosystems around the world. This study utilised a comprehensive

field data set together with a fully coupled hydrodynamic-ecological model to develop

the synthesis required to interpret phytoplankton biomass and succession events in the

Swan River estuary.

Nutrient regimes of estuaries in Australia differ from many of those previously

described in other parts of the world (Harris 1999). A primary difference is due to the

much lower population densities found in Australian catchments those of heavily

studied estuarine systems in temperate regions of the Northern Hemisphere (Caraco

1995). Additionally, features such as low rates of atmospheric nitrogen deposition

(Holland et al. 1997), and more extreme variation in seasonal and interannual flow

regimes (Puckridge et al. 1998) also impact on nutrient regimes and differentiate them

from estuaries in temperate regions of the Northern hemisphere. This highlights the

need for process-based studies to examine Australian aquatic ecosystems. There also

appear to be significant differences in light climate relative to global patterns, though

the composition of algal communities appears to be superficially similar (Harris 1995).

Data from the Swan River indicate that peak phytoplankton biomass is at the upper

limit of what has been found in the Northern Hemisphere estuaries reviewed by

Boynton et al. (1982). Their study found that peak biomass > 40 �g chlorophyll a L-1

was unusual, though nutrient concentrations were comparable to those found in the

Australian estuaries. However, it is the acute seasonality and high uncertainty in the


168

nature of the freshwater discharge to estuaries in Australia that appears to make them

unique (e.g. Croke and Jakeman 2001). This observation has significant implications

for future research into phytoplankton dynamics in Australian estuaries, particularly

given the findings in the present study.

Prior to commencement of this study, there were relatively few interdisciplinary

physical-biogeochemical modelling studies of estuaries (Hofmann 2000). There are

also few estuaries for which there are routine monitoring data available to differentiate

the biomass of different phytoplankton taxa, though the findings of the present study

indicate that estuarine ecosystem responses to the physico-chemical environment are

both a controlling factor and have high dependence on the phytoplankton species

assemblage. In this context it is notable that the importance of phytoplankton

composition to the function of aquatic systems is receiving increased recognition (e.g.

Sin and Wetzel 2002; Yamamoto and Hatta 2004).

The work presented in this thesis has examined the hydrodynamic and ecological

processes affecting phytoplankton succession and growth in the Swan River estuary in

Western Australia. The study required integration and analysis of physical, chemical

and biological data from an extensive field record (Chapter 3). River flow is the most

robust single predictor of phytoplankton taxa succession and biomass in the Swan

River estuary. Salinity was also identified as an important control on bloom

development. Responses of phytoplankton taxa to both parameters corresponded with

literature values for growth rates and salinity tolerances. The analysis did not reveal

any significant separation of phytoplankton taxa based on temperature or light

availability. Neither nutrient loads nor concentrations showed clear relationships with


169

the different phytoplankton groups, except for winter nitrogen loads, which were

significantly related to the magnitude of the spring chlorophyte bloom. Stratification

of salinity, dissolved oxygen and nutrients also did not have a significant relationship

with phytoplankton succession or biomass. The absence of a clearly discernible

relationship between phytoplankton biomass and species composition, and nutrients, is

attributed to the dominating influence of physical factors such as flow and the resultant

distribution of salinity in the estuary. The unexplained differences in phytoplankton

monitoring data between sampling dates and sites suggests that scaling analysis and

numerical modelling could be used to more accurately direct fieldwork that would

address issues of phytoplankton heterogeneity in the Swan River estuary.

The field data investigation provided key calibration and validation data for the

application of a 3D hydrodynamic-ecological numerical model of the Swan River

estuary (Chapter 4). The model encapsulates in a quantitative manner our conceptual

understanding of processes affecting physico-chemical conditions and phytoplankton

succession, and growth and loss processes, in the estuary. Verification of the

numerical model against field data also confirms the conceptual model of the system,

so that relationships not identifiable from the field data could be tested. Simulations

adequately replicated the primary physical and biochemical characteristics of the Swan

River estuary, and in particular, reproduced the medium- to long-term variation in

dominance and biomass of the major phytoplankton groups. However, a number of

discrepancies between field and model data, such as inability to capture short-term

fluctuations in biomass, indicate where further work may benefit the model to enhance

its application as a predictive tool.


170

Application of the model allowed identification and quantification of which factors

(i.e. salinity, temperature, light, or nutrients) dominate phytoplankton growth over the

year. It also showed the specific influence of advection on the phytoplankton biomass

observed in the estuary, and illustrated the dominance of this factor over a large part of

the year. The importance of advection and our ability to quantify it is particularly

significant in the light of the extreme variability of the hydrological regime noted in

Australian estuaries (Croke and Jakeman 2001), and as hydrodynamics is often

modelled in a simplistic manner (Hofmann 2000).

The field bioassays for phytoplankton nutrient limitation (Thompson 1998) provided

for a direct physiological comparison against model outputs of nutrient limitation.

Model phytoplankton limitation functions were not examined during the calibration of

the model, however the seasonal pattern of relative nutrient limitation behaviour

observed in the field supported the final simulation results. Relative nutrient limitation

in summer was not as extreme as observed in the field, and there was also greater

short-term variability in the simulations. The seasonal pattern for the degree to which

nitrogen was more limiting than phosphorus, was also captured by the model, however

short-term fluctuations were again significant, and there was additional uncertainty due

to the influence of phosphorus limitation. Discrepancies between simulated and field

results reflected periods when other factors such as advection were important. This

comparison highlighted the restricted period during which nutrient limitation plays an

important role in the biomass.

A series of model scenarios were used to delineate the effects of changes in catchment

land use on the phytoplankton community (Chapter 5). The simulations of the


171

individual and collective impacts of increased discharge of water and nutrients arising

from catchment land clearing, and decreased duration of discharge due to weir

impoundments and reservoirs, indicate that phytoplankton succession and biomass in

the estuary have been impacted little by changes in hydrology arising from

impoundment of water. Increased discharge and the associated reductions in salinity

produced an increase in the simulated chlorophyte biomass. Model simulations to

replicate the pre-European situation produced low phytoplankton biomass dominated

by chlorophytes. Despite increased hydraulic flushing and reduced residence times

with catchment land clearing, the large increases in nutrient loads were the dominant

perturbation in the simulations, producing increases in the frequency of blooms of both

estuarine and freshwater phytoplankton taxa in the estuary. By comparison, changes in

salinity associated with altered seasonal freshwater discharge have a limited impact on

phytoplankton dynamics, although they may favour the presence of undesirable taxa

such as dinoflagellates.

6.1 Suggestions for future work

Additional monitoring data collected since those used in this study include a number of

major perturbations that could provide a challenging test of the model. A

cyanobacterial bloom in 2001 has already been simulated with the model presented

here (Robson and Hamilton, in press), and further events (e.g. the Karlodinium micrum

bloom in 2003, Swan River Trust 2003) may provide an additional opportunity to test

the robustness of the model. These tests may extend the applicability of the model for

examining the inter-annual variability of phytoplankton, estuary response to

‘catastrophic’ events, and efficacy of management actions.


172

The Swan River model could also be applied to further assist in improving the design

of field experiments such as nutrient limitation bioassays (Thompson 1998).

Modelling results can assist in selection of representative sites in the estuary and in

selection of critical periods of phytoplankton bloom development and hypoxia,

allowing for effective and targeted field investigations. More process-oriented studies

of phytoplankton behaviour may also be addressed, such as characterization of the

dominant spatial and temporal scales of phytoplankton patchiness and heterogeneity.

Modelling of a scenario with boundary conditions mimicking localized summer

rainfall-runoff events, and rapidly flushing high concentrations of cells and nutrient-

rich runoff into the estuary under conditions of high water temperature would be of

interest in exploring the role of ‘seeding’ in initiation of blooms, as discussed in

Chapter 4. In particular, whether such features could better replicate the in-estuary

variability of biomass observed in the field would be important in further application

of the model.

The modelling and understanding of phytoplankton dynamics and bloom events may

be improved significantly by investigation of the highly variable phytoplankton

physiological characteristics, and integrating this with the broader ecosystem

conceptualizations and macro-scale process descriptions used in this study. A

narrowing of the range in physiological characteristics of the phytoplankton taxa which

exist in the Swan River estuary would be of use, particularly those characteristics

relating to growth and respiration, cell nutrient quotas, and salinity tolerances. Cell

nutrient quotas vary between taxa but may also be highly elastic within individual taxa,

and their refinement could contribute to better predictive modelling efforts. The


173

addition of motility to the dinoflagellate group would be particularly relevant in

alleviating the lack of DO stratification, while there is also scope for further

characterization and parameterization of grazing by zooplankton (e.g. Griffin et al.

2001).

At the opposite end of the spatial scale, further exploration of the connectivity of the

estuary with the catchment, which was initiated in Chapter 5, as well as with the ocean,

is also required. A starting point would be to further investigate the influence of

catchment (e.g. Viney and Sivapalan 2001) and groundwater modelling scenarios (e.g.

Linderfelt and Turner 2001) in the Swan River, and to examine physical perturbations

such as extreme floods or management actions (Baird 1999). Recent establishment of

a monitoring station at the estuary mouth (Fremantle) should enable a more detailed

characterization of the ocean boundary condition. Examination of the effects of using

this data versus an oceanic buffer region is required, and there is still scope for

exploration of the influence of the estuary on ocean dynamics and vice-versa,

particularly with respect to tidal and oceanic influence in the upper reaches, and how

this varies with interannual variation in freshwater discharge to the estuary.

Current monitoring programs implemented by the authorities responsible for

management of the Swan River are aimed at reducing nutrient inputs, though scenario

modelling has shown that long-term hydrological changes can have profound effects.

Short-term perturbations such as floods may exert rapid cause-and-effect changes on

water quality in the Swan River estuary (Robson and Hamilton, in press). Under most

circumstances, however, phytoplankton biomass and succession are strongly


174

dependent on the physical environment which regulates whether phytoplankton have

sufficient time to respond to the physico-chemical conditions.

6.2 References

Baird, D. (1999). Estuaries as ecosystems: a functional and comparative analysis. In: B. Allandon and D. Baird

(Eds.), ‘Estuaries of South Africa’. Cambridge University Press, Cambridge, pp. 269-287.


influencing estuarine phytoplankton production. In: V.S. Kennedy (Ed.), ‘Estuarine Comparisons’.

Academic Press, New York. pp. 69-90.

Caraco, N.F. (1995). Influence of human populations on P transfers to aquatic systems: a regional scale study

using large rivers. In H. Tiessen (Ed), ‘Phosphorus in the global environment’. Wiley and Sons,

Chichester, pp. 235-244.

Croke, B.F.W. and Jakeman, A.J. (2001). Predictions in catchment hydrology: an Australian perspective. Marine

and Freshwater Research 52: 65-79.




Harris, G.P. (2001). Biogeochemistry of nitrogen and phosphorus in Australian catchments, rivers and estuaries:

effects of land use and flow regulation and comparisons with global patterns. Marine and Freshwater

Research 52: 139-149.

Harris, G.P. (1995). The ecological basis of eutrophication - are Australian waters different from those overseas?

AWWA Water 22(2): 9-12.

Hofmann, E. (2000). Modelling for estuarine synthesis. In J.E. Hobbie (Ed.), ‘Estuarine Science, a synthetic

approach to research and practice’. Island Press, Washington, D.C. pp. 129-148.

Holland, E.A., Braswell, B.H., Lamarque, J.F., Townsend, A., Sulzman, J., Muller, J-F., Dentener, F., Brasseur, G.,

Levy, H., Penner, J.E., and Roelofs, G-J. (1997). Variations in the predicted distribution of atmospheric

nitrogen deposition and their impact on carbon uptake by terrestrial ecosystems. Journal of Geophysical

Research 102: 15849-15866.



2653.

Puckridge, J.T., Sheldon, F., Walker, K.F., and Boulton, A.J. (1998). Flow variability and the ecology of large

rivers. Marine and Freshwater Research 49: 55-72.

Robson, B.J., and Hamilton, D.P. (2004). Three-dimensional modelling of a Microcystis bloom event in the Swan

River estuary, Western Australia. Ecological Modelling (in press).

Sin, Y., and Wetzel, R.L. (2002). Ecosystem modelling analysis of size-structured phytoplankton dynamics in the

York River estuary, Virginia (USA). I. Development of a plankton ecosystem model with explicit feedback

controls and hydrodynamics. Marine Ecology Progress Series 228: 75-90.

Swan River Trust. (2003). Karlodinium micrum bloom. Swan Triver Trust, Perth Western Australia.


175



Viney, N.R. and Sivapalan, M. (2001). Modelling catchment processes in the Swan-Avon River basin.


Appendix I

176

APPENDIX I: Modelling phytoplankton succession

and biomass in a seasonal West Australian estuary

T.U. Chan, D.P. Hamilton, and B.J. Robson.

Verhandlungen der Internationale Vereinigung für Limnologie, 28, pp. 1086-1088,

2001

Introduction

Phytoplankton succession and biomass are of particular interest due to their position in

the food web, and the adverse effects phytoplankton blooms may have on estuarine

water quality and biota. Phytoplankton primary production transforms energy and

inorganic materials into organic material with significant implications for not only

phytoplankton biomass, but also the cycling of oxygen, carbon dioxide, nutrients, trace

elements, suspended matter, and for other organisms. Dominance of a particular

species of taxa of phytoplankton will affect these cycles. Understanding the processes

affecting phytoplankton biomass and succession is required to predict and manage the

occurrence of potentially harmful blooms.

The objective of this study was to use a three-dimensional, coupled hydrodynamic-

ecological model, ELCOM-CAEDYM (Herzfeld and Hamilton 1997; Hodges et

al.1998) to validate the environmental factors hypothesized to influence succession and

bloom dynamics of phytoplankton in the upper Swan River estuary, Western Australia.

These hypotheses were originally developed from analysis of a three-year data set

(Water and Rivers Commission, unpublished data), which indicated that flushing and

Appendix I

177

salinity tolerance were of primary importance to phytoplankton succession, and that

nutrients played a lesser role associated with potential biomass development.

Methods

The Swan River estuary in the southwest of Western Australia (Figure A1) experiences a Mediterranean

climate. Marine water is flushed from the upper estuary with highly seasonal, winter freshwater runoff

from the large (121,000km) inland catchment. During late spring and summer, low discharge allow

intrusion of a salt wedge into the upper reaches, microtidal conditions dominate flushing, and the Swan

River is brackish up to 60 km upstream of the ocean (Spencer 1956). The upper Swan River estuary

(Figure A1), is narrow (mean width 150 m), shallow (mean depth 1.5 m) and poorly flushed.

Phytoplankton blooms and hypoxia are frequent in this region (Thompson and Hosja 1996; Hamilton et

al. 1999).

The ELCOM-CAEDYM model was applied to the upper estuary (Figure A1) with a model grid

resolution of 500 m along the river axis, 50 m across the perpendicular horizontal axis and 0.6 m in the

vertical for a period of 1-year using a 5-minute time step. Daily freshwater discharge data were used for

the upstream boundary condition (Figure A2a), and for four tributaries and drains within the domain,

and 15-minute tidal data was applied at the lower boundary. Daily groundwater inflows were added

within the domain according to modelled data from Linderfelt and Turner (2001). Weekly water quality

data for temperature, salinity, dissolved oxygen, pH, PO4, NH4, NO3, Si, TN, TP, suspended solids,

BOD, and biomass for each of the four main phytoplankton groups (marine diatoms, estuarine

dinoflagellates, freshwater diatoms, and freshwater chlorophytes) at stations at the upstream and

downstream boundaries of the model (Figure A1) were used as model inputs for composition at the

boundaries. Biomass data for distinct phytoplankton taxa were calculated from cell counts, using mean

chlorophyll a values per cell from the literature (Griffin et al. 2001). Additional chlorophyll a analyses

at 3 depths and six sites in the upper estuary provided estimates of total phytoplankton biomass.

Appendix I

178

The model was calibrated using a combination of measured physiological parameters and values tuned

within literature-defined values for the four phytoplankton groups; marine diatoms, dinoflagellates,

freshwater diatoms and chlorophytes.

Results and discussion

The seasonal phytoplankton succession for the upper Swan River estuary was

reproduced for the year 1995 (Figure A2b). Marine diatoms dominate the

phytoplankton assemblage during summer (January-March). During late summer and

autumn (April-June), dinoflagellate blooms occur with moderate biomass of marine

diatoms. A freshwater discharge event, beginning on day 158, reduced salinity and

flushed phytoplankton from the estuary, after which time freshwater diatoms displaced

the marine phytoplankton. The freshwater diatoms grew little in the upper estuary

before being advected out, and their biomass was predominantly due to transport in

from the upstream boundary. With the start of the peak flows on day 190 (winter),

phytoplankton biomass reached a minimum. After this high flushing period, a short-

lived chlorophyte bloom occurred around day 270, while the upper estuary was fresh,

but flushing times were decreasing rapidly. Intrusion of the salt wedge with further

reduction in freshwater discharge and increases in residence time, allowed the return of

marine diatoms and dinoflagellates around day 330. The transition between low

residence time and low salinity in winter to increased residence time and higher

salinity in spring, produces a limited window during which chlorophytes can dominate.

Refinements to the model salinity tolerances of this group are expected to more

accurately reproduce their dominance during days 310-340.

Appendix I

179

The simulated biomass was overpredicted during the summer and autumn in particular,

(Figure A2c). Differences between the modelled and observed data are likely to be due

to the use of a number of the parameters taken from the literature to define

phytoplankton behaviour. Many of these were developed under conditions unlike those

found in the Swan River estuary. Further refinement of phytoplankton physiological

parameters, particularly growth responses, which are critical in determining whether

net growth occurs before advection from the estuary domain, and salinity tolerances,

are expected to reduce the error between model simulation and measured data. Thus

far, the modelling has indicated that replication of the seasonal succession with

biomass of the right order is possible with flow and salinity as primary influences.

Further scenario modelling will enable evaluation of management options for

phytoplankton bloom control, for example, control of discharge into the estuary from

water supply reservoirs and weirs, as well as allowing evaluations of responses to

difference climatic conditions.

Acknowledgments

We thank the Water and Rivers Commission of Western Australia for the field data

provided for this study. The authors also thank the Western Australian Estuarine

Research Foundation and the Water and Rivers Commission for the funding which

made this study possible.

Appendix I

180

References




Herzfeld, M. and Hamilton, D. (1997). A computational aquatic ecosystem dynamics model of the Swan River,

Western Australia. - MODSIM '97, International Congress on Modelling and Simulation Proceedings, 8-11

December, 1997, University of Tasmania, Hobart 2: 663-668.

Hodges, B., Herzfeld, M., Winters, K., and Hamilton, D. (1998). Coupling of hydrodynamics and water quality in

numerical simulations. - EOS Trans. AGU, 79(1), Ocean Sciences Meeting Supplement OS11P-1.

Spencer, R.S. (1956). Studies in Australian estuarine hydrology II. The Swan River. Australian Journal of Marine

and Freshwater Research 7: 193-253.

Thompson, P.A. and Hosja, W. (1996). Nutrient limitation of phytoplankton in the Upper Swan River Estuary,


Linderfelt, W. R. and J. V. Turner. (2001). Interaction between shallow groundwater, saline surface water and

nutrient discharge in as seasonal estuary: the Swan-Canning system. Hydrological Processes 15: 2631-

2653.

Appendix I

181

6.3 Figures

Figure A1. The Swan River estuary, box shows modelled area.

Appendix I

182

Figure A2. (a) Freshwater discharge into the estuary, and mean upper estuary salinity; (b) Phytoplankton biomass, for a one year simulation (1992), integrated over the upper Swan River estuary, and smoothed with a one day moving average: marine diatoms (�), dinoflagellates (-.-.), freshwater diatoms (-.-.-), chlorophytes (….); and in situ chlorophyll a: marine and freshwater diatoms (diamonds), dinoflagellates (squares), chlorophytes (triangles); (c) total upper estuary model biomass, and in situ chlorophyll a.

Appendix II

183

APPENDIX II: Reply to examiners’ reports

Individual responses to the examiners’ comments (in italics below) are given

immediately following the italicised points. The main focus has been on the comments

of Prof. Brett, for which the candidate was requested to address comments about

Chapter 3 in particular.

Brett

This is a solid PhD dissertation but it does have some notable gaps. In general this

Dissertation is quite well written and very logically laid out. I think the mechanistic

model presented in chapters four and five was well thought out and implemented. I

was particularly impressed that Chan was able to model four quite different

phytoplankton groups. This effort is a “substantial and original” contribution to our

ability to model shifts in phytoplankton species succession in general and especially in

estuarine systems. The scenario analyses presented in chapter five were well justified

and Chan did a good job of not overselling his results. Chan clearly demonstrates he

has a broad understanding of the relevant estuarine and phytoplankton ecology

literature.

[…]

However, I am requesting that Chan consider a substantial overhaul of his

quantitative framework for chapter three. I have provided a detailed description of

what I feel are the main short-comings of this chapter below.

Chapter Three

This chapter posed the greatest challenge for me. I felt the abstract and initial

introduction were very well written and thought out. However, the study objectives

Appendix II

184

and especially the analytical framework lacked a logical basis. My general feeling

was this chapter was organized as statistical associations in search of hypotheses as

opposed to vice versa. For example, Chan presented several figs (i.e. 3-8 a-d) that

plotted nutrient load against phytoplankton density. However, it is not at all clear why

the nutrient load at any given time should be directly associated with phytoplankton

density.

This paper was designed as an exploratory study for the modelling component of the

study, described in subsequent chapters. The intention of the chapter was therefore to

identify how measured phytoplankton biomass and taxa composition were related to

various physical, chemical and biological attributes of the Swan River estuary. The

assessment of the organization of the chapter as “associations in search of hypotheses”

is thus partially correct, and in this case, intentional and appropriate. It should be

noted, however, that this chapter was already published in entirety in Chan and

Hamilton 2001 (Marine and Freshwater Research 52: 869-884).

For example, the nutrient-phytoplankton biomass relationship of Figs 3-7 and 3-8 is of

interest because it demonstrates clearly the role of phytoplankton biomass in depleting

available nutrients despite the presence of many potentially interacting processes (e.g.

sediment nutrient release, nitrification, denitrification, mineralization, etc.). The

existence of this relationship is of interest as there were no corrections applied between

the times of nutrient uptake and biological response. The relationship between

available nutrients and phytoplankton biomass provided a basis for nutrient uptake

rates that were used in subsequent modelling chapters. Figs 3-7 and 3-8 are also

integral to the discussion of concentration vs loading relationships, and lead into the

winter load vs spring bloom relationship presented in Fig. 3-11.

Appendix II

185

The nutrient load is a useful variable to compare against phytoplankton biomass

(represented as chlorophyll a) as it accounts for a temporal component (via flow rate),

as opposed to an instantaneous measure of nutrient concentration. Phytoplankton

generation and nutrient uptake rates are on the order of days, and an instantaneous

comparison can be misleading in this respect.

Similarly in figs 3-7… Cause and effect are reversed…

With respect to Fig. 3-7, cause and effect cannot be discerned from the available data

“snapshot”, due to temporal effects. This is also discussed in the reply to Brett’s first

comment. It is not stated that the nutrient concentration observed causes the

phytoplankton density observed, or vice versa. In fact, no clear relationship is

observed. Specifically:

“Traditional concepts of phytoplankton bloom regulation are derived from

models for standing waters (e.g. Harris 1986) that are based on the concept that

nutrients regulate biomass. In the case of the Swan River estuary, there is no

clear relationship between DIN, the most frequently limiting nutrient, and cell

numbers. The direct and indirect influences of physical factors on biomass as

well as feedbacks between nutrient assimilation and biomass clearly complicate

predictive relationships in estuaries.”

Furthermore the focus of the discussion is on what influences phytoplankton

succession rather than bloom size/phytoplankton density:

“Nitrogen is the limiting nutrient in the Swan River during summer, and may

be up to 20 times more limiting than phosphorus (Thompson 1998). However,

there is little separation of the different phytoplankton groups with respect to

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186

DIN concentrations, at either the surface or near-bed (Figure 3-7a and b),

which suggests that factors other than nutrients are controlling phytoplankton

succession.”

Chan should have double log transformed the data presented in Figures 3-7 and 3-8

before conducting the statistical analyses. Related concerns concerning Fig 3-10.

Double log transformed data for Figures 3-7, 3-8 and 3-10 are presented as an

alternative in Appendix III (Figures A3 to A5). It was decided not to include these

transformations in the main body of the chapter as this chapter had already been

published and the comments did not represent errors on the part of the candidate. Log

transforming the data did not alter the interpretations of this chapter, and in fact the

features (such as bloom events) are less accentuated in the transformed data (compare

Figure 3-7 with Figure A3, the transformed Figure 3-7, in Appendix III).

It can be seen in the table below that R2 for the untransformed data are comparable to

those for the transformed data. The R2 values indicate that a relatively low percentage

of variation in nutrient concentrations was explained by either of the two flow regimes.

Untransformed data Double log transformed data Surface DIN, low flow R2 = 0.26 R2 = 0.23 Surface DIN, high flow R2 = 0.21 R2 = 0.21 Near-bed DIN, low flow R2 = 0.24 R2 = 0.18 Near-bed DIN, high flow R2 = 0.21 R2 = 0.22

Untransformed data Double log transformed data Surface FRP, low flow Not significant - Surface FRP, high flow R2 = 0.21 R2 = 0.26 Near-bed FRP, low flow Not significant - Near-bed FRP, high flow Not significant -

Appendix II

187

Chapter four

I found chapter four to be a strong and valuable contribution to the literature on

mechanistic modeling of phytoplankton bloom dynamics. I was particularly impressed

by the fact that Chan attempted to model the dynamics of four different phytoplankton

groups! I would recommend this chapter for publication in a good aquatics journal.

My qualms about this chapter mostly concern “minor details”.

On page 91 Chan states that “The model was calibrated and validated” without ever

stating how he was using these terms or even describing how he went about

calibration and validation. This is important because these terms have been used in

very different ways by different authors.

To clarify the terms calibration and validation, the following revision has been made:

(now pages 93-94):

“Calibration was performed by running the model with one year of data (1995)

and adjusting the parameters to attempt to more accurately reproduce the

observed data. These parameters reflect some of the intrinsic variations in

physiology associated with different phytoplankton assemblages, and even

within phytoplankton species or strains that may be due to different life history

stages or responses that are not parameterised within the model. Additionally,

spatial averaging for the grid cells used in the modelling meant that other

parameters such as phosphorus release from bottom sediments may vary over

different spatial scales from those used in the model due to heterogeneity of

sediment properties (e.g. porosity, organic matter, biochemical oxygen demand,

Appendix II

188

mineral composition). The calibrated parameters are given in Tables 4-4 and 4-

5.

Validation of the model was carried out using field data from 1996 and 1997,

using identical parameters to those calibrated for the 1995 field data set.”

He should have reported some statistical measures of model fit and error. Because of

the way he presented his data (i.e. thin time series for model predictions and large

symbols for observed values, with long skinny plots and the results for surface and

near bottom overlaid) it is very difficult to figure out when the model predictions

actually match the observed values. It is critical in cases like this the modeller provide

some summary statistics.

I agree that there may have been some loss of clarity of presentation, but this was a

result of presenting a large number of variables over a large area and long period of

time. Separate plots for surface and near-bed, or larger figures, would have become

unwieldy as there are already 20 figures presented of variation in estuarine variables.

However, to address the examiner’s point about the difficulty of matching predicted

and observed values, Tables A1 to A21 have been added in Appendix IV with the

directly corresponding data for the field measurement times and monitoring sites. It

should be noted, of course, that model output is for a 1,000 m by 100 m by 0.5 m box

averaged over 10 minutes, whilst the field data is a point in space and time. In

addition, aliasing is a problem in periodically driven systems such as this (due to tidal

forcing and daily insolation cycles), and differences in the modelled data may occur

Appendix II

189

due to the large gradients in variables over time and space. The continuous time series

presented in the figures in Chapter 4 may thus be a better comparison in some ways.

Additionally, summary statistics in the form of variance (R2) for each variable are also

included in Appendix IV and also near the beginning of the discussion for each

variable in chapter four.

Chapter 5

Since chapters four and five are closely related I have similar views about both.

Chapter five is an interesting and well presented scenario analysis of potential impacts

from anthropogenic factors on phytoplankton bloom dynamics in the Swan River

estuary. Because the model calibrated and validated in chapter four is used for

scenario analyses in chapter five, it is particularly important that the reader be

provided with objective measures of model performance for key model variables.

The direct comparison data and measures of model performance (the summary

statistics) for key variables presented in Appendix IV (Tables A1 to A21) and chapter

four, added for Brett’s previous comment addresses this point.

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Paerl

This is an exemplary thesis. The work presented in this thesis represents an excellent

synthesis of field observational, experimental and modeling work addressing the

interactive controls that nutrient supply, hydrodynamics (discharge/flushing/residence

time), climatology and seasonality exert on the composition, activity and bloom

dynamics of the phytoplankton community of the Swan Estuary. The individual

chapters confer and verify earlier work, and also shed new perspectives on the

interactions of hydrologic and nutrient forcing features as they pertain to shaping

phytoplankton community responses on seasonal and inter-annual scales […] The

products of his modeling efforts will prove useful both in basic research and

management communities […] I think this piece of work more than fulfils the thesis

requirements (as I understand them) for the PhD degree […] Below are a few specific

comments that may prove useful to consider in clarifying the thesis.

P 47. What is the “hysteresis” that the authors are talking about? (Fig.3-3b)

This has been clarified in the text (now page 49):

“There is also hysteresis over the annual seasonal cycle (Figure 3-3b), where

for a given discharge, salinity is substantially higher in autumn-winter than in

spring when the salt wedge intrudes more slowly back up the estuary.”

P 47. and P 58. cell number-DIN relationship due to limitation by this nutrient

(Now pages 51 and 60) As discussed for Brett’s second comment about “cause and

effect”, Figs 3-3 to 3-7 were intended to demonstrate the complicated nature of the

relationship between available nutrients and phytoplankton biomass.

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191

P 86. No cyanobacteria involved in the modeling efforts?

(Now page 89) As mentioned in section 3.5.4 (and also later in Chapter 5),

cyanobacteria had never been an important taxon in the Swan River estuary and were

therefore not included as a state variable in the modelling carried out for this chapter.

Since the modelling undertaken for this study, there has been a major freshwater

cyanobacterial bloom in the Swan River estuary (Robson and Hamilton 2003, 2004).

Inclusion of cyanobacteria was considered outside the scope of the present study (see

discussion in Chapter 6).

P 163. “Nutrient regimes of estuaries in Australia differ from those commonly

described in the Northern hemisphere” seems overly general and simplistic. I think

you’ll find as much variability among the Northern hemisphere estuaries as between

them and Southern hemisphere estuaries.

(Now page 167) The reviewer raises a valid point, and the passage concerned has been

amended to:

“Nutrient regimes of estuaries in Australia differ from many of those

previously described in other parts of the world (Harris 1999). A primary

difference is due to the much lower population densities found in Australian

catchments those of heavily studied estuarine systems in temperate regions of

the Northern Hemisphere (Caraco 1995). Additionally, features such as low

rates of atmospheric nitrogen deposition (Holland et al. 1997), and more

extreme variation in seasonal and interannual flow regimes (Puckridge et al.

1998) also impact on nutrient regimes and differentiate them from estuaries in

temperate regions of the Northern hemisphere.”

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Bormans

The thesis is interesting and encapsulates well an improved understanding of the

combined effects of environmental factors on biomass and succession of phytoplankton

in the Swan estuary. I liked it because it combines data analysis, model application

and scenario testing. It is also well written. The presentation is slightly repetitive but

the author acknowledges the reason for it […] This thesis brings about new

understanding and useful way to test management actions in the future. A more

thorough literature review and more details on the coupled model were however

expected.

Chapter 2

Not all the physical factors identified were discussed (i.e. wind, atmospheric pressure,

temperature), and some discussed (pH) were not on the initial list.

The initial list of physical factors (page 18) has been amended to include only those

considered important enough to be reviewed. pH has been added to this initial list, and

a discussion of temperature has been added:

“Temperature is important in any biological process. The so-called “Q10 rule”

predicts that growth rates will double for every increase in temperature of 10º C

(Eppley 1972). The photosynthetic response of phytoplankton to temperature

has been demonstrated in numerous studies (e.g. Platt and Jassby 1976,

Davison 1991). Phytoplankton also have preferred temperature ranges outside

of which they will grow sub-optimally and die at an enhanced rate (Geider

1998). In the Swan River, surface water temperatures from 10 to 30º C

(Thompson 1998) suggest temperature will have a significant influence on

phytoplankton dynamics.”

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193

The structure changes with the specific relevance to the Swan Estuary presented later

in a different section on the study site. It would be better to keep the same format all

the way through

The “study site” section (section 2.4) provides a synthesis of the specific local

information relevant to phytoplankton dynamics in the Swan River estuary. The

previous section (section 2.3) is a highly structured introduction and background to the

individual factors affecting phytoplankton dynamics. In this section specific factors

(pH, nitrogen, etc.) can be introduced in discrete subsections. It would not be

appropriate to keep discussion of these factors separate when discussing the study site.

The section on tides should be more detailed with mechanism of generation, and why

some estuaries are micro, or macro tidal.

Additional tidal information included on pages 19-20 (section 2.1.2):

“The Swan-Canning system is a microtidal estuary (Burling 1994). Microtidal

estuaries occur when the tidal amplitude is too low to alter the physical

conditions of the estuary; this is generally defined as tidal amplitudes of less

than 2 m. Tidal amplitudes are affected by global topography, where

propagation of a tidal wave is influenced by landmasses, and dissipation of

tidal energy and amplitude by ocean-bed bathymetry (Dyer 1973). Local

topography is also highly significant, particularly when there are islands in a

water body, or when it is enclosed within bays and estuaries. At the mouth of

the Swan River, spring tide is approximately 0.65 m in amplitude, while neap

tide is approximately 0.2 m (Burling 1994). In this microtidal regime,

atmospheric pressure systems can have a significant influence, producing

variations in water level of up to 0.3 m on a time-scale generally several times

Appendix II

194

longer than the astronomical tide (Burling 1994). The tidal excursion in the

Swan River estuary (i.e. the distance upstream and downstream that the salt-

wedge moves over a tidal cycle) is 2 to 4 km. The regime is mainly diurnal in

summer and winter, with smaller semi-diurnal tides occurring in spring and

autumn (Thurlow et al. 1986; Douglas et al. 1996).”

I was expecting a section on the different phytoplankton taxa. Are they motile, fix

nitrogen what are their sizes, silica requirements, buoyancy, which ones are

undesirable taxa, etc.

Section 2.3 has been expanded to include more detail on the phytoplankton taxa (pages

28-29):

“In general, diatoms grow quickly and settle or decompose rapidly. They are

easily digestible by grazers, and have high nutritional value (Griffin et al.

2001). They are non-motile, and non-nitrogen-fixing. A defining factor is their

requirement for silica, which they use in construction of highly differentiated

cell walls. They may be unicellular or colonial and are comprised of both

freshwater and marine species (Dodge 1973). Overall, diatoms are generally

regarded as relatively benign in most aquatic systems.

In contrast, dinoflagellate proliferations may be problematic, often being toxic

or inedible to zooplankton, and they may form “red-tides” (Schöllhorn and

Granéli 1993). Dinoflagellates are usually unicellular flagellates and motile,

allowing them to accumulate into dense aggregations, which may give them a

competitive advantage by allowing access to elevated nutrients in the near-bed

region, and elevated light levels in surface waters (Malone et al. 1988). Most

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195

dinoflagellates are marine, although there are some freshwater species.

Defining features include two flagella and a transverse or spiral girdle (Dodge

1973). Next to diatoms, they are the most numerous primary producers in

coastal waters.

Chlorophytes are a large group of phytoplankton, usually found in freshwaters

(Wetzel 1983). They are morphologically diverse and may be motile with

multiple flagella. They may be unicellular, colonial or filamentous (Matto and

Stewart 1984).”

When the literature review is discussed (Swan, Canning, etc.) the reader has no idea of

locations yet.

Another replication of a map of the Swan River was considered unnecessary. The few

site specific references (pages 32-34) have been amended for clarity, and cross-

references to the later maps included.

The word Swan River should be on Fig 4.1 cf Canning River. Helena River is not on

Figure 3.1 as said in the text

Both figures amended.

P 22. When does anoxia set in, the level of DO (mg/L) should be identified

‘Hypoxia’ and ‘anoxia’ now explicitly defined for low DO (hypoxia) < 2 mg L-1, and

no DO (anoxia) = 0 mg L-1, and amended throughout.

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196

P 23. The author should describe the light climate for the Swan Estuary from previous

studies?

Paragraph added:

“In the Swan River estuary, water clarity is highest just before winter rains

(April-May), and reaches a nadir in August (Thompson 1998). Thompson

(1998) also found that light climate ranged considerably along the estuary

except during the period of peak clarity, with lower water clarity in the upper

reaches.”

What about a section on temperature and its importance for growth?

A paragraph has been added on the influence of temperature on phytoplankton (pages

23-24), however, it should be noted that this addition is minor, as temperature was not

shown to have a significant effect in this system:

“Temperature is important in any biological process. The so-called “Q10 rule”

predicts that growth rates will double for every increase in temperature of 10º C

(Eppley 1972). The photosynthetic response of phytoplankton to temperature

has been demonstrated in numerous studies (e.g. Platt and Jassby 1976;

Davison 1991). Phytoplankton also have preferred temperature ranges outside

of which they will grow sub-optimally and die at an enhanced rate (Geider

1998). In the Swan River, surface water temperatures from 10 to 30º C

(Thompson 1998) suggest temperature will have a significant influence on

phytoplankton dynamics.”

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197

Chapter 3

P 41. We need a reference for nutrient limiting biomass and not growth rate.

References added and differentiation between nutrient limitation and growth rate

effects clarified in the text:

“The development of phytoplankton blooms in estuaries is closely linked to

advection and mixing rates (Cloern 1996; Eldridge and Sieracki 1993),

availability of nutrients (Egge and Asknes 1992; Ornolfsdottir et al. 2004), light

(Cloern 1987), temperature (Nielsen 1996), grazing rates and the interactions

amongst these factors (Marshall and Alden 1990). The effect of growth limiting

nutrients on phytoplankton has been a specific focus of many studies (e.g.

Fisher et al. 1988; D’Elia et al. 1992; Cooper and Brush 1993)”

and

“Our hypothesis is that flow regime dictates whether or not a bloom can occur

according to growth rate of the relevant phytoplankton taxa (Alpine and Cloern

1992), while nutrient availability may govern the potential size of the bloom

(Mallin et al. 2004).”

References added in this section: Alpine and Cloern (1992), Mallin et al. (2004),

Ornolfsdottir et al. (2004).

P 44. What size bottles are collected, how much water is filtered for Chl a extraction?

(Now page 45) This is clarified in the text:

“Water samples were taken at the surface, 1 m depth, and bottom (0.5 m from

the bed) by pumping water to the surface for distribution into pre-washed 500

mL polyethylene containers. Samples were immediately divided in two and

one sub-sample (100 mL) of each pair was filtered through 0.45 µm cellulose

Appendix II

198

nitrate filter paper before placing both subsamples, and filter paper (protected

from light), on ice.”

P47. I don’t see a reference to Fig 3.2 and Fig 3.3c is Fig 3.4.

(Now page 49) Omission and typographical error amended.

There is no discussion of cells/ml versus Chl-a for the different groups. No discussion

of taxa sizes.

Chlorophyll a variation is discussed in section 4.4.3.3:

“…There will also be problems in trying to simulate the changes in chlorophyll

a within phytoplankton cells, which may vary more than five-fold depending

on light and nutrient history (Geider et al. 1998). Previous studies have used

mechanistic models of physiological changes within cells to simulate algal cell

chlorophyll a content, however, the additional processing power required to

model this process is likely to be prohibitive in a full ecosystem model such as

ELCOM-CAEDYM. An alternative suggested by Flynn (2003) uses an

empirical relationship between environmental parameters and the chlorophyll

to biomass ratio, though divisions of phytoplankton into physiologically broad

groups smoothes much of the inherent variability in this relationship.”

A short note on taxa size is now also included in the literature review section (section

2.3, page 28):

“The different groups vary widely in appearance, physiology, and dynamics

(Capblancq and Catalan 1994). Additionally, each group is sufficiently varied

that different species may range in cell size from around 2 �m up to 2 mm in

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199

diameter (Banse 1976; Snoeijs et al. 2002). Generally however, dinoflagellates

are relatively large, e.g. length of Scripsiella ~ 25 �m, while diatoms and

chlorophytes are smaller, e.g. Skeletonema ~ 13 �m and Chlamydomonas at

around 12 �m (Griffin 2000). However, formation of multicellular colonies is

a complicating factor, with some diatoms sometimes forming colonies, while

dinoflagellates are usually solitary (Peperzak et al. 2003), as is

Chlamydomonas (Agusti and Philips 1992), the dominant chlorophyte in the

Swan River.”

P53. Tidal prism is not explained

(Now page 55) This is clarified in the text:

“The time for flushing due to tides was calculated using a tidal prism (Dyer

1997) based on tidal amplitudes and excursions. The tidal prism is the three-

dimensional shape of the oceanic water within a river or estuary as it moves up

the channel.”

How were the loads calculated?

The method of load calculation is clarified in the text (page 51):

“The conversion to loading was performed by interpolating nutrient

concentrations to a daily timestep and multiplying by the measured daily flow

rates, and summing at appropriate monthly, seasonal and annual intervals.”

Change in nomenclature diatoms (ch 4) versus bacillariophyta (ch2 and 3), and

dinoflagellates vs dinophyta.

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200

Nomenclature in Chapters 2 and 3 has been changed to be consistent with Chapters 4

and 5. Diatoms, dinoflagellates and chlorophytes are now the default throughout,

although the alternative Bacillariophyta, Dinophyta and Chlorophyta are mentioned in

the introduction.

P59 A bit more details (reference to Figure 3.11d) could be given about the link

between nitrate and chlorophytes the following year.

(Now page 61) Additional detail given:

“The seasonal averages of nutrients, discharge and phytoplankton cell counts

indicate only three clearly related variables. Inter-relationships of flow, nitrate,

and chlorophytes (lagged by one season) suggest that nutrients (nitrate in this

case) carried into the system in winter flows partly determine the magnitude of

subsequent spring chlorophyte blooms. It is hypothesized that nitrogen stored

from the winter nitrogen load in estuarine sediment is released under hypoxic

conditions the following spring, enhancing productivity of phytoplankton.

However, once nitrogen enters the biota in spring, tracking its fate becomes

more complex due to changes in its form and location, with multiple pathways

(sediments, water column, phytoplankton), and differing timescales affecting

its cycling. This complex processing may disguise relationships with the

diatoms and dinoflagellates, which dominate in the subsequent 1-2 seasons.”

P75 no mention in the text of high DIN range at one flow value 5000 ML/d.

(Now page 77) The high DIN actually occurs over at least three different flow values

near 5000 ML/d, which contribute significantly to the regression lines identified (Fig

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201

3-10a (i) and Fig 3-10b (i)). This is discussed in the text on page 52 (section 3.4.4) and

page 59-60 (section 3.5.2).

Chapter 4

A more detailed explanation of the equations in CAEDYM should be included

An additional table has been added to Chapter 4 (now Table 4-2) to clarify the

equations in Table 4-1. Footnotes cross-referencing the symbols used in Table 4-1

have also been added to their definitions in Tables 4-4, 4-5 and 4-6.

How are ELCOM and CAEDYM coupled? Mentioned in Ch 5, but should be

described in Chapter 4.

A description of the ELCOM-CAEDYM coupling has been added in Chapter 4 (page

88, section 4.3.1):

“ELCOM passes the physical model variables (primarily salinity and

temperature) to CAEDYM for modification of ecological state variables at each

time step, while CAEDYM passes the water quality variables to ELCOM to

compute the advective and dispersive transport processes.”

There are no indications of depth of the system in Chapter 4. More graphs like Fig 5.8

would be useful. Need a grid of the model grid, bathymetry.

Unfortunately, the proportions of the study site (as described in the text, e.g. p 43-44)

made it extremely difficult to produce a useful bathymetric contour map. The

narrowness of the upper reaches and constrictions such as The Narrows (site 3) and

Blackwall Reach (site 1), as well as the steepness of the channel sides in comparison to

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202

the broad shallow nature of most of the lower reaches made it difficult to produce an

overview figure.

What is TP made of in the model besides PO4 and algal biomass?

The composition of TP has been clarified in section 4.4.3.2 (page 97-98):

“Total phosphorus (TP) concentrations are comprised of phosphate, algal

biomass and particulate and soluble P. The particulate and soluble P consist of

both organic and inorganic constituents. Organic phosphorus from excretion by

phytoplankton is assumed to be converted rapidly to inorganic form.”

What equations govern change in TP and TN?

This is also clarified in section 4.4.3.2 (page 98):

“TP (and total nitrogen, TN) are conserved within the modelled domain, except

(a) boundary exchanges, where addition to and removal from the domain

occurs via inflows and outflows; (b) sedimentation of phytoplankton/particulate

matter to the bed (Stokes settling); and (c) the release of nutrients from bottom

sediments (equations detailed in Table 4-1).”

Are [TP and TN] linked to Carbon?

TP and TN are not directly linked to carbon. They are indirectly linked to carbon via

the minimum and maximum ratios allowed for internal nutrients in phytoplankton,

which affects nutrient uptake. TP and TN will also be linked to carbon as the settling

of phytoplankton cells and their removal from the modelled domain will have a

correlated effect on net removal of N, P and C from the system (within the specified

ratio boundaries).

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203

I would like a feel for advection velocities and tidal velocities throughout the year and

with depth. Is wind ever an issue to disperse phytoplankton blooms? Can it explain

some of the spatial heterogeneity observed?

Figure A6 has been added in Appendix IV showing modelled surface and near-bed

velocities at the nine field sites in 1997. Note that velocities were not part of the

monitoring program, so field data are not available for direct validation. Salinity, as a

conservative tracer, was considered a proxy for validation of water movement.

Robson and Hamilton (2004) found surface wind effects were an issue for

phytoplankton bloom distribution in the Swan River estuary, but only for buoyant

cyanobacteria which formed surface scums. These blooms did not occur in the Swan

until after the period examined, and thus wind effects would not have been significant

in this study. It should also be noted that due to the sinuous nature of the channel,

particularly in the upper reaches, the maximum fetch is only 2-3 km.

Because of the notorious spatial heterogeneity of phytoplankton (discussed for the

Swan River on page 101), phytoplankton data were aggregated for the whole of the

upper reaches. The role of advection in and out of small domains is thus minimized.

Additionally, the effect of cell advection is specifically accounted for in, for example,

Fig. 4-15 to Fig 4-18 (see also section 4.4.3.3).

It might also be noted that previous studies where advective and tidal velocities have

been a focus include Kurup et al. (1998) and Hamilton et al. (2001).

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204

Chapter 5

In general where a reference is given there is no explanation about where that

particular study was done to examine similarities, c.f. Douglas observations of anoxia.

Where sites are not specified, they are specific to the Swan River. In this chapter in

particular, because it is a very site specific study, almost all the references are Swan

River-based. The observation of Douglas et al. (1996) of anoxia (page 147, section

5.5.4) has been amended to:

“Douglas et al. (1996) observed elevated levels of inorganic nutrients in Swan

River bottom waters when hypoxia occurred under prolonged stratification.”

In preceding chapters also, where the location of a referenced study is relevant, the

location is mentioned (e.g. page 56, section 3.5.1, discussing Cloern (1983) in north

San Francisco Bay).

P 132. There is a need for more references to other Australian work (cf Gippsland

Lakes study)

(Now pages 136-137) Additional Australian studies relevant to the research have been

added to section 5.2: Sewell (1982), Young et al. (1996), Webster et al. (2000),

Webster and Harris (2004).

P135 why is a microcystis bloom a concern for biodiversity?

(Now page 139) The impact of algal blooms on biodiversity via hypoxic/anoxic events,

toxin production, and general competition for resources (nutrients, light) is well

documented. Relevant references pertaining to the effects of Microcystis blooms on

biodiversity have been added in the text (Carpenter et al. (1998); Kononen (2001);

Chretiennot-Dinet (2001)).

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205

P143 I’d like to see more about how much nutrients are coming from upstream or

regenerated

(Now page 149) This is discussed in part in Chapter 4.4.3.2 (page 97 on) and

represented in Fig. 4.7-4.10. A more extensive discussion is outside the scope of this

chapter, and generally outside the focus of this study, as phytoplankton dynamics is the

major focus. The recommendations for further work in the final chapter provide a

number of areas in which an examination of these factors might be included.

P163. The word disparate is rather negative. In the results (Chapter 3 and 4) there is

no discussion on the level of Chl-a observed except in Ch 5 when compared to

overseas studies > 40 ug/L.

(Now page 167) ‘Disparate’ is replaced with ‘diverse’:

“Understanding of phytoplankton dynamics has progressed with the synthesis

of diverse studies in the fields of hydrodynamics, biogeochemistry and

ecology, in both marine and freshwater ecosystems around the world.”

Because levels of chlorophyll a are so variable within cells and between groups (as

discussed in section 4.4.3.3, see also earlier reply to this examiner regarding

chlorophyll a in Chapter 3) as well as between systems (the reason for referencing

Boynton et al. (1982)’s review of estuarine studies) this study focuses instead on

relative levels of the major phytoplankton taxa and the interaction between these

groups within the Swan River estuary. Additionally, chlorophyll a data was only

available for total phytoplankton biomass in the Swan River, which was not of use in

examining the dynamics between the main phytoplankton taxa.

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206

APPENDIX III: Additional figures for Chapter 3

“Analysis of the effects of physico-chemical factors on

the Swan River estuary phytoplankton succession and

biomass in the field”.

Figures to supplement those from the published paper in Chapter 3 (Chan and

Hamilton, 2001) are included in this Appendix.

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207

Figures A3 to Figure A4 (alternatives to Figures 3-7 to 3-8) all use: Diatoms �, dinoflagellates �, chlorophytes �, cryptophytes �, cyanophytes �, and chlorophyll a x.

Figure A3 (double-logged version of Figure 3-7). Log transformed nutrient concentrations and phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary: (a) surface DIN, (b) near-bed DIN, (c) surface FRP, and (d) near-bed FRP.

Appendix III

208

Appendix III

209

Figure A4 (double-logged version of Figure 3-8). Nutrient loadings vs. phytoplankton cell counts and biomass for all stations sampled in the upper Swan River estuary. (a) Surface DIN, (b) near-bed DIN, (c) surface FRP, and (d) near-bed FRP.

Appendix III

210

Appendix III

211

Figure A5 (double-logged version of Figure 3-10). Flow vs. nutrient concentrations in the upper Swan River estuary. (a) Surface DIN (i) For flows <5000 ML d-1 (circles), DIN = 1.7x10-1 x Flow – 1.37, R2 = 0.23. (ii) For flows >5000 ML d-1 (diamonds), DIN = 8.2x10-1 x Flow - 3.42, R2 = 0.21. (b) Near-bed inorganic nitrogen. (i) For flows <5000 ML d-1 (circles), DIN = 1.2x10-1 x Flow - 1.28, R2=0.18. (ii) For flows >5000 ML d-1 (diamonds), DIN = 7.6x10-1 x Flow - 3.20, R2 = 0.24. (c) Surface FRP. For flows <5000 ML d-1 (circles), no significant relationship. For flows >5000 ML d-1 (diamonds), FRP = 6.8x10-1 x Flow – 4.08, R2 = 0.28. (d) Near-bed FRP, no significant relationship.

Appendix IV

212

APPENDIX IV: Additional data for Chapter 4

“Three-dimensional modelling of processes

controlling phytoplankton dynamics in the Swan

River estuary”.

Table A1. Surface salinity, 1995, R2 = 0.78. 1995 FIELD DATA, BED MODEL DATA, BED SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 3.5 35.0 34.6 33.2 28.5 23.0 22.5 18.0 16.5 13.9 35.0 34.6 33.2 28.5 23.0 22.5 18.0 16.5 13.9 11.5 34.0 35.1 33.9 28.0 23.5 22.5 18.0 16.0 14.5 36.7 35.5 33.7 28.1 21.6 22.9 20.5 18.8 18.7 17.5 - - - - - - - - - 36.3 34.9 33.0 29.0 24.2 24.3 20.9 19.2 18.2 24.5 - - - - - - - - - 35.7 34.9 33.3 29.0 25.6 24.7 20.9 18.9 18.3 31.5 35.9 36.0 35.1 1.5 26.7 25.7 22.2 20.5 22.0 35.6 34.2 33.2 30.3 25.5 24.1 21.4 19.5 19.1 38.5 36.1 36.2 35.3 26.8 27.1 26.5 22.2 20.5 18.3 35.1 34.2 33.1 30.7 25.0 24.8 21.2 18.7 18.4 45.5 36.4 36.4 36.4 33.3 29.8 28.7 14.3 23.7 21.7 35.4 34.2 32.5 30.8 24.8 24.3 21.0 19.1 18.6 52.5 36.3 36.6 36.0 32.8 - - - - - 34.3 33.3 33.1 28.6 25.3 24.8 22.7 21.9 21.7 59.5 - 36.9 36.3 33.8 29.6 28.9 26.4 24.9 23.0 33.7 32.7 31.4 29.3 26.0 25.0 23.0 22.2 22.0 66.5 37.9 37.9 37.3 34.5 31.7 31.1 27.3 26.5 24.4 34.0 33.2 32.3 29.3 25.6 25.2 22.4 21.2 21.5 73.5 37.4 37.6 37.2 34.5 31.7 31.1 27.6 25.2 24.7 33.9 32.8 32.1 29.6 25.3 25.3 22.3 21.4 21.0 80.5 37.2 37.6 37.9 35.1 32.9 32.6 29.1 28.4 26.9 34.6 33.1 32.2 29.9 25.7 25.8 22.0 21.5 21.2 87.5 37.2 37.9 37.4 34.4 32.0 31.1 29.5 27.4 26.5 33.4 32.7 31.4 28.8 26.3 25.4 23.2 23.9 23.8 94.5 39.0 38.4 37.9 34.9 33.7 33.1 30.6 29.2 28.4 33.8 33.3 31.9 29.3 25.9 25.8 23.8 22.8 23.2 101.5 37.3 37.9 37.3 34.0 32.8 31.3 28.7 27.2 26.3 33.4 33.5 32.6 29.0 27.1 26.2 23.5 23.2 23.1 108.5 - - - 35.1 32.6 33.1 30.9 29.9 29.0 34.7 33.0 32.0 30.0 26.2 26.3 24.4 23.6 23.9 116.5 36.8 37.1 37.3 34.5 32.7 32.4 30.5 29.0 28.3 33.0 32.0 31.6 28.7 26.6 26.3 22.9 22.5 23.1 122.5 37.9 38.1 37.5 34.9 33.9 33.7 30.9 29.7 29.0 33.0 32.9 31.6 28.9 26.7 26.6 24.4 23.6 23.7 129.5 39.1 39.5 39.6 37.7 34.9 33.4 31.7 29.9 29.3 32.2 31.8 32.0 29.0 27.0 26.2 24.2 25.0 25.3 136.5 35.0 35.0 33.3 27.8 20.9 18.8 14.3 13.3 10.2 31.5 31.3 31.2 28.1 26.4 26.3 25.1 24.6 23.1 144.5 - - 24.7 16.5 11.3 11.4 12.2 10.6 8.2 31.3 29.9 26.5 19.6 13.3 15.4 15.4 15.3 15.1 150.5 33.2 29.6 27.3 16.1 12.0 11.2 2.0 9.8 8.7 30.3 27.8 26.6 19.1 19.3 18.7 15.5 15.7 14.7 164.5 16.0 12.3 5.4 5.1 5.1 5.1 5.4 5.5 5.5 21.5 17.2 9.9 6.6 5.9 5.6 5.3 5.6 5.5 178.5 25.3 20.4 10.8 6.7 5.7 5.0 4.5 4.8 4.8 27.2 24.9 13.7 6.4 5.0 4.9 4.8 4.8 4.8 192.5 - - - - - - - - - 25.5 28.0 11.7 3.6 2.9 3.0 3.0 3.0 2.9 199.5 6.3 6.2 2.8 2.8 2.8 2.8 2.9 3.0 3.0 9.5 9.1 4.8 2.1 2.0 2.0 2.0 2.0 2.0 206.5 5.3 3.8 2.3 2.1 2.1 2.1 2.0 1.9 2.0 9.0 6.8 2.0 2.0 2.0 2.0 2.0 2.0 2.0 208.5 - - - - - - - - - 6.7 5.5 2.0 2.0 2.0 2.0 2.0 2.0 2.0 213.5 2.9 2.4 2.0 2.1 2.2 2.3 2.4 2.4 2.4 6.3 4.2 2.3 2.4 2.4 2.4 2.4 2.4 2.4 220.5 3.6 3.4 4.3 3.2 3.2 3.3 3.5 3.5 3.6 19.7 26.0 10.6 4.5 3.3 3.4 3.5 3.6 3.6 227.5 5.5 4.6 2.5 3.1 3.4 3.4 3.2 3.1 3.0 13.0 13.1 5.9 3.0 3.0 3.0 3.0 3.0 3.0 234.5 10.2 6.9 3.8 3.5 3.5 3.7 4.0 3.9 4.1 18.8 17.0 3.9 3.7 3.8 3.8 3.9 3.9 4.0 241.5 7.3 7.2 3.8 3.7 3.9 4.0 4.1 4.2 4.3 16.6 13.5 13.5 4.7 4.2 4.2 4.3 4.3 4.3 248.5 7.5 7.6 5.9 4.5 4.1 3.9 4.1 4.4 4.6 17.1 17.6 14.7 8.8 4.4 4.5 4.6 4.6 4.6 255.5 - - - - - - - - - 19.1 19.1 14.4 5.4 4.5 4.5 4.6 4.6 4.6 262.5 12.4 9.5 4.2 2.5 4.1 4.1 4.5 4.5 4.5 22.3 21.8 11.0 5.0 4.4 4.5 4.6 4.6 4.6 276.5 17.3 15.5 0.1 4.5 0.1 0.1 3.8 3.6 3.8 26.3 24.9 20.0 13.3 4.9 4.2 4.1 4.0 4.0 283.5 21.0 19.2 0.0 6.2 3.8 3.1 3.8 4.0 4.3 28.3 26.4 22.8 13.0 4.5 4.9 4.0 4.2 4.3 290.5 21.5 21.8 15.8 6.8 4.4 3.8 3.7 3.4 3.6 27.8 25.6 21.1 12.0 4.7 4.1 3.9 3.8 3.7 297.5 18.6 17.4 8.2 4.8 3.8 3.8 3.3 3.3 3.2 27.5 27.8 24.5 15.7 5.7 6.1 3.4 3.3 3.3 304.5 25.4 21.1 16.4 8.4 4.0 3.9 4.1 3.7 3.7 29.3 28.4 21.6 16.3 8.5 5.4 3.5 3.7 3.8 311.5 24.2 22.7 16.3 8.3 5.0 4.0 3.5 3.5 3.8 29.2 28.9 26.2 18.7 7.2 6.2 4.1 3.8 3.8 318.5 24.5 23.1 17.3 7.3 3.9 3.6 3.6 3.6 3.6 28.9 27.5 24.6 17.0 6.8 8.8 4.5 3.6 3.7 325.5 - - - - - - - - - 29.1 28.2 26.1 18.6 11.9 11.6 8.0 5.9 5.0 332.5 31.4 29.7 21.2 16.1 7.8 6.4 4.3 4.0 4.2 31.2 30.1 25.9 20.3 12.4 11.6 8.4 6.4 4.9 339.5 27.6 26.9 21.4 13.1 2.9 6.8 4.4 3.4 3.5 30.7 28.4 27.6 20.1 11.0 13.8 7.4 7.0 6.0 340.5 - - - - - - - - - 30.7 30.0 27.5 19.8 12.6 11.0 7.9 6.7 5.3 346.5 21.7 28.8 9.1 5.4 3.3 7.2 2.3 4.8 3.9 30.9 29.7 29.3 24.0 18.8 17.6 12.9 12.6 8.9 353.5 26.8 29.7 9.6 20.7 16.1 4.9 10.8 7.3 7.2 33.5 32.0 29.5 25.3 22.3 20.5 17.5 15.7 13.5 361.5 9.1 24.7 0.7 19.9 0.7 4.5 10.9 9.5 2.4 32.0 31.1 28.1 24.7 22.1 20.5 17.8 17.4 14.8

Table A2. Bed salinity, 1995, R2 = 0.88. 1995 FIELD DATA, BED MODEL DATA, BED SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 3.5 35.6 35.4 33.8 30.5 27.5 27.0 21.5 19.0 18.0 35.6 35.4 33.8 30.5 27.5 27.0 21.5 19.0 18.0 11.5 35.7 35.6 34.6 30.5 27.5 25.0 21.0 19.0 18.0 37.0 37.1 34.4 30.8 27.7 26.5 22.1 18.9 18.7 17.5 - - - - - - - - - 36.4 36.2 33.9 30.7 28.9 27.8 23.1 21.1 19.4 24.5 - - - - - - - - - 35.8 35.7 33.5 29.2 26.0 25.0 21.0 19.1 18.3 31.5 36.0 36.0 35.2 33.0 30.0 28.0 24.5 21.5 21.0 35.9 35.2 33.2 30.8 29.3 28.0 23.1 21.2 19.1 38.5 36.3 36.3 35.5 24.5 29.5 29.5 24.1 22.7 21.0 35.3 35.2 33.1 30.7 26.5 24.8 21.3 18.8 18.4 45.5 36.4 36.4 36.4 33.3 31.4 30.0 31.4 24.0 22.7 35.6 34.8 32.5 30.8 28.6 27.7 23.1 20.9 18.7 52.5 36.4 36.5 36.6 34.7 - - - - - 35.2 34.7 33.1 30.1 28.1 27.2 24.4 23.6 22.2 59.5 - 36.8 36.4 35.2 30.9 30.8 27.5 25.6 24.7 35.1 34.7 31.6 29.4 28.0 27.1 23.7 22.7 22.0 66.5 37.6 37.6 37.2 36.2 32.8 32.0 29.0 27.3 26.1 34.9 34.1 32.4 30.1 28.2 27.0 23.1 21.8 21.5 73.5 37.7 37.8 37.4 36.5 34.0 33.8 30.6 29.1 27.9 35.1 34.4 32.1 29.8 29.0 27.8 24.0 22.1 21.0 80.5 37.8 37.9 37.9 36.5 34.7 34.2 31.1 29.7 28.7 35.2 34.5 32.4 30.1 28.4 27.3 24.0 22.3 21.2 87.5 37.4 38.1 37.9 36.5 33.8 34.0 31.1 30.4 29.7 35.1 34.8 31.9 29.3 28.6 27.8 24.7 24.1 23.8 94.5 37.6 37.9 37.9 35.5 33.5 33.0 30.6 29.5 28.7 34.9 34.2 31.9 29.5 27.7 27.2 24.2 23.0 23.2 101.5 37.8 37.9 37.9 37.1 35.8 35.1 32.8 31.8 31.2 34.9 34.6 33.2 30.3 30.2 29.2 26.1 24.4 23.3 108.5 - - - 35.5 33.2 33.4 31.4 30.2 29.2 34.9 34.6 32.1 30.6 28.6 28.0 25.1 24.3 23.9 116.5 37.3 37.4 37.3 36.1 33.5 33.0 31.4 30.4 29.2 34.9 34.4 31.7 29.9 29.4 28.8 26.0 23.6 24.3 122.5 37.6 37.9 37.8 35.4 34.2 33.9 31.5 30.4 29.4 34.9 34.4 31.8 29.1 27.4 26.8 24.6 23.8 23.7 129.5 40.1 40.3 40.1 38.9 37.7 37.5 35.2 29.9 33.1 34.9 34.6 32.0 30.1 29.7 29.6 27.0 26.0 25.5 136.5 35.3 35.0 35.3 33.2 31.5 30.3 31.5 29.9 29.9 35.0 34.2 31.2 28.2 27.5 27.2 25.4 25.0 24.2 144.5 - - 33.6 29.9 30.3 10.1 9.7 25.8 28.0 34.4 34.1 28.5 26.5 26.6 23.1 21.8 21.0 15.2 150.5 33.9 34.4 33.9 29.6 29.4 28.0 26.0 25.0 23.3 35.0 34.3 29.9 27.2 24.7 24.1 19.2 18.3 16.4 164.5 33.6 33.2 31.4 15.1 5.1 5.2 5.4 5.5 5.4 34.5 33.6 23.9 20.1 9.1 6.6 6.8 5.6 5.5 178.5 32.8 32.8 27.9 26.8 25.1 20.1 19.9 4.9 4.8 34.1 33.9 24.9 18.0 16.3 6.3 5.6 4.8 4.8 192.5 - - - - - - - - - 34.8 33.6 14.3 3.9 3.2 3.0 3.0 3.0 2.9 199.5 - 32.1 24.3 2.8 2.8 2.8 2.9 3.0 3.0 33.7 32.9 13.1 2.1 2.1 2.0 2.1 2.0 2.0 206.5 32.2 31.9 2.3 2.1 2.1 2.1 2.0 1.9 2.0 31.4 30.8 2.1 2.0 2.0 2.0 2.0 2.0 2.0 208.5 - - - - - - - - - 31.2 28.6 2.0 2.0 2.0 2.0 2.0 2.0 2.0 213.5 32.1 - 2.0 2.1 2.2 2.3 2.4 2.4 2.4 31.4 29.8 2.3 2.4 2.4 2.4 2.4 2.4 2.4 220.5 32.6 - 27.2 3.2 3.3 - 3.5 3.5 - 33.7 32.9 10.6 4.5 3.3 3.4 3.5 3.6 3.6 227.5 32.2 - 27.5 3.2 - - 3.3 3.1 - 33.8 33.1 15.7 3.1 3.0 3.0 3.0 3.0 3.0 234.5 32.5 32.0 27.6 - 3.6 - 4.0 4.0 - 34.0 33.5 14.5 3.7 3.8 3.8 3.9 3.9 4.0 241.5 32.1 - 27.1 3.7 3.9 - 4.1 4.3 - 34.0 32.9 16.8 9.3 4.2 4.2 4.3 4.3 4.3 248.5 - 32.0 29.4 7.7 4.2 - 4.1 4.4 - 34.2 33.7 21.6 12.8 4.4 4.5 4.6 4.6 4.6 255.5 - - - - - - - - - 33.5 33.0 14.7 7.4 4.5 4.5 4.6 4.6 4.6 262.5 32.4 - 29.2 3.8 4.1 - 4.6 - - 33.8 33.4 15.3 5.6 4.4 4.5 4.6 4.6 4.6 276.5 32.7 - 30.0 - - - 3.9 4.0 - 34.4 34.0 25.0 18.5 16.9 15.5 4.6 4.0 4.0 283.5 32.6 - 10.2 - - - 3.9 4.1 - 34.3 33.8 24.2 19.2 15.3 13.5 4.0 4.2 4.3 290.5 - 32.9 30.6 - 13.1 - - 4.0 - 34.3 34.0 26.6 19.3 20.4 18.2 8.4 6.0 3.7 297.5 32.9 - - - 4.1 - 3.4 3.4 3.4 34.0 33.4 24.5 15.8 17.6 15.9 4.4 3.3 3.3 304.5 33.2 32.8 30.7 12.3 8.3 - - 4.1 4.1 34.5 34.2 21.6 16.3 18.8 12.6 3.6 3.7 3.8 311.5 33.2 - 31.0 18.4 12.8 - - 3.6 - 34.3 34.0 26.2 22.2 18.6 15.6 8.3 3.9 3.8 318.5 32.9 - - - - - 3.8 3.7 - 34.4 34.0 25.8 18.0 18.8 16.4 9.6 3.7 3.7 325.5 - - - - - - - - - 34.1 33.8 29.3 23.2 15.8 14.3 9.5 6.6 5.2 332.5 36.7 36.4 - 20.7 - - - 4.0 4.4 34.9 34.5 25.9 22.4 19.6 18.0 10.9 8.3 6.0 339.5 33.2 - 29.5 - 12.6 - 6.7 3.8 - 34.2 34.1 28.2 23.3 18.4 17.4 10.6 8.0 6.3 340.5 - - - - - - - - - 34.5 34.3 28.4 23.6 20.0 18.6 13.8 9.7 6.3 346.5 33.7 - - - - - 9.1 6.1 - 34.3 34.5 29.4 24.4 22.3 20.6 13.6 14.0 11.2 353.5 33.5 - - - - - 16.7 12.3 11.1 34.6 34.1 29.8 25.9 23.8 20.5 17.5 16.0 15.5 361.5 33.2 - - - 20.1 - 14.4 - - 34.2 34.7 28.8 25.0 22.4 22.1 18.0 17.5 16.4

Table A3. Surface salinity, 1997, R2 = 0.85. 1997 FIELD DATA, SURFACE MODEL DATA, SURFACE SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 7.5 32.2 32.5 29.2 21.3 15.8 15.2 11.0 9.4 7.0 32.2 32.5 29.2 21.3 15.8 15.2 11.0 9.4 7.0 14.5 33.6 32.8 30.7 23.0 17.1 15.2 12.5 10.3 8.3 34.4 32.8 33.7 30.2 23.4 19.3 17.8 16.2 13.4 21.5 35.5 30.0 31.5 24.4 6.2 6.7 14.1 11.9 3.2 33.5 32.8 32.9 30.0 26.2 21.9 17.9 16.6 13.5 28.5 35.5 35.4 32.9 26.9 21.4 20.4 17.1 15.5 13.5 33.5 33.0 32.5 30.1 26.7 23.5 21.3 16.7 15.2 34.5 35.6 35.1 30.8 26.6 22.4 21.9 14.6 16.3 14.7 34.7 31.9 32.3 30.4 26.6 26.1 24.4 22.8 19.9 42.5 36.0 35.9 33.9 27.7 23.0 22.1 18.4 17.0 15.8 33.2 32.6 32.4 28.6 24.1 23.6 21.7 17.3 15.7 49.5 36.6 36.2 35.2 29.2 25.0 23.5 19.1 17.6 15.6 35.7 32.6 34.0 30.6 27.1 25.4 22.0 18.8 16.5 55.5 36.7 36.6 34.3 30.6 26.1 25.7 21.5 19.7 18.6 35.3 34.4 33.9 31.2 27.2 25.1 22.6 19.5 17.7 64.5 36.7 37.0 35.4 30.6 26.9 25.6 23.0 21.7 19.7 36.1 33.3 32.8 32.4 28.7 25.7 23.0 22.4 20.2 69.5 37.4 37.1 35.3 31.7 27.8 26.7 23.7 22.2 20.5 34.3 33.8 34.5 30.6 25.6 25.5 22.8 19.2 16.4 76.5 36.8 36.3 35.2 31.4 27.8 26.1 22.6 22.2 20.6 36.7 34.9 35.2 33.2 29.3 28.0 24.2 23.4 21.2 83.5 35.9 35.8 34.4 30.9 26.9 26.5 22.6 21.4 19.8 35.1 34.5 34.8 31.5 27.8 26.6 24.7 21.9 20.5 92.5 36.0 35.9 33.3 27.5 21.0 17.1 12.4 7.0 6.8 36.5 35.3 35.5 32.7 28.3 23.4 22.3 21.4 18.7 97.5 34.5 33.1 24.9 19.4 13.3 13.1 10.4 8.0 7.4 35.3 35.0 34.6 26.9 22.4 12.6 13.8 12.2 10.1 104.5 32.2 32.0 26.1 20.3 15.6 14.1 11.1 8.8 7.6 35.2 32.9 33.6 32.2 24.0 20.8 19.8 19.2 18.5 111.5 33.0 31.8 26.9 23.1 17.6 15.8 13.2 12.5 12.2 33.1 32.5 28.7 24.6 20.1 18.3 14.5 8.9 8.0 118.5 34.5 32.9 30.7 25.2 22.4 21.5 17.6 15.7 15.8 33.7 33.1 33.1 29.9 25.7 22.6 20.7 18.7 15.3 125.5 33.2 32.4 28.6 24.0 20.8 19.3 16.1 14.7 13.9 33.4 33.0 32.1 27.2 21.8 18.8 16.1 13.4 10.0 132.5 33.4 33.2 31.7 27.0 22.3 21.7 18.2 15.8 16.5 33.4 32.9 32.8 30.3 20.9 20.9 18.4 15.3 11.6 139.5 33.7 32.2 28.7 25.7 22.3 21.8 18.0 15.3 14.0 32.4 31.9 32.7 26.6 21.7 19.3 16.2 13.8 13.0 146.5 34.3 33.0 31.4 8.9 22.3 21.2 17.7 15.4 7.8 31.2 31.9 31.7 29.1 23.2 22.5 22.5 20.8 18.8 154.5 34.3 32.9 30.6 20.9 14.8 13.5 9.2 5.1 7.5 32.9 31.6 29.8 28.3 17.0 12.7 11.6 8.8 9.3 160.5 32.7 20.3 18.3 10.4 8.3 7.6 6.5 5.9 2.4 33.1 31.7 30.8 27.0 19.5 14.5 11.5 10.1 8.2 167.5 24.4 24.1 17.7 10.2 8.6 7.7 6.9 2.6 6.8 31.7 30.1 29.1 19.1 12.5 6.5 4.7 4.7 4.0 174.5 27.7 26.6 21.7 19.9 12.9 11.2 8.4 6.8 5.6 26.1 26.4 25.1 21.9 12.7 11.4 9.1 7.2 5.9 181.5 27.0 25.9 19.8 15.1 11.3 10.8 8.3 7.8 2.3 25.6 26.1 23.9 19.9 11.7 8.4 5.8 5.7 5.1 190.5 30.1 28.1 16.0 10.0 - 6.9 5.6 5.4 5.2 25.6 24.3 23.3 21.8 14.2 9.9 9.9 6.2 5.0 195.5 27.8 26.8 14.3 9.0 6.3 6.2 4.5 3.4 6.2 26.7 25.3 23.4 15.7 7.6 5.2 4.6 4.3 4.3 202.5 25.3 23.4 21.3 15.8 9.5 8.1 6.6 5.7 4.9 23.7 23.8 24.5 12.5 9.2 3.6 4.0 3.9 4.0 209.5 - - - - - - - - - 26.4 22.5 20.0 16.7 9.6 5.9 3.7 3.4 3.5 216.5 25.5 24.2 21.0 15.8 8.4 8.9 6.4 5.7 5.9 25.9 24.1 23.0 14.6 6.3 3.9 3.5 3.4 3.7 223.5 - - - - - - - - - 24.1 22.7 23.2 18.1 10.6 8.0 4.4 3.7 3.6 226.5 11.1 11.0 4.0 4.0 3.9 4.2 4.6 4.7 4.7 18.5 17.6 10.2 4.4 2.3 2.3 2.3 2.3 2.3 230.5 12.9 9.7 4.5 2.8 2.8 3.2 3.5 3.6 3.8 15.9 14.6 14.7 8.3 2.4 2.4 2.4 2.5 2.5 237.5 11.4 10.5 7.0 4.0 3.4 3.4 3.4 3.4 3.5 14.8 12.9 10.4 3.5 2.3 2.3 2.3 2.4 2.5 244.5 18.1 16.7 10.5 7.2 3.3 3.1 3.3 3.4 3.5 13.2 9.7 11.9 7.8 2.5 2.6 2.4 2.5 2.6 251.5 16.3 11.7 3.9 2.3 2.5 2.7 2.8 3.0 3.3 12.2 10.4 10.3 7.5 2.7 2.5 2.4 2.4 2.4 258.5 9.1 7.2 4.9 3.0 3.6 3.8 3.9 3.8 3.8 12.6 10.2 8.0 3.3 2.4 2.4 2.4 2.3 2.4 265.5 12.9 10.1 4.5 4.0 3.6 3.7 3.6 3.5 3.8 13.1 5.3 5.3 3.8 2.6 2.6 2.7 2.7 2.9 274.5 25.3 15.2 8.5 5.2 3.5 3.5 3.5 3.8 3.4 12.9 10.1 9.3 6.4 2.5 2.2 2.5 2.5 2.6 279.5 21.7 20.8 15.6 6.1 3.8 3.8 3.9 4.0 4.2 13.8 10.0 9.5 6.8 2.8 2.2 2.3 2.4 2.5 286.5 23.8 22.9 6.6 1.8 1.1 1.1 1.6 0.9 2.3 19.0 9.9 9.9 9.1 5.9 3.9 2.8 2.9 2.9 293.5 24.0 24.6 20.6 6.3 3.2 3.3 4.0 4.2 4.5 17.9 13.8 13.9 8.9 2.8 2.6 2.7 2.8 2.8 302.5 - 27.7 20.9 11.9 7.4 6.3 4.7 4.4 4.4 23.4 13.8 16.0 14.3 8.3 6.3 4.3 3.0 2.5 307.5 29.1 27.6 22.7 12.5 7.1 5.7 4.6 4.4 4.4 21.7 19.1 19.4 15.0 8.2 6.3 3.8 2.1 1.8 314.5 30.2 29.1 25.0 18.6 13.6 8.8 5.7 4.5 4.3 27.7 19.7 20.7 15.3 12.6 8.0 6.2 4.5 2.7 321.5 33.1 31.9 26.4 16.9 10.8 9.6 6.0 5.0 4.9 27.7 23.1 22.7 20.1 14.7 11.9 9.8 7.1 4.7 328.5 31.8 31.2 24.3 20.1 13.1 15.2 8.6 6.4 6.8 32.0 24.7 26.7 23.1 19.2 13.0 12.2 10.4 7.5 335.5 32.4 31.4 26.9 20.3 15.1 13.6 9.7 8.2 6.2 29.9 27.7 26.8 23.4 18.5 15.8 12.7 8.7 7.4 342.5 32.1 32.1 28.0 15.9 14.9 12.6 9.2 7.0 5.8 32.0 29.5 29.4 25.8 21.5 19.7 15.3 14.0 10.5 349.5 33.9 33.4 29.9 24.4 18.6 17.2 13.5 11.4 9.4 31.7 28.1 29.1 26.0 17.8 15.1 13.9 12.3 7.2 356.5 33.8 33.7 30.8 23.9 18.5 16.5 12.9 10.3 8.5 33.0 29.2 30.7 28.1 24.2 20.9 20.3 17.9 14.0 363.5 34.7 33.9 30.9 25.9 20.4 19.7 15.5 13.1 11.1 33.1 30.8 31.7 27.1 23.3 18.5 15.5 14.0 11.0

Table A4. Near-bed salinity, 1997, R2 = 0.77. 1997 FIELD DATA, BED MODEL DATA, BED SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 7.5 35.8 35.4 33.8 28.6 21.9 20.2 15.1 11.9 9.7 36.5 36.2 32.8 25.5 24.3 18.8 17.9 15.4 12.6 14.5 35.3 35.5 31.3 27.2 24.6 22.3 17.4 15.0 13.6 34.4 34.4 33.9 30.2 25.3 24.2 19.8 16.2 14.0 21.5 36.5 36.2 32.8 25.5 24.3 18.8 17.9 15.4 12.6 33.5 33.9 33.2 30.6 27.7 24.8 22.6 16.6 15.0 28.5 36.8 36.6 33.6 29.7 25.6 24.4 20.7 18.5 16.6 33.6 33.5 32.6 30.1 26.9 25.9 22.5 16.8 15.4 34.5 36.5 36.2 35.0 32.1 27.9 27.3 22.8 20.7 19.0 35.2 34.6 33.6 30.8 29.1 27.1 25.1 22.8 20.3 42.5 36.8 36.9 34.5 31.0 27.2 25.9 21.1 19.6 18.5 35.6 35.1 33.6 29.4 27.2 26.4 24.4 19.1 20.7 49.5 37.4 37.3 35.3 31.5 28.2 27.1 23.0 20.3 18.4 35.7 35.4 34.7 30.6 28.3 26.2 23.9 18.8 17.8 55.5 36.7 36.6 35.9 34.1 28.0 28.1 24.7 22.5 21.8 35.8 35.6 34.0 31.2 27.9 26.7 24.7 19.8 19.4 64.5 36.6 36.4 36.7 34.2 30.0 28.8 24.9 22.7 21.8 36.4 35.8 35.4 32.4 28.9 26.8 24.3 22.5 20.6 69.5 37.0 36.7 36.5 33.4 29.5 28.8 25.0 22.7 21.8 36.8 36.5 35.8 30.6 28.2 28.9 26.6 19.3 20.2 76.5 36.3 36.0 35.4 33.3 32.2 31.0 26.2 23.1 22.4 37.1 36.6 35.6 33.2 30.4 28.2 25.5 23.4 21.3 83.5 35.1 35.6 35.0 32.7 28.9 28.7 24.8 23.5 22.3 36.7 36.9 36.2 31.6 29.0 28.6 27.3 22.4 23.1 92.5 35.9 35.9 34.7 31.2 30.0 27.9 25.1 23.6 22.1 36.5 36.4 35.7 32.8 30.6 26.4 23.9 21.4 18.8 97.5 35.9 35.7 35.2 32.2 28.8 27.9 22.9 21.7 18.5 36.1 35.9 35.6 27.8 24.7 23.4 24.4 12.2 15.7 104.5 35.1 35.4 34.8 30.8 29.0 27.0 25.6 24.1 23.0 35.9 35.2 35.2 32.3 30.3 26.2 23.9 21.9 18.5 111.5 35.1 35.1 33.1 28.2 27.4 26.2 24.1 23.6 22.6 34.6 34.5 34.4 31.6 23.7 24.2 22.4 13.8 16.8 118.5 34.5 34.4 31.3 26.8 25.4 23.6 20.1 18.0 17.1 34.1 34.3 33.8 31.1 27.5 25.6 23.2 18.9 16.1 125.5 34.5 34.2 32.2 28.3 25.9 24.7 21.4 20.2 18.5 33.6 33.7 33.2 29.8 28.0 26.1 24.7 14.9 18.3 132.5 34.5 34.5 32.7 30.0 25.1 24.3 20.3 19.1 18.0 33.4 33.4 32.9 30.7 26.4 25.0 24.5 19.5 16.5 139.5 34.7 34.3 33.5 30.1 30.6 30.1 28.0 27.3 26.6 33.3 33.2 32.9 29.9 25.2 25.1 23.6 16.3 18.9 146.5 34.4 34.0 32.9 28.0 26.7 24.9 23.5 22.6 22.0 33.4 33.3 32.5 30.9 27.7 26.0 25.3 21.0 20.6 154.5 34.6 34.2 31.0 25.1 27.7 26.6 23.2 21.1 19.5 34.1 33.4 32.9 29.7 24.5 22.8 21.3 14.6 13.4 160.5 34.0 33.4 29.9 24.2 23.0 20.4 20.8 8.9 8.8 34.1 33.7 32.9 29.5 23.9 22.5 20.7 11.9 12.7 167.5 35.1 34.2 32.5 26.0 22.1 21.4 21.1 9.5 7.4 33.5 33.4 32.6 21.5 18.0 13.9 16.8 5.0 5.1 174.5 34.1 33.8 32.6 24.0 22.5 21.1 19.0 19.0 14.7 30.9 32.6 31.0 27.8 17.8 17.9 17.4 16.2 8.8 181.5 35.0 34.3 32.9 26.3 23.7 23.4 21.4 20.3 19.0 27.9 29.4 28.4 24.2 18.4 17.5 16.0 5.7 7.1 190.5 33.3 33.5 31.2 25.9 26.8 24.9 21.7 19.9 6.8 26.6 28.0 26.7 24.3 19.7 18.8 18.1 16.4 10.4 195.5 33.5 33.3 29.0 26.2 26.1 22.9 20.7 16.4 6.4 27.2 26.6 25.9 21.2 13.8 11.3 9.0 4.3 4.3 202.5 33.6 33.6 32.1 23.5 23.5 20.0 19.0 11.1 7.1 27.4 26.9 25.7 15.9 9.5 7.8 11.4 3.9 4.0 209.5 - - - - - - - - - 27.2 26.9 26.4 21.6 13.4 13.1 11.3 3.4 3.5 216.5 25.5 24.2 21.0 15.8 8.5 19.9 6.4 5.4 5.9 26.5 26.2 25.3 22.0 10.6 7.7 8.2 3.4 3.8 223.5 - - - - - - - - - 24.4 25.6 24.2 18.1 10.6 9.2 8.5 3.7 3.6 226.5 34.7 34.4 33.3 25.3 22.5 4.2 18.3 14.6 7.7 20.9 23.3 22.1 14.0 2.5 2.3 2.3 2.3 2.3 230.5 34.0 33.3 29.3 10.6 4.1 3.2 4.6 4.7 4.7 18.9 23.2 21.1 8.3 2.4 2.4 2.4 2.5 2.5 237.5 32.8 32.8 27.6 2.8 3.0 3.4 3.6 3.8 3.8 16.5 20.8 16.0 8.5 2.3 2.3 2.4 2.4 2.5 244.5 33.4 33.1 29.6 6.6 3.5 8.5 3.5 3.6 3.6 13.8 17.0 14.6 8.0 2.5 2.7 2.4 2.5 2.6 251.5 33.8 33.3 30.8 12.7 9.9 2.7 3.4 3.6 3.8 13.0 14.3 12.2 9.6 3.8 2.9 2.4 2.4 2.4 258.5 33.1 32.8 28.8 3.7 2.7 3.8 2.9 3.2 3.4 14.8 12.6 11.4 5.9 2.4 2.4 2.4 2.3 2.4 265.5 34.3 34.0 29.8 3.0 3.6 3.7 3.9 4.0 4.0 15.2 14.1 13.2 6.2 2.6 2.6 2.7 2.7 2.9 274.5 33.7 33.0 29.8 4.2 3.8 3.6 3.8 3.9 3.9 14.5 14.1 13.6 6.6 2.5 2.3 2.5 2.5 2.6 279.5 33.9 33.6 30.8 5.7 - 3.8 3.7 3.8 4.0 16.9 15.2 13.5 6.9 2.9 2.2 2.4 2.4 2.5 286.5 36.7 34.9 32.9 21.8 3.8 1.6 4.0 4.0 4.3 20.1 18.4 16.2 10.3 6.0 4.8 2.8 2.9 2.9 293.5 34.4 34.0 30.4 14.5 10.3 3.3 2.7 2.8 2.9 22.2 20.6 19.2 9.4 3.7 2.6 2.7 2.8 2.8 302.5 35.5 34.4 31.3 21.1 8.2 9.5 3.9 4.3 4.7 23.6 22.9 21.0 15.8 12.7 10.9 4.7 3.0 2.5 307.5 35.7 34.5 23.8 20.3 11.3 8.7 5.7 4.5 4.6 26.2 24.3 22.9 15.3 11.1 7.7 5.3 2.1 1.8 314.5 34.3 35.1 32.9 19.8 11.3 14.5 5.2 4.5 4.5 28.1 27.3 25.4 19.0 15.7 12.6 6.2 4.5 2.7 321.5 36.9 34.2 32.1 23.9 16.3 11.1 9.3 5.8 4.4 30.2 29.2 28.0 21.1 16.3 14.0 12.2 7.6 5.9 328.5 35.3 36.2 28.4 24.0 17.0 21.7 10.1 7.5 4.9 32.0 31.6 29.7 24.6 21.7 18.8 13.2 10.4 7.6 335.5 35.4 35.1 31.5 26.0 22.2 15.3 14.3 10.0 8.6 32.8 31.9 31.2 24.7 20.7 19.6 17.4 9.5 8.5 342.5 35.1 35.2 28.5 20.6 20.5 17.5 13.3 9.5 7.6 32.5 32.1 31.0 25.8 22.9 22.0 15.9 14.0 10.5 349.5 35.7 34.9 31.4 25.9 19.7 19.3 12.3 9.8 9.3 32.4 32.2 31.6 26.0 21.2 18.5 17.1 12.3 11.3 356.5 35.6 35.5 31.2 26.7 21.8 21.6 16.5 12.6 11.0 33.4 33.1 32.0 28.2 24.4 24.2 20.7 18.0 16.5 363.5 35.8 35.4 31.7 26.3 23.3 20.6 14.6 13.2 12.2 34.0 33.4 31.7 27.6 24.9 21.0 19.9 14.0 11.1

Table A5. Surface temperature, 1997, R2 = 0.85. 1997 FIELD DATA, SURFACE MODEL DATA, SURFACE SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 7.5 23.9 23.9 24.1 26.1 26.5 27.0 28.9 29.0 28.3 23.9 23.9 24.1 26.1 26.5 27.0 28.9 29.0 28.3 14.5 24.3 24.8 24.5 26.7 27.3 28.2 28.8 29.4 29.4 23.4 23.5 23.5 25.0 25.9 25.8 26.1 26.7 26.8 21.5 23.5 23.3 21.9 23.8 24.6 25.0 26.7 26.8 26.8 23.4 23.9 25.0 27.0 27.4 27.9 27.4 28.1 28.1 28.5 23.1 22.9 22.9 24.7 24.8 25.3 26.1 27.1 27.0 23.2 23.6 25.2 24.4 23.6 23.7 24.8 24.5 24.7 34.5 25.4 26.2 27.3 29.1 29.0 29.2 29.4 29.5 29.0 22.6 23.0 23.2 23.4 22.4 22.4 22.9 23.2 22.9 42.5 24.0 24.0 23.5 25.8 25.8 26.2 27.5 28.0 27.2 22.9 23.2 23.4 22.5 22.8 23.0 23.5 23.7 23.2 49.5 23.8 23.8 23.5 24.9 25.7 25.9 27.9 26.9 27.0 22.9 23.3 23.6 23.4 23.0 23.3 23.7 23.6 23.6 55.5 23.9 23.7 23.3 24.5 25.0 25.2 26.1 26.6 26.2 23.4 23.6 24.7 25.0 24.8 24.8 25.2 25.1 25.0 64.5 23.9 23.6 22.6 24.0 24.3 25.2 25.4 26.7 25.8 22.7 23.4 23.9 24.6 24.1 24.4 24.8 24.9 25.0 69.5 23.4 23.2 23.0 24.2 24.8 25.1 25.1 26.1 26.5 21.9 22.5 22.5 21.4 20.9 21.2 21.9 22.1 21.8 76.5 22.2 22.1 21.5 21.8 22.7 22.8 23.4 24.3 23.6 21.6 21.5 21.8 20.4 19.4 19.7 19.8 20.6 20.4 83.5 22.3 22.3 22.4 22.9 23.3 23.7 25.8 26.3 25.3 21.8 22.0 22.6 23.0 22.5 22.3 22.4 23.0 23.0 92.5 20.5 20.1 19.2 19.7 20.6 20.8 21.4 21.2 20.9 21.5 21.6 21.7 19.8 19.9 19.8 20.3 20.5 20.2 97.5 20.5 20.6 20.7 21.2 21.4 21.5 22.3 22.6 21.9 20.9 20.7 20.8 20.8 20.2 20.1 20.0 20.0 20.2 104.5 21.0 21.2 21.1 22.2 22.1 22.5 21.9 22.8 23.7 21.1 21.2 21.3 21.3 21.6 21.6 21.8 21.5 21.8 111.5 21.5 21.8 20.9 22.8 22.5 22.2 23.0 22.6 22.3 21.1 21.3 21.7 22.9 23.1 23.2 23.4 23.5 23.1 118.5 20.6 20.5 20.4 20.4 21.1 20.7 21.1 20.9 21.0 21.3 21.7 22.5 22.0 22.3 22.4 22.6 22.5 22.2 125.5 19.0 18.6 17.2 18.8 19.5 19.5 19.3 19.2 19.8 20.8 21.2 21.8 20.8 19.6 19.7 20.1 19.9 19.2 132.5 16.9 16.5 15.2 14.7 15.0 15.0 15.4 15.8 16.5 18.8 18.6 19.1 15.8 15.6 15.7 15.6 15.6 15.7 139.5 18.3 17.4 16.3 18.0 18.7 18.1 18.7 18.2 17.8 18.6 18.8 18.7 18.2 17.5 17.4 17.8 17.8 17.6 146.5 17.7 17.0 16.5 16.8 17.0 17.0 17.1 17.0 19.2 18.8 18.6 18.3 17.4 17.2 17.3 17.4 17.3 17.2 154.5 17.0 16.5 16.5 15.4 15.5 15.6 15.5 14.7 15.5 18.2 18.0 17.8 17.1 16.5 16.5 16.4 16.3 15.9 160.5 16.2 15.4 14.7 15.0 14.9 15.4 15.6 15.8 15.6 17.8 17.4 17.1 16.3 16.0 15.7 15.5 15.5 15.4 167.5 14.6 14.5 14.4 14.5 14.7 14.6 14.6 15.0 14.6 16.6 16.9 16.8 16.7 15.8 15.6 15.2 15.0 14.7 174.5 15.7 15.8 15.3 16.0 16.0 15.6 15.8 16.2 15.7 17.3 17.3 16.6 16.2 16.0 15.9 15.5 15.3 15.0 181.5 15.3 15.3 14.7 14.6 14.4 14.0 14.4 15.0 14.5 17.0 17.4 17.2 17.4 16.8 16.5 16.9 16.6 16.1 190.5 14.0 13.1 11.7 11.9 12.4 12.4 12.2 12.7 12.6 16.4 16.4 15.7 14.7 14.4 13.7 13.3 13.1 13.0 195.5 12.8 12.7 10.9 11.7 11.2 11.2 11.6 11.7 10.9 15.6 15.5 15.8 13.1 13.6 13.0 12.5 12.1 11.9 202.5 13.1 12.7 13.0 13.3 11.9 11.5 11.0 11.8 10.4 16.0 15.0 14.4 14.0 12.7 11.8 11.5 11.2 11.1 216.5 15.7 15.6 15.7 16.1 15.1 15.1 15.1 14.4 14.1 16.2 16.1 16.4 16.5 16.0 15.8 15.3 15.3 15.3 223.5 - - - - - - - - - 15.2 15.0 14.1 13.6 13.9 13.9 13.8 13.8 13.7 226.5 14.5 14.6 13.8 13.1 13.3 12.5 12.7 12.9 12.9 15.7 15.6 16.0 15.1 12.7 12.8 13.0 12.9 12.7 230.5 14.3 13.9 12.8 13.8 13.4 13.3 13.6 13.4 13.0 15.6 15.6 15.3 13.6 14.1 14.2 14.3 14.2 14.2 237.5 14.4 14.5 13.9 14.1 13.4 13.5 13.3 14.2 13.7 15.7 16.1 16.1 15.8 14.0 14.2 13.9 13.5 13.6 244.5 14.8 15.1 14.5 15.3 14.3 14.0 14.0 13.6 13.5 15.6 16.0 16.1 16.1 14.8 14.7 15.4 15.3 15.4 251.5 15.6 15.7 14.7 15.3 15.8 15.6 15.1 15.5 15.2 15.5 16.0 15.8 14.4 14.2 14.3 14.5 14.4 14.5 258.5 16.5 16.1 16.0 16.0 15.6 15.9 15.8 16.4 16.1 15.6 15.8 16.0 16.2 15.5 15.5 15.6 15.3 15.4 265.5 18.7 19.0 19.0 19.5 20.9 19.7 21.7 20.9 19.4 15.7 16.1 16.3 19.6 19.2 18.8 18.8 18.7 18.7 274.5 18.6 19.4 19.3 19.8 20.1 19.3 19.5 19.4 19.1 15.7 16.5 16.8 21.1 20.4 20.3 20.2 20.2 20.2 279.5 18.0 18.1 18.0 19.9 19.4 20.4 20.1 20.3 20.3 15.8 16.6 17.1 19.5 19.6 19.1 19.4 19.0 18.9 286.5 19.5 19.4 20.3 19.4 20.0 19.7 19.4 19.2 19.1 16.0 16.4 16.7 19.4 19.7 19.6 19.5 19.4 19.4 293.5 18.1 17.8 17.8 19.8 19.7 20.6 21.2 21.5 22.2 16.2 16.5 17.2 18.6 19.3 19.0 19.7 19.4 19.2 302.5 - 20.7 21.3 21.0 22.1 21.8 22.4 22.2 23.0 16.7 17.3 17.1 21.0 20.7 21.2 22.0 21.3 21.5 307.5 19.7 20.1 21.0 22.6 22.7 24.2 23.3 23.8 23.7 17.0 17.4 17.8 19.5 20.3 20.1 21.7 21.5 21.6 314.5 20.5 21.0 21.6 23.0 24.3 24.2 24.6 24.6 24.6 17.7 17.9 18.0 21.1 21.7 22.5 22.3 23.3 23.4 321.5 19.3 19.2 18.7 21.3 22.0 22.4 23.6 22.7 23.6 18.4 18.7 18.8 20.0 20.3 20.6 22.2 22.2 22.3 328.5 20.5 20.7 21.0 22.5 22.7 23.2 24.4 24.0 23.8 19.0 19.3 19.6 22.4 22.9 22.7 23.4 23.6 23.9 335.5 22.3 23.1 24.2 25.9 25.9 26.3 27.4 27.5 27.5 19.6 20.3 20.6 25.4 26.3 26.7 26.9 27.3 27.3 342.5 22.0 22.0 22.3 24.4 24.9 25.7 26.5 27.7 27.7 20.3 20.8 21.3 25.0 25.9 26.1 26.6 27.1 27.0 349.5 22.8 23.0 23.2 25.4 25.6 26.1 26.8 27.7 27.7 20.2 21.1 21.6 24.8 25.3 25.4 26.3 26.1 26.1 356.5 22.9 22.9 23.1 25.3 25.7 26.9 27.2 28.1 27.9 20.8 21.7 22.9 24.1 23.3 23.6 23.6 24.4 24.6 363.5 23.4 23.9 24.3 26.2 26.9 27.5 29.5 29.6 30.2 20.5 21.8 22.0 24.7 25.0 24.9 25.1 25.1 25.2

Table A6. Near-bed temperature, 1997, R2 = 0.80. 1997 FIELD DATA, BED MODEL DATA, BED SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 7.5 23.2 22.7 23.8 26.2 27.0 26.8 27.5 27.8 27.6 23.2 22.7 23.8 26.2 27.0 26.8 27.5 27.8 27.6 14.5 23.8 23.6 24.3 27.4 28.4 28.6 28.0 29.1 29.3 23.4 24.0 23.6 25.0 25.9 26.1 27.1 26.7 27.0 21.5 24.1 24.4 21.7 23.7 25.0 24.6 25.6 26.2 26.0 23.9 25.9 25.5 27.6 27.7 28.2 29.0 28.1 28.8 28.5 23.2 23.3 22.7 24.5 24.5 24.9 25.7 26.0 26.1 23.2 24.1 25.2 24.4 23.9 24.4 25.2 24.5 24.8 34.5 24.2 23.4 25.6 27.7 26.7 26.7 27.5 27.2 27.5 22.9 24.1 23.5 23.4 22.7 22.8 23.6 23.2 23.5 42.5 24.1 23.9 24.1 26.0 25.9 25.3 26.4 27.6 26.9 23.4 23.6 23.5 23.0 23.0 23.4 23.8 23.7 23.7 49.5 24.0 23.8 23.5 25.2 25.6 25.8 26.4 26.6 26.7 22.9 24.3 23.8 23.4 23.6 23.6 24.4 23.6 24.4 55.5 23.7 23.8 23.7 24.2 24.6 25.0 25.1 25.8 26.0 24.1 24.6 24.8 25.0 25.1 25.4 25.5 25.3 25.7 64.5 23.9 23.6 23.6 23.9 24.3 24.1 24.7 25.0 25.1 23.1 24.6 24.6 24.6 24.1 24.6 25.2 25.0 25.1 69.5 23.2 22.6 23.6 24.2 24.2 24.2 24.9 25.1 25.1 23.0 23.2 23.1 21.4 21.9 22.1 21.9 22.1 22.6 76.5 22.3 22.2 21.5 22.0 22.8 22.7 23.3 23.2 23.1 22.3 22.9 22.9 22.7 22.3 22.3 22.6 22.4 22.7 83.5 22.4 22.2 22.6 23.2 23.0 23.4 24.0 24.2 24.4 21.8 21.9 22.1 20.6 20.3 20.2 20.2 20.8 21.1 92.5 20.4 20.0 20.1 20.6 20.2 20.4 20.8 21.3 21.6 22.1 23.0 22.7 23.2 22.5 22.5 23.2 23.0 23.1 97.5 20.6 20.4 20.4 20.9 20.7 20.7 20.9 21.3 21.2 21.7 22.2 22.0 20.1 20.4 20.6 20.8 20.6 21.2 104.5 21.2 20.7 21.2 22.1 21.6 22.1 21.7 21.7 21.8 21.1 21.1 21.2 20.8 20.8 20.5 20.5 20.4 20.2 111.5 21.4 21.0 22.2 22.8 22.5 22.7 22.3 22.2 22.3 21.8 21.8 21.9 22.2 21.9 22.0 22.2 22.1 22.5 118.5 20.7 21.3 20.0 20.2 22.2 21.8 22.2 21.2 21.4 21.8 22.4 22.9 23.4 23.4 23.8 24.2 23.6 23.6 125.5 19.6 20.4 19.3 18.6 19.9 19.8 20.3 20.6 20.7 21.9 22.7 23.0 22.7 23.0 23.4 23.6 23.0 23.1 132.5 17.6 18.0 16.1 15.5 15.3 15.4 15.6 15.8 16.4 21.0 21.8 21.9 21.3 20.4 20.4 21.7 21.5 21.3 139.5 19.2 17.9 17.6 18.2 17.7 17.7 17.2 17.2 17.0 19.5 19.4 19.5 17.7 15.9 16.0 16.1 15.9 16.2 146.5 17.7 17.8 17.3 16.5 17.8 17.6 18.1 17.9 17.9 19.2 19.2 19.1 18.9 18.5 18.4 18.3 18.0 18.1 154.5 17.1 17.3 16.5 16.2 17.4 17.3 17.3 17.4 17.3 18.6 18.5 18.7 17.8 16.9 17.0 17.4 17.1 17.2 160.5 17.0 16.5 16.2 16.2 16.7 16.3 17.2 15.4 15.1 18.6 18.5 18.6 17.8 17.0 17.0 17.2 17.0 17.1 167.5 16.8 16.8 16.6 16.6 16.7 16.5 17.3 14.8 14.0 18.3 18.2 18.1 16.8 16.6 16.3 16.6 15.7 15.6 174.5 17.5 17.0 16.8 16.3 16.5 16.4 16.4 16.5 15.8 18.0 18.1 17.9 17.8 16.7 16.8 17.0 17.3 15.9 181.5 17.5 17.6 17.2 17.7 17.0 16.9 16.7 16.7 16.6 17.6 17.5 17.2 16.8 16.3 16.3 16.2 15.3 15.4 190.5 16.2 16.7 15.8 15.9 16.0 15.9 16.2 16.1 11.8 17.8 17.8 17.7 17.9 17.8 17.7 17.8 17.9 17.5 195.5 16.4 16.6 15.2 15.1 16.2 15.7 16.0 14.0 10.8 16.6 16.6 16.6 15.8 15.2 15.1 15.4 13.1 13.0 202.5 16.2 16.0 16.2 14.6 15.5 14.7 14.9 11.5 9.9 16.4 16.4 16.3 14.0 13.7 14.2 14.9 12.1 11.9 216.5 15.7 15.6 16.1 16.1 15.6 15.7 14.8 14.6 13.8 16.0 16.0 15.8 15.7 14.3 13.8 13.9 13.0 12.9 223.5 - - - - - - - 16.4 16.6 16.6 16.5 16.0 16.1 16.3 15.3 15.3 226.5 17.0 16.5 16.1 14.4 12.7 12.5 12.6 12.9 13.0 16.2 16.4 16.4 15.5 13.9 13.9 13.8 13.8 13.7 230.5 16.3 16.6 16.1 13.6 13.0 13.2 12.8 12.8 13.0 15.9 16.3 16.2 15.1 12.7 12.8 13.0 12.9 12.7 237.5 16.1 16.6 16.3 15.3 13.3 13.3 13.0 12.9 13.6 16.2 16.2 16.2 15.5 14.1 14.2 14.4 14.2 14.2 244.5 16.1 16.6 16.5 16.4 15.3 15.2 13.8 13.4 13.4 15.9 16.6 16.5 15.9 14.0 14.3 13.9 13.5 13.6 251.5 16.0 16.2 16.3 15.1 14.8 14.9 15.1 15.1 15.2 16.2 16.5 16.7 16.9 15.4 15.3 15.4 15.3 15.4 258.5 15.9 16.2 16.3 15.9 15.5 15.8 15.3 15.0 15.7 15.9 16.4 16.4 15.5 14.2 14.3 14.5 14.4 14.5 265.5 16.1 16.2 16.4 19.2 18.5 18.9 18.7 18.2 19.2 16.3 17.0 17.1 17.1 15.5 15.6 15.6 15.3 15.4 274.5 18.0 16.3 17.2 20.0 19.6 19.1 19.0 18.9 18.5 17.9 18.7 19.0 19.7 19.3 18.9 18.8 18.7 18.7 279.5 18.2 17.2 17.5 19.2 19.0 19.0 18.9 18.9 18.9 17.8 19.8 20.3 21.1 20.4 20.3 20.3 20.2 20.2 286.5 18.4 17.3 18.0 19.0 19.2 19.6 19.0 19.3 18.8 16.2 19.6 19.6 19.8 19.7 19.7 19.4 19.0 18.9 293.5 18.3 18.1 18.5 20.0 20.3 20.3 20.0 20.6 20.6 17.4 19.0 18.7 19.6 20.0 19.6 19.6 19.4 19.4 302.5 18.1 18.4 21.2 22.1 22.7 22.3 22.3 22.1 22.1 16.3 19.2 18.7 18.8 19.5 19.6 20.0 19.4 19.2 307.5 19.1 18.4 19.5 22.5 22.3 21.9 22.5 22.7 22.1 18.4 19.7 19.7 21.1 21.4 22.2 23.1 21.3 21.5 314.5 19.5 18.7 20.5 22.2 23.0 22.9 23.0 - 23.3 17.2 19.9 19.4 20.2 20.4 20.6 21.9 21.5 21.6 321.5 19.4 19.3 18.7 21.6 21.4 21.0 22.7 22.4 22.5 18.9 20.5 20.5 21.3 22.3 22.6 23.0 23.3 24.6 328.5 19.9 19.5 20.9 22.4 22.5 22.6 23.4 23.6 23.7 18.5 20.2 19.3 20.3 20.4 21.0 22.5 22.2 22.5 335.5 20.4 19.8 23.7 25.2 25.2 25.1 25.6 25.8 25.7 20.8 21.1 21.6 22.5 23.3 23.6 24.0 23.7 24.4 342.5 21.9 20.1 22.7 24.9 25.1 24.7 25.4 25.9 26.3 20.6 22.8 22.1 25.4 26.7 26.9 27.8 27.3 27.4 349.5 22.8 21.1 22.8 25.3 25.1 25.0 26.5 27.0 26.8 21.0 24.5 23.9 25.0 25.9 26.4 27.5 27.1 28.0 356.5 22.7 22.2 22.8 25.3 25.2 25.4 25.7 26.6 26.6 20.5 24.6 22.9 24.9 25.4 25.8 26.6 26.1 26.8 363.5 23.4 22.6 24.0 26.2 26.4 26.1 26.7 27.5 27.1 21.9 23.8 22.9 24.6 24.5 24.7 27.1 24.4 24.8

Table A7. Surface dissolved oxygen, 1997, R2 = 0.05. 1997 FIELD DATA, SURFACE MODEL DATA, SURFACE SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 7.5 6.4 6.3 5.8 6.7 5.9 6.7 10.2 8.0 7.8 6.4 6.3 5.8 6.7 5.9 6.7 10.2 8.0 7.8 14.5 6.5 6.2 5.5 6.3 6.6 6.7 8.5 10.6 7.2 5.9 6.3 4.7 5.2 6.2 6.4 6.3 6.0 6.0 21.5 6.3 6.6 6.3 6.5 5.1 6.3 8.3 8.3 7.6 6.5 6.4 6.0 6.6 6.9 7.1 7.2 7.1 7.1 28.5 6.7 6.3 6.1 8.5 6.6 6.5 7.4 6.3 7.5 6.8 6.7 6.4 6.8 7.1 7.3 7.2 7.3 7.3 34.5 6.2 5.9 5.3 8.0 12.4 7.3 9.9 9.0 6.4 6.9 6.9 6.8 7.0 7.4 7.6 7.7 7.7 7.8 42.5 6.6 6.4 5.9 6.8 5.4 5.8 9.1 8.7 6.4 7.0 6.9 6.7 7.4 7.7 7.8 7.8 7.9 7.9 49.5 6.5 6.1 5.7 7.3 5.1 4.7 8.3 7.6 6.3 6.9 6.9 6.7 7.1 7.4 7.5 7.7 7.6 7.7 55.5 5.7 5.7 5.6 5.3 4.4 4.2 5.0 4.5 3.4 6.7 6.8 6.6 6.8 7.2 7.4 7.5 7.5 7.6 64.5 6.3 5.9 5.4 7.3 7.0 5.5 8.5 8.8 7.3 6.9 6.8 6.7 6.7 7.0 7.2 7.3 7.3 7.4 69.5 6.2 6.2 5.6 5.6 5.2 4.6 7.2 6.7 7.0 6.9 6.9 6.7 7.3 7.7 7.8 7.8 7.9 8.0 76.5 6.5 6.2 5.8 6.4 6.3 6.3 9.6 7.7 7.5 7.2 7.1 6.9 7.4 7.9 8.0 8.2 8.4 8.5 83.5 5.8 6.3 5.9 7.0 7.0 6.0 7.5 7.8 8.2 6.9 6.9 6.8 7.0 7.5 7.7 8.0 8.0 8.1 92.5 6.5 7.0 6.6 8.1 7.2 6.9 7.2 4.7 5.2 6.9 6.7 6.6 7.7 8.0 8.3 8.4 8.5 8.5 97.5 7.3 7.4 7.9 8.1 5.3 4.2 6.1 4.2 4.3 7.2 7.4 7.2 7.2 7.9 7.9 8.1 8.0 8.0 104.5 7.9 8.4 10.2 15.0 11.2 10.4 6.2 5.7 4.5 7.0 7.1 7.2 7.4 7.5 7.5 7.8 8.2 8.1 111.5 7.0 7.7 8.9 9.8 12.5 8.8 13.8 6.7 5.2 7.1 6.9 6.7 7.2 7.1 7.1 7.1 7.1 7.3 118.5 5.8 5.6 5.3 7.3 6.4 7.5 8.0 16.0 7.2 6.7 6.6 6.6 7.1 7.3 7.3 7.3 7.4 7.7 125.5 7.4 8.6 8.7 9.5 16.0 16.0 11.5 11.4 11.5 6.6 6.5 6.3 7.2 7.7 7.8 7.8 8.0 8.2 132.5 7.0 6.9 6.5 7.6 7.3 10.0 6.4 6.8 4.6 7.4 7.4 7.1 8.2 8.5 8.6 8.9 9.0 9.0 139.5 7.2 7.6 6.8 6.8 12.6 9.5 15.3 12.7 9.7 7.6 7.6 7.4 7.8 8.0 8.1 8.1 8.2 8.3 146.5 6.4 6.9 6.5 6.1 5.2 6.0 9.0 9.1 9.7 7.3 7.4 7.5 8.0 8.3 8.5 8.6 8.6 8.7 154.5 7.1 7.3 6.9 6.9 7.2 7.0 6.4 6.1 6.5 7.4 7.4 7.5 8.0 8.4 8.6 8.8 8.9 9.1 160.5 7.1 8.6 8.2 8.8 7.7 7.2 7.8 7.9 7.4 7.8 7.8 7.9 8.6 8.9 9.2 9.3 9.3 9.4 167.5 9.6 9.4 9.8 8.8 9.8 7.4 7.8 7.7 7.9 8.3 8.2 8.2 8.3 8.8 8.9 9.3 9.4 9.5 174.5 8.3 9.1 9.5 11.7 8.1 8.9 7.1 7.6 7.5 7.8 7.9 8.4 8.5 8.9 9.1 9.3 9.3 9.4 181.5 8.9 9.7 10.3 11.3 11.6 8.5 7.5 7.6 8.4 7.9 8.4 8.4 8.4 8.6 8.7 8.6 8.7 8.9 190.5 8.8 8.8 8.9 9.0 - 8.3 8.8 9.1 9.3 7.8 8.3 8.7 9.0 9.3 9.5 9.7 9.7 9.7 195.5 8.7 9.0 8.2 7.8 8.5 8.9 9.3 9.3 9.6 8.6 8.5 8.1 9.6 9.4 9.6 9.8 10.0 10.1 202.5 10.1 10.8 8.8 7.6 7.9 8.5 9.8 9.8 10.0 8.1 9.3 9.4 9.5 9.8 10.0 10.1 10.1 10.3 209.5 - - - - - - - - - 8.3 9.1 9.1 9.5 9.8 10.1 10.1 10.1 10.1 216.5 8.5 8.5 6.1 7.9 7.9 7.5 8.2 8.4 8.4 8.3 9.2 8.7 9.1 9.2 9.3 9.5 9.5 9.5 223.5 - - - - - - - - - 8.8 9.4 9.5 9.3 9.5 9.6 9.8 9.8 10.0 226.5 9.9 9.6 8.6 8.9 8.8 9.1 9.2 9.1 9.1 8.7 9.5 9.7 9.5 10.0 10.0 10.0 10.1 10.4 230.5 10.5 9.0 7.9 9.0 8.8 8.7 9.2 9.1 9.2 8.4 9.5 9.5 9.7 8.3 8.0 7.5 7.3 7.4 237.5 10.8 11.1 7.9 8.6 8.5 8.7 9.0 9.1 9.2 8.4 9.5 9.2 9.6 9.9 9.8 9.9 10.0 10.0 244.5 10.5 11.0 8.7 7.1 8.5 8.3 8.8 9.1 9.3 8.5 9.5 9.5 9.5 9.6 9.6 9.3 9.2 9.1 251.5 9.3 8.7 8.1 8.0 8.1 8.4 8.5 8.3 8.4 9.0 9.3 9.4 9.9 9.5 9.4 9.3 9.5 9.6 258.5 10.5 9.2 7.8 7.9 8.2 8.1 8.8 8.6 8.5 8.4 9.3 9.2 9.3 9.4 9.5 9.5 9.5 9.5 265.5 8.7 9.5 8.1 6.8 6.9 6.9 7.2 7.1 7.0 8.5 8.6 8.6 8.5 8.8 8.8 8.8 8.8 8.8 274.5 7.3 8.4 8.0 8.4 8.3 7.1 7.3 7.6 7.1 8.4 8.4 8.4 8.4 8.6 8.6 8.6 8.6 8.6 279.5 7.8 7.9 6.6 10.5 8.9 9.7 7.3 7.0 6.9 8.2 8.6 8.5 8.5 8.8 8.9 9.0 9.0 9.0 286.5 9.4 9.0 9.8 9.9 7.0 6.5 6.9 7.3 7.3 8.4 8.9 8.8 8.7 9.1 9.1 9.0 9.0 9.0 293.5 7.4 7.4 6.4 9.2 9.9 10.5 7.0 6.5 6.7 8.1 8.6 8.5 8.6 8.7 8.8 8.9 8.9 9.0 302.5 - 4.9 8.2 6.9 7.2 6.8 7.2 5.7 4.9 8.5 8.7 8.2 8.4 8.3 8.3 8.5 8.8 8.9 307.5 6.0 5.4 5.8 5.8 7.7 8.3 8.8 8.2 6.7 7.9 8.6 8.4 8.3 8.5 8.6 8.7 9.0 9.3 314.5 7.4 7.3 6.5 8.0 6.8 6.5 8.4 7.9 7.0 8.0 8.5 8.4 8.4 8.4 8.4 8.4 8.5 8.8 321.5 7.1 7.0 6.9 7.8 8.2 8.7 9.2 7.7 8.0 7.7 8.1 7.8 7.9 8.2 8.3 8.2 8.2 8.4 328.5 7.5 7.4 7.6 9.0 9.1 5.3 9.0 8.2 4.3 7.8 8.3 8.2 8.2 8.5 8.6 8.6 8.1 8.0 335.5 6.9 6.8 5.1 6.0 5.2 5.6 7.8 6.5 7.9 7.5 7.5 7.1 7.1 7.3 7.4 7.4 7.3 7.3 342.5 6.7 6.6 5.5 7.0 7.2 6.7 9.7 9.2 7.9 7.4 7.2 7.2 7.1 7.3 7.3 7.2 7.1 7.4 349.5 6.4 6.2 5.6 5.9 6.8 7.2 7.4 8.5 7.5 7.3 7.3 7.0 7.0 7.1 7.2 7.2 7.2 7.3 356.5 6.7 6.5 5.5 6.3 6.3 7.2 8.4 9.6 6.9 7.3 7.2 6.8 7.1 7.4 7.5 7.7 7.5 7.6 363.5 6.5 6.3 5.5 5.7 8.9 8.1 9.4 10.3 9.3 7.3 7.2 6.9 7.1 7.5 7.6 7.7 7.7 7.7

Table A8. Near-bed dissolved oxygen, 1997, R2 = 0.10. 1997 FIELD DATA, BED MODEL DATA, BED SITE SITE DAY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 7.5 5.5 3.9 3.1 4.0 2.8 3.2 3.2 2.9 2.8 5.5 3.9 3.1 4.0 2.8 3.2 3.2 2.9 2.8 14.5 3.9 3.4 5.5 5.4 0.2 1.8 5.3 1.1 1.0 5.9 5.3 3.6 5.2 6.0 6.0 5.3 6.0 5.8 21.5 5.9 4.3 5.8 6.3 4.1 5.1 3.6 3.0 3.0 6.2 3.2 4.7 6.4 6.7 6.8 5.9 7.1 7.0 28.5 6.1 4.1 6.0 6.0 5.1 4.0 4.1 4.4 3.0 6.6 6.5 6.2 6.8 7.1 7.0 7.1 7.3 7.3 34.5 5.7 3.4 5.2 4.6 0.8 1.1 1.1 0.6 0.9 6.6 6.7 6.2 7.0 7.4 7.5 7.4 7.7 7.8 42.5 6.2 4.8 5.7 3.5 2.3 2.6 4.0 - 2.5 6.5 6.4 5.8 7.1 7.5 7.4 7.1 7.9 7.7 49.5 6.1 4.6 5.5 5.6 4.0 2.7 3.9 3.5 2.9 6.9 6.5 5.9 7.1 7.2 7.4 7.5 7.6 7.7 55.5 5.6 4.4 5.4 4.6 4.0 3.3 3.4 1.7 0.6 6.7 6.0 6.5 6.8 7.1 7.2 7.3 7.5 7.5 64.5 5.9 5.1 5.6 4.8 4.0 4.1 4.3 4.3 3.6 6.5 6.6 5.5 6.7 7.0 7.0 7.1 7.3 7.3 69.5 6.2 4.4 5.8 4.8 4.8 3.9 4.1 4.8 3.4 6.4 6.6 6.1 7.3 7.5 7.3 7.6 7.9 7.8 76.5 5.8 6.0 5.4 5.3 4.5 4.2 3.2 4.5 3.8 6.4 6.5 6.1 6.9 7.3 7.6 7.7 8.1 8.1 83.5 5.7 4.9 5.5 5.6 5.1 4.0 3.8 3.4 1.5 6.7 6.6 6.0 7.4 7.7 7.8 7.6 8.4 8.2 92.5 6.3 6.3 6.2 4.2 2.3 2.5 0.9 0.3 0.1 6.6 5.8 6.1 6.9 7.2 7.5 7.7 8.0 8.1 97.5 6.7 5.6 5.0 3.4 2.0 1.5 0.5 0.1 0.2 6.1 6.0 5.4 7.5 7.7 7.8 7.2 8.5 8.2 104.5 5.6 3.4 1.7 0.2 0.1 0.1 0.2 0.1 0.1 5.6 6.6 5.4 7.1 7.3 7.5 7.5 7.8 8.0 111.5 4.9 2.4 2.3 2.4 0.1 0.1 0.1 0.1 0.0 6.0 5.8 5.3 6.3 7.2 6.8 6.6 7.8 7.5 118.5 5.7 1.0 5.6 4.5 0.1 0.8 0.2 4.2 3.1 5.6 4.1 5.1 6.8 6.7 6.5 5.8 7.1 7.1 125.5 6.2 3.7 5.6 5.0 0.2 0.9 0.3 0.4 0.2 6.0 3.9 5.9 6.8 6.7 6.7 5.8 7.2 6.9 132.5 6.5 5.5 6.0 5.0 6.1 5.8 6.1 5.9 4.2 6.1 5.4 5.5 7.0 7.3 7.3 6.1 7.2 7.4 139.5 6.1 5.4 4.9 4.0 1.7 2.0 1.4 1.9 1.2 6.7 7.1 6.7 7.6 8.2 8.1 8.0 8.8 8.5 146.5 6.2 5.8 5.9 4.8 0.5 2.5 0.5 0.1 0.1 7.0 6.7 6.4 7.5 7.6 7.8 6.9 8.1 8.1 154.5 6.6 5.0 6.3 5.2 1.3 1.3 0.5 0.1 0.1 7.1 6.5 6.5 7.3 8.0 8.0 7.1 8.3 8.3 160.5 6.7 6.2 4.4 3.9 0.9 1.5 0.1 7.3 7.0 6.9 6.9 6.4 7.7 7.9 7.9 7.2 8.4 8.1 167.5 7.3 4.8 3.2 3.5 0.1 0.2 0.2 4.3 7.9 6.8 6.8 6.4 8.3 8.4 8.8 7.9 9.1 9.3 174.5 7.0 5.0 2.5 5.7 0.3 0.6 0.3 0.1 1.6 5.9 4.8 6.2 7.5 8.0 8.1 7.7 7.2 9.2 181.5 7.6 5.4 0.7 4.8 0.3 0.2 0.1 0.1 0.1 7.2 4.3 7.1 8.2 8.3 8.3 8.2 9.3 9.1 190.5 6.9 5.6 4.5 0.6 0.1 0.1 0.1 0.2 9.1 7.0 4.3 6.4 7.9 8.1 8.1 7.3 7.9 8.4 195.5 7.0 4.8 2.8 2.4 0.1 0.2 0.1 0.1 9.5 7.3 6.1 6.7 8.7 8.8 9.0 8.2 9.7 9.7 202.5 7.6 5.3 1.7 5.6 0.1 0.9 0.1 3.9 9.9 7.1 7.2 6.6 9.3 9.3 8.9 8.0 10.0 10.1 209.5 - - - - - - - - - 7.1 7.4 7.3 9.2 9.5 9.3 9.2 10.1 10.3 216.5 8.6 8.5 4.0 4.0 0.1 0.4 0.2 1.1 6.0 6.8 6.4 6.9 9.4 9.4 9.7 8.8 10.1 10.1 223.5 - - - - - - - - - 7.9 5.4 7.2 9.1 9.2 9.1 8.8 9.5 9.5 226.5 7.4 5.8 2.5 5.4 8.8 9.0 9.1 8.9 9.0 8.2 5.5 7.6 9.2 9.4 9.6 9.8 9.8 10.0 230.5 7.3 3.5 1.4 8.2 8.4 8.7 9.0 9.0 9.0 7.8 4.9 6.6 9.5 10.0 10.0 10.0 10.1 10.4 237.5 7.3 3.2 0.3 6.6 8.4 8.6 8.8 8.9 9.2 8.1 4.7 8.7 9.4 8.3 8.0 7.5 7.3 7.4 244.5 6.6 2.9 1.2 5.2 2.9 3.8 8.5 8.7 9.1 6.8 4.4 6.9 9.5 9.9 9.7 9.9 10.0 10.0 251.5 7.7 4.8 0.9 6.2 8.0 8.2 8.2 8.2 8.3 6.9 3.4 7.5 9.3 9.3 9.5 9.3 9.2 9.1 258.5 5.2 3.6 0.2 7.8 7.8 8.0 8.3 8.3 8.3 7.5 5.8 8.1 9.4 9.5 9.4 9.3 9.5 9.6 265.5 5.4 2.7 0.2 6.4 6.3 6.7 6.8 6.7 6.9 7.2 7.4 6.8 9.1 9.4 9.4 9.4 9.5 9.5 274.5 6.8 2.2 0.1 6.6 6.7 6.7 6.7 6.7 6.8 6.7 6.0 6.9 8.5 8.7 8.8 8.7 8.8 8.8 279.5 5.5 3.0 0.3 2.9 8.2 7.7 6.8 6.7 6.5 7.2 7.4 6.8 8.3 8.6 8.6 8.6 8.6 8.6 286.5 7.0 2.2 1.8 1.8 0.7 3.7 6.6 6.7 7.1 7.5 8.1 7.8 8.2 8.7 8.8 9.0 9.0 9.0 293.5 5.1 4.3 2.8 4.2 5.8 10.1 6.6 6.1 6.2 7.5 7.9 7.1 8.6 8.9 9.1 9.0 9.0 9.0 302.5 6.8 5.2 5.6 0.2 2.7 2.1 1.5 5.8 6.1 7.6 8.0 7.5 8.4 8.6 8.6 8.9 8.9 9.0 307.5 6.8 3.4 1.8 4.9 4.5 4.9 6.9 7.7 6.5 7.2 7.6 6.6 8.3 8.2 8.3 6.4 8.8 8.9 314.5 5.7 3.5 3.9 3.4 1.4 2.0 1.4 - 5.4 7.6 7.8 7.3 8.2 8.4 8.4 8.7 9.0 9.3 321.5 6.9 4.4 6.8 4.9 3.7 5.6 3.3 3.6 6.0 7.5 7.5 7.2 8.3 8.4 8.3 7.6 8.4 8.5 328.5 5.7 4.1 3.6 4.7 0.1 0.3 1.3 0.9 0.8 7.7 7.6 7.2 7.8 8.1 8.1 8.2 8.2 8.4 335.5 4.1 5.1 5.1 5.4 0.9 2.1 1.1 2.0 3.1 6.7 6.2 6.4 7.7 8.3 8.4 7.3 8.0 7.6 342.5 6.0 2.8 4.9 5.1 2.9 1.5 2.9 2.4 1.6 6.9 6.7 5.8 7.1 7.2 7.3 7.2 7.3 7.3 349.5 6.4 3.0 5.7 5.2 3.2 3.1 2.7 4.7 3.9 6.9 6.9 6.0 7.1 7.2 7.1 6.3 7.1 6.8 356.5 5.9 4.2 5.5 5.2 0.7 0.6 4.4 1.5 0.7 6.9 7.1 6.0 7.0 7.1 7.1 7.1 7.2 7.1 363.5 6.0 3.1 5.5 5.5 0.9 4.8 5.1 4.7 2.6 7.0 6.7 6.8 7.0 7.4 7.2 6.0 7.5 7.6

Table A9. Surface phosphate, 1997, R2 = 0.35. 1997 FIELD DATA, SURFACE SITE DAY 1 2 3 4 5 6 7 8 9

MODEL DATA, SURFACE SITE 1 2 3 4 5 6 7 8 9

7.5 0.017 0.024 0.039 0.045 0.044 0.031 0.018 0.018 0.010 14.5 0.025 0.030 0.036 0.043 0.039 0.037 0.019 0.013 0.011 21.5 0.021 0.033 0.043 0.070 0.072 0.058 0.029 0.016 0.014 28.5 0.022 0.026 0.038 0.046 0.059 0.060 0.037 0.034 0.021 34.5 0.018 0.028 0.039 0.044 0.068 0.061 0.037 0.031 0.031 42.5 0.020 0.028 0.039 0.066 0.075 0.071 0.037 0.033 0.040 49.5 0.028 0.036 0.052 0.078 0.093 0.096 0.053 0.047 0.037 55.5 0.019 0.026 0.048 0.076 0.100 0.100 0.073 0.067 0.060 64.5 0.019 0.028 0.033 0.037 0.054 0.059 0.040 0.042 0.044 69.5 0.014 0.024 0.042 0.048 0.076 0.089 0.042 0.043 0.038 76.5 0.009 0.028 0.038 0.047 0.079 0.076 0.049 0.054 0.041 83.5 0.014 0.012 0.032 0.041 0.059 0.064 0.053 0.053 0.044 92.5 0.018 0.023 0.032 0.064 0.061 0.047 0.031 0.024 0.021 97.5 0.017 0.019 0.016 0.018 0.039 0.045 0.034 0.027 0.026 104.5 0.008 0.006 0.005 0.012 0.016 0.019 0.039 0.037 0.040 111.5 0.004 0.012 0.013 0.010 0.012 0.015 0.017 0.042 0.048 118.5 0.013 0.017 0.037 0.052 0.078 0.082 0.063 0.094 0.041 125.5 0.011 0.020 0.035 0.063 0.062 0.055 0.041 0.032 0.024 132.5 0.014 0.022 0.033 0.044 0.054 0.056 0.053 0.042 0.039 139.5 0.015 0.024 0.031 0.034 0.032 0.037 0.040 0.023 0.019 146.5 0.016 0.022 0.030 0.045 0.046 0.046 0.030 0.021 0.022 154.5 0.017 0.020 0.026 0.035 0.030 0.029 0.022 0.025 0.023 160.5 0.019 0.017 0.023 0.019 0.021 0.026 0.009 0.013 0.012 167.5 0.008 0.005 0.008 0.012 0.014 0.026 0.023 0.028 0.021 174.5 0.012 0.006 0.009 0.013 0.016 0.021 0.024 0.022 0.025 181.5 0.002 0.002 0.003 0.004 0.005 0.010 0.016 0.017 0.014 190.5 0.003 0.006 0.013 0.011 0.024 0.019 0.019 0.021 0.018 195.5 0.005 0.005 0.015 0.021 0.024 0.023 0.021 0.016 0.024 202.5 0.020 0.037 0.011 0.016 0.023 0.029 0.020 0.019 0.016 209.5 0.019 0.042 0.018 0.021 0.021 0.024 0.025 0.015 0.023 216.5 0.022 0.035 0.008 0.010 0.021 0.026 0.028 0.024 0.019 223.5 - - - - - - - - - 226.5 0.012 0.049 0.033 0.030 0.027 0.022 0.019 0.019 0.018 230.5 0.019 0.043 0.019 0.033 0.037 0.051 0.031 0.029 0.027 237.5 0.023 0.050 0.019 0.020 0.017 0.016 0.015 0.014 0.013 244.5 0.016 0.041 0.003 0.018 0.026 0.029 0.025 0.021 0.020 251.5 0.016 0.050 0.027 0.037 0.044 0.043 0.037 0.032 0.031 258.5 0.038 0.048 0.034 0.036 0.028 0.028 0.026 0.026 0.027 265.5 0.017 0.055 0.007 0.026 0.021 0.022 0.020 0.020 0.019 274.5 0.025 0.054 0.016 0.008 0.010 0.016 0.014 0.016 0.015 279.5 0.016 0.035 0.011 0.004 0.006 0.008 0.018 0.016 0.015 286.5 0.014 0.038 0.006 0.006 0.017 0.021 0.021 0.018 0.015 293.5 0.018 0.024 0.016 0.004 0.004 0.003 - 0.014 0.013 302.5 0.011 0.037 0.002 0.002 0.002 0.002 0.002 0.006 0.004 307.5 0.015 0.036 0.009 0.004 0.002 0.007 0.002 0.002 0.005 314.5 0.019 0.047 0.008 0.008 0.009 0.011 0.015 0.011 0.010 321.5 0.028 0.038 0.015 0.021 0.012 0.010 0.010 0.010 0.007 328.5 0.015 0.054 0.008 0.010 0.002 0.003 0.006 0.006 0.010 335.5 0.021 0.032 0.021 0.014 0.010 0.009 0.011 0.010 0.006 342.5 0.016 0.045 0.015 0.007 0.009 0.007 0.007 0.006 0.003 349.5 0.017 0.025 0.021 0.021 0.013 0.014 0.015 0.016 0.015 356.5 0.022 0.038 0.021 0.024 0.002 0.004 0.010 0.010 0.010 363.5 0.014 0.027 0.024 0.015 0.015 0.016 0.013 0.011 0.012

0.017 0.024 0.039 0.045 0.044 0.031 0.018 0.018 0.010 0.024 0.028 0.039 0.049 0.047 0.041 0.036 0.033 0.028 0.020 0.035 0.054 0.061 0.057 0.051 0.049 0.050 0.046 0.017 0.031 0.058 0.075 0.070 0.067 0.068 0.065 0.065 0.009 0.021 0.039 0.071 0.075 0.074 0.076 0.077 0.078 0.009 0.024 0.047 0.074 0.072 0.074 0.082 0.086 0.087 0.008 0.020 0.032 0.070 0.069 0.071 0.076 0.088 0.091 0.010 0.017 0.048 0.068 0.067 0.071 0.078 0.089 0.092 0.009 0.019 0.029 0.063 0.076 0.077 0.082 0.087 0.096 0.007 0.017 0.024 0.070 0.072 0.068 0.082 0.105 0.102 0.011 0.014 0.028 0.050 0.063 0.067 0.069 0.093 0.090 0.010 0.021 0.033 0.055 0.062 0.062 0.075 0.085 0.098 0.018 0.024 0.043 0.066 0.072 0.078 0.084 0.124 0.103 0.012 0.026 0.030 0.056 0.066 0.069 0.075 0.074 0.074 0.013 0.033 0.045 0.064 0.065 0.064 0.057 0.050 0.049 0.016 0.036 0.052 0.046 0.067 0.068 0.077 0.081 0.082 0.025 0.064 0.066 0.072 0.076 0.075 0.081 0.097 0.089 0.022 0.076 0.091 0.067 0.068 0.068 0.084 0.098 0.103 0.016 0.035 0.062 0.100 0.104 0.105 0.108 0.145 0.131 0.011 0.035 0.071 0.069 0.086 0.086 0.100 0.110 0.113 0.012 0.062 0.087 0.077 0.082 0.085 0.089 0.103 0.103 0.009 0.033 0.082 0.075 0.080 0.084 0.086 0.086 0.085 0.022 0.053 0.092 0.064 0.065 0.057 0.044 0.041 0.033 0.009 0.031 0.078 0.078 0.058 0.052 0.040 0.034 0.032 0.023 0.031 0.065 0.070 0.051 0.045 0.036 0.035 0.032 0.011 0.044 0.048 0.062 0.057 0.064 0.067 0.068 0.064 0.008 0.088 0.071 0.067 0.063 0.061 0.063 0.067 0.068 0.008 0.010 0.139 0.051 0.062 0.049 0.033 0.026 0.020 0.005 0.038 0.064 0.093 0.074 0.057 0.043 0.036 0.026 0.009 0.052 0.088 0.067 0.053 0.042 0.052 0.070 0.082 0.012 0.047 0.064 0.059 0.064 0.058 0.042 0.038 0.035 0.004 0.067 0.059 0.030 0.033 0.035 0.038 0.041 0.037 0.006 0.048 0.075 0.114 0.048 0.039 0.031 0.028 0.017 0.011 0.096 0.085 0.049 0.038 0.033 0.035 0.034 0.023 0.009 0.039 0.073 0.069 0.042 0.047 0.022 0.018 0.017 0.014 0.071 0.069 0.074 0.083 0.075 0.074 0.059 0.046 0.005 0.074 0.076 0.042 0.040 0.036 0.039 0.042 0.043 0.006 0.036 0.092 0.046 0.044 0.038 0.033 0.031 0.030 0.007 0.027 0.054 0.111 0.042 0.037 0.033 0.031 0.029 0.009 0.021 0.040 0.121 0.093 0.078 0.047 0.042 0.039 0.005 0.028 0.037 0.117 0.114 0.081 0.056 0.046 0.041 0.006 0.020 0.030 0.099 0.062 0.050 0.041 0.040 0.040 0.004 0.010 0.028 0.056 0.057 0.066 0.063 0.055 0.047 0.005 0.010 0.017 0.042 0.050 0.045 0.044 0.039 0.036 0.004 0.007 0.017 0.029 0.041 0.036 0.035 0.033 0.029 0.006 0.007 0.015 0.034 0.031 0.029 0.027 0.022 0.015 0.004 0.006 0.015 0.031 0.030 0.026 0.026 0.024 0.020 0.004 0.006 0.017 0.030 0.019 0.019 0.021 0.021 0.019 0.007 0.017 0.029 0.042 0.040 0.032 0.032 0.031 0.032 0.008 0.013 0.025 0.043 0.041 0.043 0.044 0.048 0.047 0.008 0.010 0.029 0.054 0.046 0.044 0.047 0.049 0.051 0.007 0.013 0.037 0.055 0.052 0.049 0.052 0.056 0.058 0.008 0.015 0.033 0.052 0.050 0.049 0.051 0.055 0.058

Table A10. Near-bed phosphate, 1997, R2 = 0.03. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9

MODEL DATA, BED SITE 1 2 3 4 5 6 7 8 9

7.5 0.014 0.027 0.041 0.050 0.071 0.078 0.041 0.031 0.016 14.5 0.032 0.036 - 0.043 0.260 0.140 0.082 0.059 0.044 21.5 0.013 0.025 0.043 0.071 0.110 0.079 0.062 0.039 0.027 28.5 0.012 0.027 0.039 0.043 0.077 0.073 0.068 0.058 0.043 34.5 0.012 0.026 0.035 0.047 0.100 0.120 0.110 0.093 0.069 42.5 0.016 0.024 0.038 0.062 0.087 0.100 0.068 0.068 0.069 49.5 0.022 0.035 0.049 0.068 0.120 0.120 0.110 0.078 0.065 55.5 0.019 0.033 0.040 0.059 0.100 0.120 0.110 0.110 0.100 64.5 0.017 0.030 0.030 0.042 0.067 0.074 0.075 0.064 0.081 69.5 0.011 0.038 0.038 0.053 0.073 0.085 0.080 0.054 0.055 76.5 0.014 0.027 0.038 0.054 0.072 0.075 0.093 0.067 0.066 83.5 0.014 0.028 0.032 0.036 0.068 0.074 0.073 0.074 0.069 92.5 0.019 0.024 0.029 0.063 0.120 0.110 0.150 0.120 0.085 97.5 0.016 0.026 0.032 0.020 0.069 0.079 0.098 0.170 0.085 104.5 0.021 0.035 0.009 0.056 - 0.080 0.160 0.200 0.180 111.5 0.019 0.050 0.032 0.069 0.220 0.160 0.250 0.390 0.310 118.5 0.013 0.051 0.035 0.056 0.140 0.120 0.130 0.350 0.055 125.5 0.010 0.022 0.036 0.049 0.120 0.089 0.100 0.120 0.130 132.5 0.012 0.018 0.032 0.042 0.058 0.060 0.065 0.057 0.050 139.5 0.014 0.025 0.032 0.046 0.069 0.069 0.058 0.068 0.068 146.5 0.015 0.021 0.030 0.044 0.072 0.056 0.058 0.069 0.093 154.5 0.014 0.032 0.026 0.044 0.077 0.060 0.049 0.088 0.050 160.5 0.014 0.024 0.028 0.024 0.099 0.034 0.130 0.025 0.016 167.5 0.007 0.029 0.033 0.014 0.140 0.049 0.630 0.056 0.020 174.5 0.009 0.025 0.032 0.009 0.053 0.025 0.066 0.150 0.045 181.5 0.002 0.015 0.026 0.015 0.030 0.042 0.067 0.072 0.094 190.5 0.008 0.017 0.012 0.028 0.070 0.038 0.150 0.170 0.017 195.5 0.008 0.024 0.022 0.030 0.410 0.099 0.500 1.800 0.020 202.5 0.020 0.037 0.036 0.014 0.200 0.037 0.720 0.084 0.015 209.5 0.019 0.042 0.020 0.031 0.056 0.030 0.110 0.560 0.026 216.5 0.016 0.012 0.017 0.014 0.300 0.051 0.330 0.110 0.063 223.5 - - - - - - - - - 226.5 0.031 0.043 0.028 0.033 0.023 0.023 0.020 0.019 0.018 230.5 0.061 0.062 0.047 0.034 0.036 0.035 0.031 0.029 0.028 237.5 0.037 0.037 0.055 0.024 0.017 0.017 0.015 0.014 0.013 244.5 0.030 0.030 0.043 0.016 0.038 0.032 0.025 0.024 0.019 251.5 0.011 0.009 0.041 0.033 0.047 0.042 0.036 0.033 0.031 258.5 0.009 0.008 0.059 0.037 0.029 0.029 0.027 0.027 0.027 265.5 0.015 0.015 0.041 0.026 0.026 0.022 0.022 0.021 0.020 274.5 0.015 0.029 0.050 0.010 0.017 0.018 0.017 0.017 0.014 279.5 0.030 0.013 0.033 0.029 0.007 0.011 0.021 0.017 0.017 286.5 0.012 0.009 0.031 0.019 0.027 0.023 0.023 0.020 0.018 293.5 0.013 0.010 0.028 0.013 0.006 0.004 0.016 0.015 0.014 302.5 0.017 0.011 0.003 0.035 0.002 0.002 0.008 0.003 0.002 307.5 0.008 0.009 0.008 0.010 0.007 0.006 0.002 0.002 0.003 314.5 0.018 0.020 0.022 0.018 0.009 0.011 0.011 0.013 0.037 321.5 0.018 0.017 0.012 0.020 0.009 0.010 0.011 0.010 0.009 328.5 0.021 0.024 0.018 0.012 0.010 0.008 0.005 0.008 0.009 335.5 0.017 0.023 0.022 0.018 0.028 0.016 0.018 0.011 0.018 342.5 0.022 0.026 0.022 0.005 0.005 0.006 0.006 0.008 0.011 349.5 0.021 0.025 0.019 0.013 0.019 0.020 0.020 0.018 0.018 356.5 0.026 0.024 0.023 0.004 0.014 0.014 0.015 0.020 0.023 363.5 0.023 0.028 0.021 0.014 0.019 0.018 0.016 0.016 0.018

0.014 0.027 0.041 0.050 0.071 0.078 0.041 0.031 0.016 0.024 0.043 0.044 0.049 0.049 0.046 0.042 0.033 0.029 0.026 0.087 0.064 0.062 0.063 0.055 0.071 0.050 0.048 0.022 0.044 0.060 0.075 0.079 0.072 0.069 0.065 0.065 0.013 0.065 0.056 0.071 0.077 0.076 0.081 0.077 0.078 0.045 0.059 0.069 0.079 0.075 0.076 0.094 0.087 0.089 0.008 0.059 0.042 0.070 0.073 0.071 0.080 0.088 0.094 0.021 0.038 0.049 0.068 0.069 0.073 0.080 0.089 0.096 0.013 0.056 0.061 0.063 0.077 0.078 0.084 0.087 0.096 0.042 0.053 0.042 0.070 0.076 0.081 0.091 0.105 0.116 0.016 0.035 0.037 0.051 0.066 0.066 0.086 0.088 0.100 0.027 0.038 0.043 0.050 0.066 0.069 0.081 0.095 0.102 0.015 0.041 0.038 0.056 0.064 0.064 0.081 0.085 0.098 0.035 0.039 0.046 0.068 0.084 0.106 0.124 0.126 0.146 0.031 0.053 0.048 0.057 0.077 0.074 0.077 0.076 0.074 0.056 0.062 0.067 0.067 0.067 0.079 0.079 0.057 0.061 0.031 0.148 0.062 0.063 0.070 0.075 0.093 0.082 0.083 0.051 0.272 0.070 0.079 0.079 0.085 0.093 0.099 0.105 0.028 0.094 0.136 0.070 0.086 0.077 0.098 0.115 0.131 0.077 0.104 0.072 0.100 0.115 0.125 0.143 0.152 0.158 0.045 0.074 0.087 0.077 0.092 0.093 0.112 0.110 0.116 0.044 0.097 0.129 0.094 0.091 0.091 0.099 0.089 0.093 0.040 0.123 0.121 0.076 0.085 0.088 0.103 0.089 0.095 0.028 0.151 0.183 0.070 0.073 0.064 0.081 0.045 0.035 0.069 0.258 0.125 0.104 0.080 0.080 0.086 0.094 0.035 0.106 0.722 0.105 0.076 0.068 0.066 0.071 0.035 0.041 0.031 0.497 0.090 0.072 0.071 0.068 0.089 0.075 0.072 0.012 0.523 0.206 0.084 0.082 0.072 0.090 0.067 0.068 0.079 0.190 0.237 0.070 0.064 0.069 0.099 0.026 0.020 0.021 0.132 0.146 0.120 0.094 0.097 0.098 0.036 0.026 0.020 0.102 0.128 0.078 0.120 0.071 0.088 0.070 0.083 0.023 0.351 0.082 0.059 0.064 0.063 0.073 0.038 0.035 0.075 0.358 0.114 0.067 0.048 0.035 0.038 0.041 0.037 0.065 0.613 0.147 0.114 0.048 0.039 0.031 0.028 0.017 0.102 0.554 0.111 0.097 0.038 0.033 0.036 0.034 0.023 0.032 0.617 0.113 0.070 0.042 0.059 0.022 0.018 0.017 0.026 0.738 0.095 0.107 0.190 0.088 0.074 0.059 0.046 0.047 0.379 0.122 0.058 0.040 0.036 0.039 0.042 0.043 0.025 0.094 0.231 0.090 0.044 0.038 0.034 0.031 0.030 0.029 0.105 0.112 0.135 0.043 0.038 0.034 0.031 0.029 0.024 0.073 0.119 0.123 0.095 0.078 0.050 0.042 0.039 0.009 0.107 0.118 0.133 0.115 0.092 0.056 0.046 0.041 0.031 0.076 0.082 0.101 0.065 0.050 0.042 0.040 0.040 0.008 0.073 0.054 0.062 0.115 0.075 0.064 0.055 0.047 0.016 0.030 0.109 0.044 0.054 0.047 0.079 0.039 0.036 0.008 0.034 0.031 0.038 0.042 0.041 0.035 0.033 0.030 0.012 0.027 0.056 0.036 0.035 0.033 0.045 0.024 0.022 0.005 0.030 0.025 0.031 0.033 0.031 0.028 0.024 0.020 0.017 0.037 0.038 0.033 0.026 0.023 0.042 0.023 0.026 0.015 0.050 0.034 0.042 0.043 0.032 0.033 0.031 0.032 0.015 0.046 0.041 0.043 0.041 0.045 0.051 0.048 0.050 0.013 0.044 0.043 0.054 0.046 0.045 0.047 0.049 0.051 0.011 0.046 0.037 0.055 0.054 0.055 0.066 0.056 0.059

Table A11. Surface total phosphorus, 1997, R2 = 0.212. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.05 0.04 0.05 0.10 0.07 0.08 0.07 0.14 0.07 14.5 0.04 0.05 0.07 0.10 0.10 0.08 0.09 0.10 0.06 21.5 0.04 0.06 0.07 0.10 0.12 0.13 0.10 0.09 0.07 28.5 0.03 0.05 0.08 0.09 0.13 0.13 0.10 0.12 0.08 34.5 0.04 0.05 0.07 0.11 0.24 0.14 0.23 0.21 0.10 42.5 0.04 0.05 0.06 0.09 0.12 0.11 0.11 0.11 0.11 49.5 0.03 0.04 0.05 0.10 0.11 0.10 0.12 0.12 0.08 55.5 0.05 0.05 0.07 0.11 0.15 0.14 0.22 0.15 0.12 64.5 0.04 0.05 0.07 0.10 0.11 0.14 0.12 0.13 0.11 69.5 0.03 0.04 0.06 0.08 0.11 0.11 0.11 0.10 0.10 76.5 0.02 0.04 0.05 0.08 0.19 0.15 0.16 0.13 0.12 83.5 0.03 0.04 0.06 0.09 0.13 0.12 0.16 0.17 0.16 92.5 0.05 0.06 0.06 0.17 0.11 0.11 0.08 0.06 0.06 97.5 0.04 0.03 0.09 0.09 0.09 0.08 0.07 0.05 0.04 104.5 0.02 0.02 0.08 0.31 0.26 0.14 0.06 0.08 0.05 111.5 0.04 0.05 0.09 0.09 0.16 0.07 0.18 0.11 0.11 118.5 0.03 0.04 0.08 0.13 0.20 0.17 0.13 1.70 0.12 125.5 0.03 0.08 0.08 0.93 0.90 0.25 0.12 0.12 0.12 132.5 0.04 0.05 0.06 0.09 0.09 0.15 0.09 0.10 0.13 139.5 0.03 0.03 0.05 0.08 0.22 0.10 0.24 0.10 0.08 146.5 0.04 0.05 0.08 0.09 0.10 0.13 0.10 0.07 0.14 154.5 0.04 0.05 0.06 0.10 0.07 0.19 0.08 0.05 0.12 160.5 0.04 0.04 0.05 0.10 0.09 0.14 0.04 0.04 0.03 167.5 0.03 0.03 0.03 0.06 0.06 0.05 0.03 0.04 0.03 174.5 0.04 0.04 0.04 0.18 0.15 0.17 0.05 0.05 0.07 181.5 0.01 0.01 0.02 0.04 0.04 0.04 0.03 0.05 0.03 190.5 0.02 0.03 0.03 0.03 0.11 0.04 0.04 0.04 0.04 195.5 0.02 0.02 0.03 0.04 0.05 0.05 0.05 0.04 0.03 202.5 - - 0.03 0.06 0.05 0.06 0.04 0.03 0.03 209.5 - - 0.03 0.03 0.04 0.04 0.03 0.02 0.04 216.5 - - 0.03 0.04 0.04 0.05 0.08 0.03 0.03 223.5 - - - - - - - - - 226.5 - - 0.07 0.06 0.05 0.07 0.05 0.04 0.04 230.5 - - 0.06 0.05 0.05 0.05 0.04 0.04 0.04 237.5 - - 0.05 0.05 0.05 0.05 0.05 0.05 0.04 244.5 - - 0.03 0.06 0.05 0.05 0.04 0.04 0.04 251.5 - - 0.04 0.07 0.07 0.08 0.06 0.05 0.06 258.5 - - 0.06 0.10 0.06 0.05 0.04 0.03 0.04 265.5 - - 0.05 0.06 0.06 0.04 0.04 0.03 0.02 274.5 - - 0.05 0.06 0.07 0.06 0.05 0.04 0.04 279.5 - - 0.05 0.07 0.07 0.07 0.05 0.05 0.04 286.5 - - 0.03 0.04 0.06 0.06 0.05 0.04 0.03 293.5 - - 0.07 0.05 0.06 0.08 - 0.04 0.04 302.5 - - 0.03 0.05 0.06 0.03 0.04 0.05 0.03 307.5 - - 0.06 0.06 0.12 0.05 0.09 0.03 0.03 314.5 - - 0.04 0.04 0.06 0.06 0.11 0.05 0.04 321.5 - - 0.05 0.08 0.06 0.11 0.08 0.05 0.03 328.5 - - 0.08 0.09 0.14 0.10 0.17 0.12 0.10 335.5 - - 0.09 0.11 0.12 0.16 0.16 0.13 0.11 342.5 - - 0.07 0.08 0.08 0.10 0.14 0.10 0.07 349.5 - - 0.12 0.12 0.15 0.15 0.16 0.13 0.09 356.5 - - 0.10 0.10 0.15 0.15 0.18 0.16 0.10 363.5 - - 0.08 0.08 0.23 0.14 0.10 0.17 0.11

0.05 0.04 0.05 0.10 0.07 0.08 0.07 0.14 0.07 0.05 0.06 0.07 0.07 0.06 0.06 0.06 0.07 0.07 0.03 0.05 0.07 0.07 0.07 0.06 0.06 0.07 0.06 0.02 0.04 0.07 0.08 0.08 0.08 0.08 0.08 0.08 0.01 0.03 0.05 0.08 0.09 0.08 0.09 0.09 0.09 0.01 0.03 0.06 0.09 0.09 0.08 0.10 0.10 0.10 0.01 0.03 0.04 0.08 0.08 0.08 0.09 0.10 0.10 0.01 0.02 0.05 0.08 0.08 0.08 0.09 0.10 0.11 0.02 0.03 0.04 0.07 0.09 0.09 0.09 0.10 0.11 0.01 0.02 0.03 0.08 0.08 0.08 0.09 0.12 0.11 0.01 0.02 0.04 0.06 0.07 0.08 0.08 0.11 0.10 0.02 0.03 0.04 0.06 0.07 0.07 0.09 0.10 0.11 0.02 0.03 0.05 0.08 0.09 0.09 0.10 0.14 0.12 0.02 0.03 0.04 0.06 0.07 0.07 0.08 0.08 0.08 0.02 0.04 0.05 0.08 0.08 0.07 0.06 0.06 0.06 0.02 0.04 0.06 0.05 0.07 0.07 0.08 0.08 0.08 0.03 0.07 0.07 0.08 0.08 0.08 0.09 0.10 0.09 0.02 0.08 0.10 0.08 0.08 0.08 0.09 0.10 0.11 0.02 0.04 0.07 0.10 0.11 0.11 0.11 0.15 0.13 0.01 0.04 0.08 0.08 0.09 0.09 0.10 0.11 0.12 0.02 0.07 0.09 0.08 0.09 0.09 0.09 0.11 0.11 0.01 0.04 0.09 0.08 0.08 0.09 0.09 0.09 0.09 0.03 0.06 0.10 0.07 0.07 0.06 0.05 0.05 0.04 0.01 0.04 0.08 0.08 0.06 0.06 0.05 0.04 0.04 0.03 0.04 0.07 0.08 0.05 0.05 0.04 0.04 0.03 0.02 0.05 0.06 0.07 0.08 0.07 0.08 0.07 0.07 0.02 0.10 0.09 0.07 0.06 0.06 0.07 0.07 0.08 0.01 0.02 0.16 0.07 0.07 0.05 0.04 0.03 0.03 0.01 0.05 0.07 0.10 0.08 0.06 0.05 0.04 0.03 0.02 0.06 0.10 0.07 0.05 0.04 0.06 0.08 0.09 0.02 0.06 0.08 0.07 0.07 0.06 0.05 0.05 0.05 0.01 0.08 0.08 0.05 0.06 0.07 0.07 0.08 0.08 0.01 0.06 0.09 0.13 0.08 0.07 0.08 0.08 0.06 0.02 0.11 0.11 0.06 0.06 0.06 0.07 0.07 0.06 0.02 0.05 0.09 0.08 0.05 0.05 0.03 0.03 0.02 0.02 0.08 0.08 0.08 0.09 0.09 0.10 0.08 0.07 0.01 0.09 0.10 0.05 0.06 0.06 0.07 0.08 0.08 0.01 0.04 0.11 0.06 0.07 0.07 0.07 0.07 0.06 0.01 0.03 0.06 0.12 0.05 0.05 0.05 0.05 0.05 0.02 0.03 0.05 0.13 0.10 0.09 0.06 0.05 0.05 0.01 0.03 0.04 0.13 0.12 0.09 0.06 0.05 0.05 0.01 0.03 0.04 0.11 0.10 0.07 0.06 0.06 0.05 0.01 0.02 0.04 0.07 0.07 0.08 0.07 0.07 0.06 0.01 0.02 0.03 0.05 0.06 0.05 0.06 0.06 0.06 0.01 0.01 0.03 0.05 0.05 0.05 0.05 0.05 0.06 0.01 0.01 0.03 0.05 0.04 0.04 0.04 0.05 0.05 0.01 0.01 0.03 0.05 0.04 0.04 0.04 0.04 0.05 0.01 0.01 0.03 0.05 0.03 0.03 0.04 0.04 0.04 0.01 0.03 0.04 0.06 0.05 0.04 0.04 0.05 0.05 0.01 0.02 0.04 0.06 0.05 0.05 0.05 0.06 0.06 0.01 0.02 0.04 0.07 0.05 0.05 0.06 0.06 0.06 0.01 0.02 0.05 0.06 0.06 0.05 0.06 0.06 0.07 0.01 0.02 0.04 0.06 0.06 0.06 0.06 0.07 0.07

Table A12. Near-bed total phosphorus, 1997, R2 = 0.01. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.02 0.03 0.06 0.15 0.17 0.17 0.11 0.08 0.09 14.5 0.05 0.06 - 0.14 0.41 0.24 0.16 0.11 0.09 21.5 0.03 0.04 0.07 0.12 0.21 0.20 0.15 0.11 0.09 28.5 0.02 0.05 0.06 0.11 0.28 0.16 0.17 0.13 0.10 34.5 0.02 0.06 0.08 0.08 0.23 0.18 0.22 0.18 0.18 42.5 0.03 0.04 0.05 0.13 0.14 0.16 0.14 0.13 0.09 49.5 0.03 0.04 0.05 0.08 0.19 0.17 0.15 0.15 0.11 55.5 0.04 0.06 0.07 0.10 0.21 0.18 0.21 0.17 0.16 64.5 0.04 0.06 0.04 0.11 0.14 0.16 0.14 0.16 0.19 69.5 0.02 0.05 0.05 0.08 0.11 0.14 0.14 0.12 0.12 76.5 0.02 0.04 0.06 0.10 0.12 0.12 0.16 0.15 0.16 83.5 0.01 0.06 0.06 0.10 0.15 0.15 0.13 0.13 0.14 92.5 0.10 0.04 0.07 0.13 0.20 0.16 0.19 0.17 0.14 97.5 0.04 0.04 0.05 0.04 0.11 0.11 0.14 0.34 0.13 104.5 0.02 0.04 0.08 0.11 - 0.09 0.19 0.29 0.27 111.5 0.04 0.09 0.14 0.22 0.61 0.30 0.51 0.61 0.50 118.5 0.03 0.09 0.09 0.12 0.23 0.24 0.26 0.79 0.15 125.5 0.02 0.04 0.04 0.13 0.25 0.14 0.18 0.37 0.25 132.5 0.04 0.05 0.07 0.08 0.10 0.10 0.10 0.10 0.13 139.5 0.01 0.03 0.04 0.12 0.10 0.09 0.12 0.08 0.09 146.5 0.04 0.05 0.07 0.11 0.18 0.16 0.14 0.16 0.20 154.5 0.03 0.06 0.05 0.08 0.13 0.10 0.09 0.17 0.16 160.5 0.03 0.03 0.06 0.06 0.29 0.08 0.36 0.07 0.15 167.5 0.02 0.05 0.06 0.04 0.28 0.10 1.10 0.08 0.03 174.5 0.04 0.05 0.06 0.05 0.12 0.05 0.13 0.22 0.10 181.5 0.01 0.03 0.08 0.07 0.07 0.10 0.13 0.15 0.18 190.5 0.02 0.03 0.05 0.09 0.14 0.10 0.24 0.27 0.03 195.5 0.01 0.04 0.04 0.10 0.55 0.28 0.66 2.60 0.03 202.5 - - 0.06 0.06 0.56 0.09 0.87 0.49 0.03 209.5 - - 0.03 0.05 0.09 0.05 0.17 0.65 0.03 216.5 - - 0.04 0.07 0.38 0.13 0.41 0.23 0.11 223.5 - - - - - - - - - 226.5 - - 0.04 0.07 0.06 0.05 0.04 0.04 0.04 230.5 - - 0.06 0.05 0.07 0.05 0.04 0.04 0.04 237.5 - - 0.08 0.06 0.06 0.05 0.05 0.04 0.04 244.5 - - 0.07 0.06 0.08 0.06 0.04 0.06 0.03 251.5 - - 0.08 0.06 0.14 0.07 0.07 0.06 0.06 258.5 - - 0.06 0.07 0.05 0.05 0.04 0.04 0.03 265.5 - - 0.06 0.05 0.07 0.06 0.05 0.03 0.03 274.5 - - 0.09 0.05 0.06 0.06 0.04 0.06 0.03 279.5 - - 0.08 0.10 0.08 0.06 0.06 0.06 0.04 286.5 - - 0.06 0.07 0.05 0.07 0.05 0.04 0.05 293.5 - - 0.10 0.05 0.12 0.06 0.05 0.05 0.03 302.5 - - 0.05 0.18 0.11 0.08 0.05 0.03 0.02 307.5 - - 0.05 0.08 0.07 0.09 0.05 0.02 0.03 314.5 - - 0.06 0.08 0.09 0.12 0.11 0.09 0.06 321.5 - - 0.04 0.11 0.18 0.08 0.25 0.09 0.03 328.5 - - 0.07 0.08 0.09 0.09 0.16 0.18 0.12 335.5 - - 0.09 0.10 0.14 0.17 0.16 0.13 0.08 342.5 - - 0.07 0.08 0.16 0.14 0.14 0.10 0.12 349.5 - - 0.11 0.14 0.20 0.14 0.17 0.13 0.13 356.5 - - 0.10 0.10 0.14 0.14 0.19 0.14 0.13 363.5 - - 0.08 0.08 0.20 0.16 0.16 0.16 0.14

0.02 0.03 0.06 0.15 0.17 0.17 0.11 0.08 0.09 0.05 0.11 0.08 0.07 0.06 0.06 0.08 0.07 0.07 0.04 0.11 0.08 0.07 0.07 0.07 0.08 0.07 0.06 0.03 0.07 0.07 0.08 0.09 0.08 0.08 0.08 0.08 0.02 0.07 0.07 0.08 0.09 0.08 0.09 0.09 0.09 0.06 0.07 0.08 0.10 0.09 0.09 0.11 0.10 0.10 0.01 0.06 0.05 0.08 0.09 0.09 0.09 0.10 0.11 0.03 0.05 0.06 0.08 0.09 0.08 0.10 0.10 0.11 0.02 0.06 0.07 0.07 0.09 0.09 0.10 0.10 0.11 0.05 0.06 0.05 0.08 0.09 0.09 0.10 0.12 0.13 0.02 0.05 0.05 0.06 0.08 0.08 0.10 0.10 0.11 0.04 0.06 0.05 0.06 0.07 0.08 0.09 0.11 0.12 0.02 0.06 0.04 0.07 0.07 0.07 0.10 0.10 0.11 0.04 0.04 0.05 0.08 0.09 0.11 0.13 0.14 0.16 0.03 0.08 0.06 0.06 0.08 0.08 0.09 0.08 0.08 0.06 0.09 0.08 0.08 0.08 0.09 0.09 0.06 0.07 0.03 0.15 0.07 0.07 0.08 0.08 0.10 0.09 0.09 0.07 0.28 0.08 0.09 0.08 0.09 0.10 0.10 0.11 0.03 0.10 0.14 0.08 0.09 0.08 0.10 0.12 0.13 0.08 0.11 0.08 0.11 0.12 0.13 0.14 0.15 0.16 0.05 0.09 0.09 0.08 0.10 0.10 0.12 0.11 0.12 0.05 0.10 0.13 0.10 0.10 0.10 0.11 0.10 0.10 0.05 0.13 0.13 0.08 0.09 0.09 0.11 0.10 0.10 0.03 0.15 0.19 0.08 0.08 0.07 0.09 0.05 0.04 0.08 0.26 0.13 0.11 0.09 0.08 0.09 0.10 0.04 0.11 0.74 0.11 0.08 0.07 0.07 0.08 0.04 0.05 0.04 0.51 0.10 0.08 0.12 0.08 0.11 0.10 0.08 0.02 0.55 0.22 0.10 0.09 0.07 0.10 0.07 0.08 0.08 0.20 0.26 0.08 0.08 0.07 0.11 0.03 0.03 0.03 0.15 0.16 0.14 0.10 0.10 0.11 0.04 0.03 0.03 0.11 0.15 0.10 0.13 0.07 0.10 0.08 0.09 0.03 0.39 0.10 0.07 0.07 0.07 0.08 0.05 0.05 0.08 0.41 0.14 0.09 0.07 0.07 0.07 0.08 0.08 0.07 0.70 0.17 0.13 0.08 0.07 0.08 0.08 0.07 0.11 0.64 0.13 0.12 0.06 0.06 0.07 0.07 0.06 0.04 0.71 0.13 0.08 0.05 0.07 0.03 0.03 0.02 0.03 0.85 0.11 0.12 0.20 0.10 0.10 0.08 0.07 0.06 0.45 0.14 0.08 0.06 0.06 0.07 0.08 0.08 0.03 0.11 0.26 0.10 0.07 0.07 0.07 0.07 0.06 0.04 0.11 0.12 0.15 0.05 0.05 0.05 0.05 0.05 0.03 0.08 0.13 0.13 0.10 0.09 0.06 0.05 0.05 0.02 0.12 0.13 0.15 0.12 0.10 0.06 0.05 0.05 0.04 0.09 0.09 0.11 0.10 0.07 0.06 0.06 0.05 0.01 0.11 0.08 0.07 0.13 0.08 0.07 0.07 0.06 0.03 0.05 0.13 0.05 0.06 0.05 0.10 0.06 0.06 0.01 0.05 0.05 0.06 0.05 0.05 0.05 0.05 0.06 0.02 0.04 0.08 0.05 0.05 0.04 0.07 0.05 0.06 0.01 0.04 0.04 0.05 0.05 0.04 0.04 0.04 0.05 0.03 0.05 0.06 0.05 0.04 0.03 0.07 0.04 0.04 0.02 0.11 0.05 0.06 0.05 0.05 0.05 0.05 0.05 0.02 0.06 0.06 0.06 0.05 0.05 0.06 0.06 0.06 0.02 0.05 0.05 0.07 0.05 0.05 0.06 0.06 0.06 0.02 0.06 0.05 0.07 0.06 0.07 0.08 0.06 0.07

Table A13. Surface nitrate, 1997, R2 = 0.13. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.025 0.005 0.005 0.005 0.006 0.006 0.005 0.005 0.005 14.5 0.056 0.005 0.005 0.005 0.006 0.011 0.005 0.005 0.005 21.5 0.041 0.005 0.008 0.008 0.021 0.021 0.005 0.005 0.006 28.5 0.091 0.009 0.012 0.01 0.012 0.014 0.009 0.009 0.009 34.5 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 42.5 0.039 0.005 0.006 0.005 0.017 0.022 0.005 0.008 0.026 49.5 0.005 0.005 0.005 0.005 0.01 0.018 0.005 0.005 0.005 55.5 0.008 0.005 0.017 0.017 0.026 0.03 0.018 0.018 0.019 64.5 0.022 0.007 0.015 0.007 0.011 0.011 0.01 0.005 0.035 69.5 0.005 0.005 0.015 0.023 0.034 0.038 0.005 0.005 0.007 76.5 0.075 0.005 0.006 0.011 0.045 0.056 0.005 0.013 0.008 83.5 0.005 0.005 0.006 0.005 0.026 0.037 0.012 0.005 0.076 92.5 0.33 0.006 0.021 0.076 0.067 0.057 0.044 0.44 0.39 97.5 0.012 0.006 0.32 0.46 0.83 1 1.1 1 1.3 104.5 0.005 0.005 0.086 0.054 0.48 0.53 0.69 0.76 0.77 111.5 0.007 0.005 0.005 0.005 0.005 0.064 0.013 0.092 0.096 118.5 0.016 0.005 0.005 0.005 0.025 0.035 0.063 0.005 0.062 125.5 0.008 0.005 0.005 0.006 0.005 0.007 0.007 0.005 0.005 132.5 0.012 0.005 0.01 0.006 0.017 0.008 0.031 0.021 0.025 139.5 0.018 0.006 0.02 0.037 0.005 0.009 0.007 0.005 0.005 146.5 0.027 0.008 0.016 0.029 0.046 0.038 0.011 0.006 0.005 154.5 0.018 0.01 0.032 0.086 0.093 0.082 0.1 0.13 0.1 160.5 0.024 0.032 0.14 0.26 0.36 0.4 0.5 0.49 0.55 167.5 0.006 0.008 0.12 0.23 0.34 0.43 0.4 0.41 0.35 174.5 0.01 0.005 0.007 0.005 0.075 0.12 0.17 0.18 0.16 181.5 0.008 0.006 0.009 0.011 0.018 0.077 0.11 0.11 0.098 190.5 0.005 0.005 0.022 0.077 0.1 0.13 0.16 0.14 0.18 195.5 0.087 0.005 0.076 0.12 0.16 0.15 0.21 0.32 0.13 202.5 - - 0.029 0.095 0.18 0.17 0.16 0.18 0.15 209.5 - - 0.14 0.21 0.29 0.14 0.34 0.44 0.22 216.5 - - 0.052 0.09 0.15 0.36 0.12 0.007 0.098 223.5 - - - - - - - - - 226.5 - - 0.35 0.35 0.38 0.32 0.33 0.31 0.31 230.5 - - 0.27 0.35 0.33 0.26 0.28 0.27 0.25 237.5 - - 0.19 0.25 0.26 0.2 0.23 0.22 0.21 244.5 - - 0.16 0.21 0.21 0.17 0.17 0.16 0.13 251.5 - - 0.16 0.2 0.21 0.15 0.15 0.17 0.14 258.5 - - 0.18 0.2 0.16 0.16 0.13 0.13 0.12 265.5 - - 0.098 0.17 0.16 0.14 0.12 0.13 0.096 274.5 - - 0.005 0.055 0.14 0.13 0.11 0.073 0.069 279.5 - - 0.009 0.005 0.086 0.11 0.089 0.065 0.054 286.5 - - 0.006 0.014 0.1 0.01 0.11 0.14 0.16 293.5 - - 0.023 0.005 0.005 0.005 0.061 0.057 0.059 302.5 - - 0.005 0.008 0.005 0.011 0.005 0.005 0.007 307.5 - - 0.01 0.005 0.005 0.008 0.005 0.005 0.009 314.5 - - 0.006 0.009 0.005 0.005 0.005 0.005 0.005 321.5 - - 0.014 0.006 0.005 0.008 0.005 0.026 0.055 328.5 - - 0.02 0.009 0.005 0.008 0.005 0.005 0.005 335.5 - - 0.009 0.006 0.014 0.017 0.007 0.006 0.005 342.5 - - 0.013 0.005 0.021 0.005 0.005 0.005 0.005 349.5 - - 0.012 0.005 0.005 0.014 0.005 0.005 0.005 356.5 - - 0.008 0.005 0.035 0.009 0.006 0.011 0.007 363.5 - - 0.017 0.01 0.009 0 0.01 0.005 0.006

0.025 0.005 0.005 0.005 0.006 0.006 0.005 0.005 0.005 0.027 0.121 0.015 0.036 0.067 0.091 0.043 0.020 0.012 0.018 0.069 0.033 0.059 0.108 0.154 0.078 0.057 0.059 0.056 0.099 0.036 0.048 0.158 0.127 0.096 0.078 0.078 0.038 0.053 0.069 0.039 0.086 0.100 0.099 0.092 0.087 0.155 0.099 0.054 0.108 0.146 0.159 0.073 0.057 0.059 0.011 0.067 0.059 0.048 0.102 0.105 0.086 0.056 0.050 0.126 0.194 0.043 0.060 0.134 0.105 0.070 0.043 0.048 0.027 0.060 0.073 0.030 0.058 0.075 0.059 0.049 0.035 0.079 0.056 0.049 0.052 0.097 0.107 0.067 0.045 0.045 0.137 0.140 0.046 0.134 0.117 0.098 0.084 0.042 0.047 0.019 0.070 0.055 0.045 0.089 0.125 0.078 0.028 0.009 0.087 0.057 0.048 0.100 0.124 0.102 0.064 0.041 0.054 0.032 0.125 0.064 0.061 0.096 0.111 0.115 0.117 0.118 0.076 0.113 0.086 0.109 0.122 0.124 0.141 0.150 0.149 0.069 0.329 0.060 0.114 0.161 0.146 0.118 0.112 0.111 0.133 0.084 0.053 0.147 0.135 0.135 0.121 0.102 0.114 0.038 0.071 0.062 0.092 0.161 0.189 0.129 0.113 0.106 0.104 0.072 0.052 0.117 0.117 0.123 0.111 0.093 0.099 0.200 0.205 0.074 0.171 0.197 0.180 0.145 0.121 0.111 0.061 0.090 0.075 0.150 0.154 0.146 0.146 0.102 0.103 0.065 0.098 0.096 0.179 0.184 0.164 0.154 0.127 0.120 0.067 0.126 0.116 0.320 0.179 0.162 0.185 0.164 0.174 0.236 0.221 0.155 0.141 0.172 0.172 0.184 0.176 0.175 0.114 0.153 0.145 0.211 0.186 0.169 0.170 0.121 0.121 0.066 0.125 0.107 0.111 0.178 0.165 0.162 0.151 0.128 0.016 0.114 0.076 0.190 0.163 0.144 0.129 0.103 0.100 0.163 0.116 0.099 0.334 0.190 0.158 0.158 0.147 0.151 0.026 0.062 0.076 0.117 0.144 0.156 0.154 0.156 0.156 0.025 0.089 0.032 0.212 0.180 0.144 0.129 0.122 0.116 0.050 0.083 0.038 0.103 0.184 0.175 0.142 0.153 0.158 0.093 0.110 0.156 0.223 0.272 0.302 0.359 0.396 0.421 0.143 0.214 0.061 0.196 0.325 0.312 0.344 0.339 0.338 0.096 0.084 0.122 0.228 0.238 0.250 0.269 0.276 0.282 0.024 0.094 0.063 0.115 0.203 0.193 0.200 0.188 0.183 0.020 0.081 0.066 0.123 0.170 0.166 0.152 0.153 0.147 0.044 0.093 0.086 0.163 0.184 0.198 0.235 0.257 0.266 0.044 0.165 0.167 0.188 0.239 0.247 0.255 0.262 0.261 0.054 0.247 0.075 0.129 0.182 0.192 0.164 0.162 0.152 0.042 0.202 0.099 0.106 0.205 0.195 0.151 0.140 0.130 0.015 0.154 0.118 0.093 0.131 0.140 0.125 0.124 0.117 0.104 0.111 0.099 0.182 0.195 0.141 0.115 0.111 0.103 0.019 0.195 0.113 0.053 0.097 0.113 0.114 0.111 0.105 0.066 0.068 0.047 0.065 0.129 0.144 0.083 0.067 0.054 0.016 0.046 0.031 0.020 0.066 0.109 0.085 0.057 0.027 0.087 0.187 0.068 0.024 0.087 0.093 0.063 0.022 0.013 0.014 0.261 0.060 0.009 0.038 0.091 0.078 0.057 0.027 0.077 0.068 0.052 0.054 0.086 0.079 0.041 0.038 0.049 0.032 0.134 0.031 0.011 0.047 0.123 0.082 0.060 0.044 0.065 0.142 0.047 0.020 0.088 0.098 0.081 0.070 0.069 0.037 0.145 0.065 0.015 0.077 0.112 0.093 0.088 0.085 0.051 0.101 0.020 0.039 0.069 0.128 0.075 0.067 0.069 0.081 0.135 0.049 0.025 0.053 0.087 0.074 0.057 0.059

Table A14. Near-bed nitrate, 1997, R2 = 0.36. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.005 0.007 0.005 0.005 0.005 0.005 0.005 0.005 0.005 14.5 0.005 0.005 0.005 0.005 0.005 0.005 0.019 0.005 0.02 21.5 0.005 0.005 0.006 0.008 0.006 0.021 0.016 0.005 0.007 28.5 0.01 0.009 0.013 0.011 0.011 0.009 0.012 0.009 0.009 34.5 0.005 0.005 0.005 0.007 0.005 0.005 0.005 0.005 0.005 42.5 0.005 0.006 0.005 0.005 0.005 0.007 0.022 0.017 0.017 49.5 0.005 0.005 0.005 0.007 0.005 0.005 0.014 0.005 0.005 55.5 0.007 0.005 0.008 0.019 0.014 0.015 0.026 0.016 0.01 64.5 0.014 0.013 0.014 0.013 0.014 0.013 0.019 0.012 0.021 69.5 0.005 0.005 0.008 0.02 0.018 0.023 0.033 0.008 0.012 76.5 0.006 0.005 0.007 0.009 0.01 0.014 0.057 0.027 0.022 83.5 0.009 0.005 0.005 0.005 0.005 0.008 0.045 0.01 0.005 92.5 0.016 0.007 0.018 0.044 0.047 0.056 0.064 0.037 0.023 97.5 0.012 0.013 0.02 0.062 0.13 0.13 0.25 0.025 0.34 104.5 0.016 0.012 0.005 0.005 0.063 0.071 0.092 0.085 0.097 111.5 0.014 0.011 0.005 0.005 0.005 0.005 0.005 0.012 0.005 118.5 0.017 0.009 0.005 0.005 0.005 0.01 0.008 0.003 0.053 125.5 0.018 0.005 0.006 0.005 0.005 0.005 0.005 0.005 0.008 132.5 0.012 0.009 0.006 0.016 0.011 0.01 0.025 0.023 0.019 139.5 0.013 0.008 0.01 0.01 0.005 0.006 0.014 0.01 0.005 146.5 0.027 0.012 0.014 0.027 0.013 0.026 0.012 0.007 0.028 154.5 0.02 0.023 0.042 0.061 0.046 0.054 0.041 0.009 0.016 160.5 0.035 0.03 0.042 0.066 0.028 0.082 0.015 0.42 0.4 167.5 0.009 0.022 0.019 0.023 0.047 0.087 0.024 0.31 0.37 174.5 0.027 0.018 0.022 0.005 0.012 0.005 0.05 0.039 0.098 181.5 0.017 0.027 0.017 0.01 0.01 0.005 0.005 0.005 0.005 190.5 0.017 0.017 0.005 0.005 0.005 0.005 0.005 0.005 0.11 195.5 0.019 0.022 0.016 0.013 0.005 0.005 0.005 0.007 0.12 202.5 - - 0.015 0.033 0.005 0.043 0.015 0.073 0.12 209.5 - - 0.14 0.21 0.29 0.048 0.34 0.44 0.22 216.5 - - 0.025 0.031 0.006 0.36 0.12 0.046 0.092 223.5 - - - - - - - - - 226.5 - - 0.025 0.21 0.36 0.32 0.33 0.32 0.31 230.5 - - 0.025 0.35 0.32 0.26 0.28 0.26 0.25 237.5 - - 0.016 0.21 0.26 0.2 0.23 0.22 0.22 244.5 - - 0.056 0.14 0.18 0.17 0.17 0.15 0.13 251.5 - - 0.064 0.17 0.19 0.15 0.15 0.15 0.14 258.5 - - 0.082 0.21 0.15 0.16 0.13 0.12 0.12 265.5 - - 0.23 0.18 0.15 0.14 0.11 0.098 0.097 274.5 - - 0.028 0.064 0.14 0.13 0.084 0.069 0.064 279.5 - - 0.098 0.029 0.097 0.091 0.081 0.07 0.058 286.5 - - 0.091 0.014 0.035 0.008 0.12 0.11 0.11 293.5 - - 0.071 0.019 0.01 0.012 0.061 0.054 0.05 302.5 - - 0.005 0.013 0.006 0.005 0.005 0.006 0.009 307.5 - - 0.008 0.005 0.005 0.005 0.008 0.007 0.009 314.5 - - 0.023 0.005 0.005 0.011 0.005 0.005 0.005 321.5 - - 0.011 0.009 0.007 0.005 0.022 0.006 0.058 328.5 - - 0.011 0.018 0.005 0.013 0.005 0.005 0.007 335.5 - - 0.011 0.009 0.008 0.02 0.009 0.005 0.005 342.5 - - 0.01 0.005 0.023 0.01 0.012 0.005 0.006 349.5 - - 0.007 0.005 0.005 0.008 0.006 0.005 0.005 356.5 - - 0.01 0.03 0.01 0.01 0.013 0.01 0.007 363.5 - - 0.018 0.009 0.007 0 0.006 0.008 0.014

0.005 0.007 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.027 0.029 0.008 0.035 0.063 0.073 0.036 0.020 0.011 0.012 0.024 0.030 0.047 0.102 0.105 0.068 0.057 0.052 0.046 0.042 0.034 0.048 0.109 0.121 0.090 0.078 0.077 0.007 0.029 0.046 0.039 0.076 0.092 0.096 0.092 0.086 0.020 0.055 0.041 0.087 0.131 0.097 0.057 0.056 0.049 0.011 0.037 0.049 0.048 0.083 0.100 0.073 0.056 0.045 0.049 0.039 0.041 0.060 0.107 0.099 0.056 0.041 0.036 0.006 0.038 0.027 0.030 0.056 0.067 0.056 0.049 0.035 0.032 0.031 0.037 0.052 0.088 0.082 0.058 0.045 0.035 0.006 0.033 0.046 0.037 0.078 0.108 0.070 0.075 0.052 0.068 0.032 0.043 0.132 0.093 0.093 0.059 0.041 0.030 0.013 0.047 0.045 0.042 0.085 0.109 0.037 0.028 0.009 0.029 0.041 0.042 0.093 0.095 0.071 0.044 0.040 0.026 0.013 0.045 0.045 0.060 0.090 0.105 0.107 0.111 0.118 0.023 0.047 0.053 0.053 0.104 0.111 0.113 0.139 0.132 0.034 0.047 0.046 0.090 0.155 0.129 0.100 0.112 0.110 0.033 0.058 0.036 0.130 0.122 0.114 0.106 0.094 0.077 0.023 0.054 0.051 0.074 0.141 0.143 0.114 0.097 0.083 0.025 0.036 0.046 0.085 0.106 0.103 0.095 0.090 0.084 0.041 0.049 0.059 0.119 0.147 0.146 0.136 0.118 0.102 0.009 0.080 0.078 0.084 0.169 0.117 0.107 0.091 0.088 0.011 0.074 0.080 0.075 0.159 0.137 0.115 0.105 0.089 0.038 0.055 0.086 0.243 0.163 0.136 0.157 0.163 0.169 0.009 0.078 0.090 0.087 0.168 0.171 0.175 0.175 0.174 0.068 0.075 0.073 0.151 0.149 0.128 0.126 0.121 0.113 0.003 0.062 0.063 0.049 0.148 0.121 0.123 0.111 0.106 0.008 0.043 0.025 0.146 0.127 0.113 0.103 0.103 0.100 0.017 0.058 0.045 0.179 0.177 0.128 0.147 0.147 0.151 0.003 0.012 0.001 0.043 0.137 0.143 0.148 0.156 0.156 0.010 0.006 0.002 0.000 0.148 0.137 0.123 0.122 0.115 0.008 0.038 0.013 0.103 0.183 0.167 0.139 0.153 0.158 0.004 0.031 0.000 0.080 0.254 0.302 0.359 0.396 0.419 0.007 0.042 0.025 0.196 0.325 0.312 0.344 0.339 0.337 0.014 0.043 0.027 0.139 0.238 0.250 0.267 0.276 0.282 0.004 0.068 0.052 0.111 0.203 0.193 0.200 0.188 0.183 0.010 0.068 0.023 0.025 0.166 0.151 0.152 0.153 0.147 0.006 0.034 0.027 0.119 0.184 0.198 0.235 0.257 0.266 0.009 0.059 0.067 0.130 0.239 0.246 0.252 0.262 0.261 0.006 0.049 0.058 0.126 0.181 0.187 0.161 0.162 0.152 0.021 0.068 0.055 0.106 0.204 0.195 0.144 0.140 0.130 0.005 0.053 0.074 0.064 0.131 0.135 0.125 0.124 0.117 0.023 0.065 0.036 0.118 0.177 0.141 0.112 0.111 0.103 0.008 0.043 0.045 0.038 0.076 0.100 0.114 0.111 0.105 0.023 0.047 0.000 0.051 0.112 0.111 0.053 0.067 0.054 0.008 0.000 0.001 0.002 0.048 0.082 0.083 0.057 0.025 0.026 0.019 0.000 0.004 0.070 0.090 0.043 0.019 0.004 0.013 0.010 0.009 0.002 0.029 0.060 0.073 0.057 0.027 0.026 0.012 0.000 0.007 0.042 0.060 0.000 0.038 0.041 0.010 0.023 0.006 0.011 0.037 0.093 0.064 0.060 0.044 0.017 0.046 0.010 0.020 0.087 0.092 0.076 0.070 0.067 0.020 0.000 0.039 0.015 0.076 0.104 0.093 0.088 0.084 0.013 0.009 0.019 0.019 0.055 0.118 0.035 0.067 0.069

Table A15. Surface ammonium, 1997, R2 = 0.12. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.007 0.008 0.013 0.007 0.015 0.016 0.007 0.008 0.006 14.5 0.008 0.007 0.010 0.009 0.006 0.012 0.010 0.009 0.010 21.5 0.007 0.008 0.019 0.021 0.073 0.064 0.010 0.008 0.010 28.5 0.006 0.007 0.016 0.009 0.009 0.016 0.007 0.014 0.008 34.5 0.005 0.005 0.027 0.014 0.005 0.007 0.005 0.005 0.005 42.5 0.009 0.007 0.019 0.020 0.050 0.075 0.015 0.019 0.130 49.5 0.005 0.005 0.026 0.005 0.014 0.120 0.005 0.005 0.005 55.5 0.014 0.010 0.040 0.082 0.190 0.210 0.085 0.091 0.120 64.5 0.005 0.005 0.014 0.005 0.010 0.005 0.005 0.005 0.014 69.5 0.005 0.007 0.021 0.028 0.089 0.170 0.005 0.019 0.005 76.5 0.006 0.005 0.005 0.019 0.094 0.098 0.007 0.018 0.007 83.5 0.007 0.005 0.011 0.006 0.026 0.075 0.013 0.015 0.005 92.5 0.011 0.005 0.022 0.140 0.130 0.089 0.070 0.200 0.170 97.5 0.005 0.005 0.033 0.100 0.360 0.430 0.410 0.420 0.410 104.5 0.006 0.005 0.005 0.006 0.009 0.027 0.230 0.240 0.250 111.5 0.005 0.005 0.005 0.005 0.008 0.017 0.008 0.180 0.220 118.5 0.030 0.005 0.019 0.015 0.140 0.140 0.120 0.006 0.075 125.5 0.005 0.005 0.006 0.005 0.008 0.006 0.005 0.004 0.009 132.5 0.008 0.005 0.009 0.010 0.011 0.005 0.045 0.029 0.040 139.5 0.016 0.006 0.005 0.006 0.005 0.005 0.005 0.005 0.005 146.5 0.026 0.006 0.036 0.110 0.100 0.090 0.008 0.005 0.005 154.5 0.010 0.003 0.028 0.150 0.110 0.076 0.063 0.049 0.047 160.5 0.022 0.005 0.098 0.070 0.080 0.060 0.037 0.036 0.039 167.5 0.006 0.005 0.011 0.028 0.041 0.095 0.068 0.065 0.063 174.5 0.005 0.005 0.009 0.007 0.018 0.016 0.075 0.063 0.066 181.5 0.007 0.005 0.005 0.009 0.008 0.029 0.058 0.053 0.060 190.5 0.008 0.006 0.015 0.037 0.067 0.060 0.053 0.047 0.046 195.5 0.012 0.010 0.055 0.072 0.081 0.075 0.046 0.039 0.034 202.5 - - 0.021 0.057 0.100 0.100 0.071 0.045 0.037 209.5 - - 0.081 0.130 0.130 0.097 0.095 0.066 0.052 216.5 - - 0.038 0.053 0.087 0.067 0.083 0.057 0.040 223.5 - - - - - - - - - 226.5 - - 0.120 0.088 0.088 0.079 0.055 0.054 0.045 230.5 - - 0.081 0.074 0.070 0.074 0.054 0.049 0.039 237.5 - - 0.065 0.071 0.074 0.083 0.060 0.061 0.044 244.5 - - 0.024 0.092 0.088 0.068 0.055 0.056 0.044 251.5 - - 0.089 0.072 0.076 0.074 0.043 0.046 0.041 258.5 - - 0.100 0.093 0.081 0.120 0.063 0.062 0.049 265.5 - - 0.017 0.120 0.130 0.130 0.091 0.086 0.079 274.5 - - 0.079 0.011 0.057 0.054 0.130 0.100 0.090 279.5 - - 0.016 0.009 0.009 0.120 0.120 0.130 0.120 286.5 - - 0.012 0.023 0.110 0.008 0.096 0.088 0.077 293.5 - - 0.024 0.007 0.009 0.005 0.089 0.096 0.095 302.5 - - 0.005 0.017 0.033 0.021 0.005 0.006 0.005 307.5 - - 0.015 0.067 0.008 0.007 0.014 0.013 0.017 314.5 - - 0.008 0.005 0.006 0.005 0.007 0.010 0.008 321.5 - - 0.022 0.008 0.007 0.006 0.009 0.017 0.066 328.5 - - - 0.033 0.005 0.008 0.005 0.005 0.015 335.5 - - 0.018 0.005 0.017 0.025 0.011 0.009 0.008 342.5 - - 0.015 0.008 0.021 0.006 0.008 0.006 0.008 349.5 - - 0.014 0.011 0.006 0.017 0.006 0.005 0.005 356.5 - - 0.017 0.011 0.013 0.031 0.016 0.013 0.014 363.5 - - 0.046 0.011 0.012 0.000 0.009 0.005 0.017

0.007 0.008 0.013 0.007 0.015 0.016 0.007 0.008 0.006 0.031 0.027 0.019 0.038 0.063 0.078 0.030 0.048 0.027 0.012 0.032 0.028 0.043 0.070 0.088 0.055 0.066 0.065 0.035 0.028 0.032 0.043 0.084 0.095 0.078 0.077 0.088 0.008 0.024 0.033 0.032 0.055 0.071 0.070 0.082 0.085 0.019 0.035 0.031 0.049 0.091 0.090 0.037 0.083 0.052 0.012 0.026 0.032 0.035 0.060 0.078 0.067 0.053 0.049 0.039 0.027 0.032 0.041 0.080 0.077 0.043 0.053 0.039 0.006 0.026 0.024 0.027 0.041 0.052 0.042 0.053 0.038 0.032 0.023 0.029 0.037 0.074 0.086 0.049 0.065 0.046 0.039 0.021 0.027 0.054 0.063 0.069 0.048 0.046 0.041 0.012 0.033 0.033 0.024 0.047 0.080 0.020 0.040 0.016 0.026 0.034 0.036 0.050 0.073 0.056 0.026 0.042 0.030 0.010 0.034 0.036 0.044 0.059 0.075 0.066 0.081 0.084 0.020 0.036 0.037 0.035 0.076 0.087 0.070 0.089 0.086 0.023 0.039 0.034 0.049 0.092 0.102 0.070 0.106 0.104 0.032 0.046 0.031 0.057 0.098 0.118 0.093 0.093 0.062 0.019 0.044 0.041 0.045 0.102 0.120 0.085 0.089 0.086 0.020 0.025 0.034 0.059 0.090 0.095 0.079 0.085 0.076 0.031 0.032 0.037 0.064 0.098 0.106 0.089 0.127 0.115 0.019 0.041 0.042 0.103 0.124 0.121 0.117 0.115 0.102 0.009 0.037 0.048 0.050 0.097 0.101 0.091 0.087 0.067 0.024 0.037 0.052 0.102 0.102 0.105 0.090 0.104 0.079 0.006 0.044 0.053 0.043 0.075 0.080 0.062 0.059 0.056 0.017 0.025 0.034 0.061 0.091 0.102 0.093 0.091 0.079 0.003 0.026 0.025 0.018 0.060 0.079 0.064 0.071 0.088 0.007 0.021 0.009 0.050 0.083 0.094 0.079 0.074 0.067 0.017 0.023 0.024 0.082 0.125 0.090 0.074 0.119 0.101 0.002 0.005 0.000 0.016 0.061 0.074 0.066 0.056 0.046 0.006 0.007 0.002 0.000 0.079 0.089 0.050 0.073 0.066 0.004 0.008 0.009 0.021 0.078 0.103 0.064 0.095 0.092 0.002 0.012 0.002 0.029 0.089 0.087 0.079 0.075 0.075 0.002 0.028 0.009 0.078 0.101 0.090 0.085 0.081 0.084 0.004 0.022 0.008 0.038 0.098 0.099 0.093 0.082 0.071 0.003 0.021 0.012 0.039 0.085 0.081 0.066 0.066 0.066 0.005 0.019 0.009 0.023 0.098 0.096 0.101 0.088 0.078 0.004 0.016 0.016 0.048 0.085 0.082 0.078 0.075 0.072 0.005 0.031 0.025 0.028 0.083 0.081 0.076 0.073 0.068 0.003 0.027 0.021 0.049 0.108 0.114 0.107 0.080 0.064 0.011 0.033 0.025 0.042 0.101 0.113 0.105 0.103 0.105 0.003 0.025 0.028 0.042 0.071 0.074 0.091 0.088 0.078 0.016 0.020 0.016 0.051 0.083 0.080 0.070 0.068 0.063 0.003 0.014 0.016 0.015 0.038 0.055 0.079 0.076 0.064 0.019 0.006 0.002 0.024 0.057 0.078 0.033 0.044 0.033 0.004 0.000 0.002 0.002 0.020 0.044 0.058 0.046 0.026 0.021 0.006 0.001 0.004 0.024 0.052 0.016 0.029 0.009 0.008 0.005 0.005 0.003 0.016 0.035 0.046 0.054 0.031 0.024 0.006 0.001 0.008 0.012 0.036 0.001 0.037 0.057 0.008 0.014 0.007 0.013 0.029 0.072 0.066 0.072 0.051 0.012 0.018 0.008 0.017 0.074 0.072 0.039 0.063 0.058 0.018 0.004 0.021 0.015 0.056 0.086 0.068 0.094 0.080 0.009 0.010 0.015 0.018 0.029 0.069 0.020 0.056 0.059 0.018 0.014 0.013 0.011 0.025 0.046 0.057 0.040 0.040

Table A16. Near-bed ammonium, 1997, R2 = 0.06. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.021 0.030 0.011 0.010 0.062 0.097 0.024 0.036 0.011 14.5 0.051 0.019 0.009 0.012 0.550 0.270 0.230 0.240 0.100 21.5 0.011 0.008 0.027 0.027 0.072 0.120 0.097 0.029 0.052 28.5 0.014 0.008 0.018 0.008 0.015 0.009 0.033 0.028 0.017 34.5 0.013 0.016 0.010 0.024 0.007 0.016 0.056 0.018 0.016 42.5 0.011 0.024 0.022 0.041 0.029 0.091 0.110 0.150 0.200 49.5 0.005 0.013 0.037 0.013 0.075 0.039 0.130 0.005 0.013 55.5 0.030 0.061 0.031 0.089 0.180 0.230 0.230 0.190 0.210 64.5 0.015 0.020 0.011 0.018 0.043 0.041 0.064 0.043 0.180 69.5 0.012 0.015 0.015 0.067 0.089 0.120 0.140 0.042 0.063 76.5 0.005 0.005 0.005 0.076 0.110 0.110 0.200 0.140 0.170 83.5 0.017 0.034 0.017 0.008 0.042 0.062 0.120 0.078 0.057 92.5 0.010 0.005 0.012 0.180 0.340 0.320 0.400 0.360 0.200 97.5 0.016 0.036 0.020 0.015 0.220 0.260 0.460 0.600 0.480 104.5 0.044 0.049 0.008 0.360 0.410 0.250 0.470 0.570 0.590 111.5 0.043 0.094 0.010 0.110 0.350 0.310 0.560 0.700 0.630 118.5 0.027 0.009 0.018 0.051 0.250 0.220 0.380 0.830 0.120 125.5 0.013 0.008 0.008 0.018 0.075 0.028 0.029 0.076 0.066 132.5 0.017 0.006 0.010 0.013 0.017 0.012 0.057 0.055 0.064 139.5 0.027 0.018 0.012 0.044 0.014 0.021 0.008 0.036 0.013 146.5 0.028 0.028 0.033 0.120 0.220 0.160 0.011 0.015 0.038 154.5 0.013 0.055 0.035 0.200 0.310 0.260 0.200 0.076 0.051 160.5 0.020 0.027 0.075 0.078 0.430 0.190 0.340 0.069 0.044 167.5 0.020 0.092 0.110 0.043 0.430 0.230 0.810 0.250 0.055 174.5 0.013 0.085 0.130 0.006 0.100 0.019 0.180 0.360 0.210 181.5 0.014 0.083 0.120 0.021 0.039 0.042 0.068 0.070 0.095 190.5 0.033 0.061 0.005 0.073 0.058 0.034 0.085 0.120 0.052 195.5 0.035 0.094 0.098 0.120 0.290 0.140 0.340 2.000 0.035 202.5 - - 0.150 0.053 0.470 0.180 1.400 0.350 0.034 209.5 - - 0.081 0.130 0.130 0.240 0.095 0.066 0.052 216.5 - - 0.050 0.057 0.450 0.071 0.550 0.360 0.200 223.5 - - - - - - - - - 226.5 - - 0.170 0.170 0.065 0.066 0.056 0.050 0.045 230.5 - - 0.290 0.082 0.072 0.078 0.056 0.046 0.038 237.5 - - 0.340 0.100 0.074 0.240 0.062 0.055 0.045 244.5 - - 0.220 0.110 0.400 0.061 0.056 0.063 0.043 251.5 - - 0.230 0.150 0.083 0.074 0.047 0.045 0.040 258.5 - - 0.320 0.093 0.078 0.120 0.066 0.055 0.140 265.5 - - 0.069 0.120 0.130 0.150 0.100 0.093 0.082 274.5 - - 0.120 0.047 0.140 0.081 0.110 0.110 0.094 279.5 - - 0.063 0.071 0.017 0.220 0.140 0.130 0.140 286.5 - - 0.085 0.023 0.490 0.027 0.110 0.110 0.089 293.5 - - 0.029 0.020 0.020 0.021 0.091 0.100 0.097 302.5 - - 0.005 0.066 0.005 0.007 0.010 0.005 0.005 307.5 - - 0.017 0.047 0.007 0.048 0.011 0.018 0.025 314.5 - - 0.089 0.007 0.040 0.014 0.008 0.009 0.008 321.5 - - 0.017 0.026 0.021 0.023 0.024 0.010 0.200 328.5 - - 0.017 0.009 0.043 0.100 0.006 0.006 0.009 335.5 - - 0.026 0.007 0.034 0.012 0.140 0.017 0.057 342.5 - - 0.023 0.018 0.018 0.039 0.008 0.030 0.017 349.5 - - 0.006 0.005 0.013 0.021 0.052 0.017 0.017 356.5 - - 0.017 0.019 0.022 0.012 0.014 0.088 0.036 363.5 - - 0.029 0.008 0.010 0.000 0.011 0.022 0.040

0.021 0.030 0.011 0.010 0.062 0.097 0.024 0.036 0.011 0.031 0.052 0.021 0.038 0.066 0.089 0.105 0.048 0.028 0.018 0.040 0.035 0.049 0.077 0.119 0.084 0.066 0.086 0.050 0.042 0.034 0.043 0.127 0.097 0.081 0.078 0.094 0.039 0.039 0.041 0.032 0.065 0.084 0.084 0.082 0.117 0.061 0.045 0.037 0.051 0.114 0.111 0.076 0.102 0.089 0.012 0.041 0.034 0.035 0.075 0.080 0.072 0.053 0.053 0.055 0.058 0.033 0.041 0.112 0.080 0.052 0.063 0.099 0.028 0.036 0.037 0.027 0.043 0.060 0.071 0.053 0.040 0.052 0.046 0.035 0.037 0.101 0.097 0.059 0.065 0.061 0.010 0.040 0.035 0.029 0.052 0.085 0.179 0.094 0.052 0.070 0.050 0.031 0.055 0.077 0.086 0.063 0.048 0.061 0.018 0.040 0.036 0.033 0.049 0.093 0.141 0.040 0.017 0.053 0.044 0.042 0.051 0.095 0.065 0.040 0.043 0.081 0.037 0.057 0.045 0.045 0.066 0.085 0.129 0.116 0.084 0.052 0.054 0.057 0.081 0.094 0.091 0.091 0.092 0.095 0.063 0.077 0.044 0.069 0.109 0.113 0.138 0.106 0.116 0.046 0.083 0.037 0.104 0.139 0.133 0.119 0.107 0.113 0.038 0.051 0.055 0.051 0.142 0.189 0.163 0.117 0.099 0.058 0.057 0.036 0.079 0.092 0.101 0.094 0.089 0.089 0.078 0.061 0.044 0.082 0.115 0.129 0.171 0.129 0.133 0.036 0.059 0.060 0.082 0.133 0.154 0.112 0.117 0.124 0.030 0.072 0.057 0.088 0.118 0.118 0.117 0.107 0.114 0.038 0.068 0.071 0.113 0.113 0.108 0.109 0.118 0.148 0.078 0.068 0.067 0.079 0.088 0.087 0.096 0.096 0.065 0.068 0.171 0.052 0.077 0.117 0.116 0.123 0.091 0.120 0.028 0.107 0.044 0.062 0.146 0.124 0.088 0.088 0.108 0.010 0.151 0.055 0.087 0.106 0.098 0.089 0.074 0.067 0.075 0.047 0.055 0.105 0.126 0.126 0.129 0.119 0.103 0.011 0.027 0.032 0.056 0.079 0.082 0.083 0.056 0.046 0.021 0.038 0.025 0.077 0.103 0.097 0.097 0.073 0.067 0.042 0.062 0.022 0.021 0.078 0.104 0.104 0.095 0.092 0.051 0.064 0.050 0.091 0.089 0.087 0.079 0.075 0.075 0.065 0.127 0.034 0.078 0.101 0.090 0.085 0.081 0.085 0.071 0.095 0.030 0.095 0.098 0.099 0.098 0.082 0.071 0.025 0.111 0.037 0.041 0.085 0.086 0.066 0.066 0.066 0.025 0.132 0.028 0.067 0.104 0.108 0.101 0.088 0.078 0.040 0.094 0.024 0.086 0.085 0.082 0.078 0.075 0.072 0.028 0.053 0.060 0.077 0.083 0.081 0.083 0.073 0.068 0.018 0.053 0.036 0.062 0.109 0.116 0.126 0.080 0.064 0.022 0.047 0.038 0.043 0.101 0.113 0.109 0.103 0.105 0.012 0.045 0.040 0.044 0.071 0.081 0.091 0.088 0.078 0.039 0.042 0.032 0.061 0.086 0.080 0.074 0.068 0.063 0.015 0.036 0.029 0.019 0.060 0.070 0.082 0.076 0.064 0.049 0.027 0.028 0.028 0.066 0.085 0.071 0.044 0.033 0.013 0.024 0.017 0.018 0.025 0.071 0.063 0.046 0.027 0.050 0.029 0.019 0.014 0.030 0.060 0.096 0.031 0.036 0.010 0.019 0.017 0.009 0.017 0.061 0.074 0.054 0.032 0.048 0.037 0.031 0.011 0.050 0.048 0.040 0.040 0.062 0.031 0.042 0.029 0.013 0.041 0.097 0.112 0.072 0.051 0.047 0.038 0.030 0.017 0.082 0.084 0.069 0.063 0.082 0.037 0.036 0.030 0.016 0.056 0.088 0.082 0.094 0.108 0.051 0.035 0.015 0.036 0.038 0.079 0.088 0.056 0.059

Table A17. Surface total nitrogen, 1997, R2 = 0.15. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.30 0.40 0.40 0.60 0.50 0.63 0.80 1.20 0.90 14.5 0.33 0.25 0.37 0.60 0.77 0.74 0.86 1.10 0.86 21.5 0.20 0.24 0.41 0.54 0.76 0.87 0.88 0.95 0.90 28.5 0.35 0.29 0.44 0.55 0.79 0.76 0.76 0.96 0.85 34.5 0.20 0.20 0.39 0.66 1.40 0.81 0.57 1.30 0.84 42.5 0.24 0.34 0.37 0.54 0.70 0.77 0.84 0.90 0.99 49.5 0.22 0.25 0.39 0.75 0.76 0.84 0.91 1.10 0.80 55.5 0.27 0.27 0.44 0.63 0.84 0.88 0.99 0.96 0.93 64.5 0.18 0.19 0.27 0.56 0.72 0.59 0.82 0.89 0.85 69.5 0.23 0.25 0.43 0.66 0.78 0.80 0.89 0.82 0.97 76.5 0.26 0.26 0.37 0.57 0.98 1.10 1.30 1.10 1.10 83.5 0.20 0.25 0.33 0.57 0.88 0.80 1.00 1.20 1.30 92.5 0.51 0.18 0.42 1.30 0.49 0.67 0.77 1.50 1.70 97.5 0.28 0.27 1.30 1.50 - 2.60 2.80 2.70 3.00 104.5 0.33 0.38 1.40 3.50 2.80 2.30 2.20 2.40 2.30 111.5 0.31 0.39 0.69 0.85 1.40 1.10 1.80 1.40 1.50 118.5 0.23 0.28 0.50 0.84 1.40 1.10 1.20 1.10 1.40 125.5 0.31 0.53 0.55 7.20 7.20 2.00 1.20 1.30 1.40 132.5 0.25 0.31 0.38 0.63 0.78 1.20 0.87 1.00 1.30 139.5 0.20 0.27 0.41 0.64 1.90 0.97 2.10 1.20 1.20 146.5 0.22 0.27 0.42 0.66 0.76 0.95 0.86 0.75 1.10 154.5 0.22 0.27 0.40 0.70 0.77 0.98 0.94 0.68 1.40 160.5 0.22 0.35 0.68 1.40 1.30 1.70 1.20 1.20 1.20 167.5 0.30 0.38 0.60 1.10 1.30 1.20 1.10 1.20 1.10 174.5 0.32 0.37 0.46 1.50 1.60 1.80 1.10 1.10 0.39 181.5 0.30 0.33 0.44 0.57 0.67 0.66 0.70 0.72 0.69 190.5 0.23 0.24 0.41 0.57 1.20 0.60 0.76 0.80 0.77 195.5 0.30 0.25 0.53 0.73 0.84 0.79 1.60 0.90 0.80 202.5 - - 0.43 0.89 0.81 0.84 0.80 0.77 0.78 209.5 - - 0.65 0.77 0.85 0.88 0.89 0.93 0.85 216.5 - - 0.45 0.67 0.81 0.83 1.00 0.62 0.78 223.5 - - - - - - - - - 226.5 - - 1.20 1.20 1.10 1.50 1.10 1.10 1.10 230.5 - - 1.10 1.10 1.20 1.10 1.10 1.10 1.00 237.5 - - 0.84 0.93 0.96 0.89 0.93 0.89 0.83 244.5 - - 0.76 0.94 0.95 0.95 0.83 0.80 0.79 251.5 - - 0.76 0.90 0.95 0.93 0.86 0.92 0.88 258.5 - - 0.97 0.85 0.90 0.89 0.87 0.85 0.87 265.5 - - 0.92 0.98 0.95 0.90 0.86 0.79 0.75 274.5 - - 0.69 0.89 1.10 1.00 0.89 0.80 0.82 279.5 - - 0.57 0.85 0.90 0.92 0.82 0.80 0.71 286.5 - - 0.49 0.63 0.80 0.75 0.69 0.66 0.66 293.5 - - 0.50 0.66 0.78 0.83 - 0.80 0.72 302.5 - - 0.31 0.77 1.00 0.74 0.96 0.98 0.84 307.5 - - 0.37 0.69 1.00 0.63 0.73 0.68 0.65 314.5 - - 0.29 0.47 0.65 0.76 1.10 0.65 0.60 321.5 - - 0.45 0.72 0.71 1.00 0.82 0.75 0.90 328.5 - - 0.25 0.54 0.78 0.64 0.99 0.81 0.59 335.5 - - 0.43 0.72 0.91 1.10 1.10 0.87 0.86 342.5 - - 0.54 0.79 0.89 1.00 1.30 1.10 0.94 349.5 - - 0.31 0.57 0.79 0.96 1.10 0.95 0.86 356.5 - - 0.45 0.67 1.20 1.10 1.30 1.10 0.83 363.5 - - 0.43 0.59 1.40 1.00 0.89 1.30 0.86

0.30 0.40 0.40 0.60 0.50 0.63 0.80 1.20 0.90 0.47 0.39 0.29 0.74 1.05 1.11 0.91 0.85 0.82 0.31 0.42 0.60 0.91 1.17 1.29 1.06 1.02 1.00 0.74 0.66 0.76 1.01 1.28 1.31 1.20 1.11 1.09 0.29 0.56 0.78 0.84 1.09 1.18 1.17 1.11 1.04 0.43 0.69 0.74 1.14 1.33 1.21 1.11 1.00 0.95 0.31 0.56 0.66 0.95 1.14 1.22 1.10 1.01 0.94 0.67 0.54 0.77 0.99 1.28 1.27 1.13 1.05 1.05 0.23 0.61 0.64 0.84 1.06 1.15 1.12 1.07 0.97 0.59 0.54 0.63 0.96 1.19 1.14 1.05 0.96 0.85 0.75 0.47 0.57 1.05 1.21 1.21 1.08 1.03 1.02 0.31 0.59 0.71 0.79 1.09 1.26 1.15 1.05 0.95 0.51 0.64 0.74 1.12 1.20 1.05 0.79 0.77 0.59 0.30 0.64 0.69 0.87 1.07 1.15 1.12 1.07 1.02 0.42 0.63 0.74 0.83 1.16 1.10 0.95 0.86 0.87 0.46 0.63 0.75 0.94 1.19 1.22 1.02 0.97 0.92 0.55 0.69 0.68 1.03 1.15 1.10 0.92 0.81 0.66 0.38 0.65 0.78 0.90 1.24 1.31 0.89 0.74 0.67 0.42 0.51 0.60 0.92 1.04 0.95 0.79 0.72 0.66 0.57 0.56 0.67 1.01 1.27 1.17 1.02 0.97 0.93 0.38 0.58 0.76 1.18 1.17 1.09 0.90 0.86 0.85 0.28 0.64 0.76 0.94 1.23 1.14 0.82 0.74 0.68 0.48 0.57 0.71 1.20 1.10 1.03 0.87 0.87 0.87 0.27 0.69 0.91 0.90 0.99 0.92 0.82 0.80 0.77 0.44 0.60 0.82 1.07 1.12 1.09 0.85 0.80 0.78 0.26 0.81 0.82 0.77 1.13 0.95 0.82 0.74 0.74 0.25 0.82 0.81 1.09 1.08 0.84 0.66 0.65 0.64 0.37 0.71 0.88 1.29 1.20 1.01 0.90 0.88 0.86 0.22 0.51 0.64 0.91 1.08 0.98 0.83 0.77 0.76 0.32 0.65 0.78 0.84 1.21 0.97 0.74 0.71 0.69 0.36 0.71 0.82 1.06 1.19 1.22 0.91 0.85 0.84 0.18 1.03 0.99 1.09 1.27 1.33 1.39 1.44 1.47 0.24 0.90 1.06 1.31 1.39 1.40 1.40 1.40 1.42 0.30 1.08 1.05 1.11 1.23 1.24 1.25 1.25 1.26 0.28 0.82 1.02 1.04 0.97 0.99 0.92 0.92 0.93 0.35 0.89 0.93 0.99 1.14 1.10 0.97 0.92 0.88 0.22 0.88 0.89 0.95 1.07 1.09 1.17 1.23 1.26 0.27 0.74 0.89 0.98 1.15 1.15 1.14 1.13 1.13 0.23 0.62 0.72 0.95 1.01 1.01 0.86 0.83 0.81 0.38 0.72 0.73 0.87 1.12 1.16 0.87 0.82 0.80 0.20 0.57 0.69 0.83 0.98 1.03 0.86 0.76 0.68 0.44 0.73 0.72 1.06 1.11 1.02 0.65 0.65 0.62 0.21 0.52 0.70 0.91 1.05 1.11 0.97 0.84 0.71 0.46 0.78 0.67 1.08 1.20 1.23 0.96 0.78 0.70 0.24 0.57 0.66 0.76 1.04 1.15 1.10 0.98 0.82 0.45 0.65 0.65 0.91 1.13 1.21 1.05 0.93 0.90 0.27 0.42 0.65 0.76 1.03 1.12 1.14 1.06 0.92 0.50 0.57 0.69 0.96 1.18 1.18 1.03 0.94 0.92 0.30 0.62 0.81 0.89 1.19 1.25 1.17 1.10 0.97 0.36 0.65 0.69 0.94 1.22 1.19 1.06 0.95 0.91 0.39 0.37 0.64 0.89 1.19 1.23 1.14 1.05 1.00 0.30 0.73 0.74 0.94 1.12 1.23 0.98 0.92 0.84 0.44 0.56 0.62 0.88 1.01 1.16 1.14 1.06 1.00

Table A18. Near-bed total nitrogen, 1997, R2 = 0.12. 1997 FIELD DATA, BED SITE DAY 1 2 3 4 5 6 7 8 9


7.5 0.10 0.20 0.30 0.70 0.70 0.80 0.50 0.60 0.80 14.5 0.18 0.15 - 0.57 1.20 0.86 0.94 0.90 0.72 21.5 0.14 0.15 0.34 0.63 0.76 0.87 0.86 0.72 0.78 28.5 0.26 0.27 0.37 0.59 0.90 0.68 0.72 0.73 0.72 34.5 0.13 0.21 0.36 0.41 0.78 0.64 0.63 0.62 0.73 42.5 0.20 0.26 0.32 0.57 0.60 0.69 0.83 0.83 0.69 49.5 0.20 0.25 0.38 0.62 0.84 0.71 0.82 0.86 0.78 55.5 0.21 0.34 0.37 0.54 0.86 0.84 1.00 0.86 0.83 64.5 0.11 0.23 0.25 0.40 0.53 0.61 0.74 0.79 0.87 69.5 0.20 0.30 0.34 0.59 0.67 0.78 0.90 0.77 0.85 76.5 0.19 0.24 0.35 0.55 0.60 0.63 0.97 0.96 0.96 83.5 0.03 0.26 0.31 0.53 0.68 0.67 0.88 0.93 0.78 92.5 0.18 0.24 0.37 0.60 0.49 0.66 0.89 0.91 0.63 97.5 0.17 0.24 0.29 0.43 0.85 0.85 1.30 1.20 1.50 104.5 0.18 0.22 0.46 0.90 - 0.85 1.20 1.30 1.40 111.5 0.24 0.33 0.47 0.83 1.10 1.10 1.40 1.40 1.50 118.5 0.21 0.32 0.54 0.71 1.00 1.10 1.30 2.10 1.10 125.5 0.22 0.25 0.32 0.66 0.91 0.68 0.82 1.40 1.10 132.5 0.21 0.26 0.35 0.45 0.64 0.65 0.81 0.84 0.98 139.5 0.16 0.21 0.25 0.56 0.39 0.38 0.54 0.52 0.51 146.5 0.20 0.26 0.33 0.64 0.97 0.87 0.64 0.58 0.66 154.5 0.18 0.30 0.39 0.64 0.77 0.72 0.70 0.63 0.64 160.5 0.28 0.25 0.42 0.55 0.97 0.76 0.92 1.30 1.70 167.5 0.13 0.27 0.38 0.42 0.91 0.68 1.40 1.20 1.10 174.5 0.19 0.29 0.41 0.48 0.63 0.49 0.72 0.88 0.37 181.5 0.15 0.24 0.45 0.46 0.41 0.41 0.48 0.48 0.51 190.5 0.13 0.18 0.27 0.45 0.40 0.35 0.47 0.52 0.78 195.5 0.15 0.26 0.33 0.51 0.57 0.45 0.78 2.80 0.79 202.5 - - 0.40 0.49 0.88 0.57 1.70 3.20 0.74 209.5 - - 0.28 0.55 0.61 0.56 0.77 1.30 0.86 216.5 - - 0.27 0.46 0.80 0.66 1.10 1.10 0.99 223.5 - - - - - - - - - 226.5 - - 0.37 0.98 1.20 1.20 1.10 1.10 1.10 230.5 - - 0.59 1.10 1.10 1.10 1.10 1.00 1.00 237.5 - - 0.59 0.98 0.98 0.94 0.88 0.88 0.85 244.5 - - 0.53 0.80 1.20 1.00 0.82 0.85 0.75 251.5 - - 0.50 0.87 1.10 0.94 0.87 0.88 0.85 258.5 - - 0.92 1.10 0.92 0.93 0.89 0.82 0.82 265.5 - - 0.46 0.88 0.96 0.92 0.86 0.79 0.77 274.5 - - 0.44 0.77 0.95 0.97 0.81 0.84 0.75 279.5 - - 0.41 0.61 0.87 0.87 0.86 0.85 0.74 286.5 - - 0.33 0.92 0.71 0.88 0.68 0.69 0.80 293.5 - - 0.41 0.48 0.88 0.69 0.76 0.80 0.65 302.5 - - 0.35 0.81 1.20 0.90 0.86 0.83 0.72 307.5 - - 0.16 0.46 0.54 0.86 0.72 0.56 0.68 314.5 - - 0.32 0.41 0.76 0.85 0.78 0.87 0.77 321.5 - - 0.35 0.62 1.20 0.72 1.40 0.84 0.83 328.5 - - 0.12 0.32 0.38 0.34 0.78 0.84 0.67 335.5 - - 0.41 0.63 0.65 0.95 1.00 0.82 0.66 342.5 - - 0.38 0.58 1.20 0.97 1.10 0.99 0.92 349.5 - - 0.34 0.51 0.85 0.72 0.86 0.80 0.77 356.5 - - 0.39 0.52 0.77 0.67 1.20 0.95 0.82 363.5 - - 0.45 0.57 1.10 1.00 1.00 1.00 0.93

0.10 0.20 0.30 0.70 0.70 0.80 0.50 0.60 0.80 0.47 0.71 0.35 0.74 1.07 1.15 0.96 0.85 0.83 0.39 0.82 0.62 0.97 1.22 1.34 1.18 1.02 1.03 0.84 0.98 0.77 1.01 1.33 1.35 1.24 1.11 1.10 0.68 0.92 0.91 0.84 1.13 1.21 1.23 1.11 1.05 1.10 0.98 0.84 1.15 1.38 1.41 1.24 1.05 1.10 0.31 0.99 0.77 0.95 1.21 1.23 1.18 1.01 0.99 0.90 0.92 0.81 0.99 1.34 1.30 1.20 1.06 1.08 0.51 0.92 0.87 0.84 1.07 1.18 1.16 1.08 0.98 1.07 0.95 0.78 0.96 1.21 1.26 1.12 0.96 1.01 0.28 0.96 0.79 0.79 1.10 1.23 1.23 1.10 1.01 1.10 0.99 0.78 1.05 1.25 1.24 1.16 1.07 1.09 0.39 0.84 0.76 0.88 1.15 1.30 1.19 1.05 0.95 1.03 0.88 0.82 1.13 1.29 1.24 1.19 0.78 0.99 0.57 1.05 0.92 0.87 1.14 1.21 1.18 1.10 1.02 0.95 0.97 0.97 1.16 1.17 1.29 1.21 0.95 1.04 0.94 1.01 0.84 1.11 1.20 1.29 1.14 0.98 0.95 0.90 0.93 0.77 1.19 1.24 1.26 1.16 0.90 1.02 0.62 0.92 0.83 0.94 1.34 1.33 1.21 0.96 0.87 0.95 0.97 0.65 1.05 1.10 1.11 1.06 0.78 0.87 1.08 0.91 0.82 1.13 1.31 1.33 1.14 0.99 0.99 0.61 0.95 0.88 1.18 1.27 1.28 1.23 0.99 0.95 0.54 1.04 0.89 1.22 1.28 1.32 1.23 0.92 0.94 0.67 0.97 0.88 1.35 1.20 1.17 1.19 0.88 0.87 1.05 1.02 1.01 1.04 1.06 1.08 1.15 1.19 0.86 1.10 0.98 0.97 1.16 1.25 1.23 1.20 0.80 0.82 0.61 0.94 0.95 1.08 1.18 1.21 1.20 1.11 0.91 0.36 1.02 0.88 1.16 1.22 1.13 1.04 0.65 0.64 1.30 1.10 0.90 1.33 1.21 1.19 1.15 0.88 0.86 0.40 0.96 1.02 1.03 1.14 1.19 1.15 0.78 0.76 0.43 1.04 0.93 1.22 1.30 1.22 1.05 0.71 0.69 0.89 0.95 0.98 1.06 1.20 1.25 1.11 0.85 0.84 1.25 1.16 1.07 1.22 1.29 1.33 1.39 1.44 1.48 1.16 1.31 1.18 1.31 1.39 1.40 1.40 1.40 1.42 1.30 1.21 1.14 1.21 1.23 1.24 1.26 1.25 1.26 0.51 1.18 1.06 1.05 0.97 0.99 0.92 0.92 0.93 0.48 1.13 1.00 1.17 1.17 1.12 0.97 0.92 0.88 0.70 1.05 1.03 1.08 1.07 1.09 1.17 1.23 1.26 0.62 1.02 1.03 1.07 1.15 1.16 1.14 1.13 1.13 0.51 1.08 0.94 0.96 1.02 1.02 0.89 0.83 0.81 0.59 1.01 0.89 0.88 1.13 1.16 0.93 0.82 0.80 0.29 0.97 0.93 0.91 0.98 1.06 0.86 0.76 0.68 0.74 0.97 0.91 1.09 1.18 1.02 0.69 0.65 0.62 0.36 1.04 0.92 0.91 1.10 1.13 0.98 0.84 0.71 0.92 1.06 0.81 1.11 1.22 1.28 1.02 0.78 0.70 0.31 0.98 0.87 0.88 1.07 1.24 1.11 0.98 0.83 0.82 1.07 0.87 1.01 1.15 1.30 1.17 0.95 0.95 0.30 1.08 0.81 0.82 1.07 1.22 1.15 1.06 0.93 0.92 1.02 0.92 1.04 1.23 1.31 1.20 0.97 1.00 0.62 1.20 0.92 0.89 1.21 1.28 1.19 1.10 0.97 0.77 1.01 0.85 0.94 1.23 1.26 1.18 0.95 0.92 0.62 0.85 0.81 0.89 1.19 1.24 1.15 1.05 1.01 0.80 0.95 0.74 1.08 1.12 1.30 1.13 0.92 0.84

Appendix IV

231

Table A19. Chlorophyll a concentrations in the upper estuary in 1995 (µg L-1) . MODEL FIELD MODEL FIELD MODEL FIELD MODEL FIELD DAY Marine

Diatoms Marine Diatoms

Freshwater Diatoms

Freshwater Diatoms

Dinoflagellates Dinoflagellates Chlorophytes Chlorophytes

3 11 17 24 31 38 45 52 59 66 73 80 87 94 101 108 116 122 129 136 144 150 164 178 199 208 213 220 227 234 241 248 255 262 283 290 297 304 311 318 325 332 340 346 353 361

36.340 43.698 33.202 28.945 23.129 21.480 17.792 14.666 9.317 6.184 5.847 6.020 7.207 6.179 5.203 3.068 1.778 1.138 0.890 0.629 0.126 0.061 0.007 0.018 0.002 0.001 0.001 0.030 0.002 0.002 0.011 0.041 0.022 0.014 0.087 0.061 0.162 0.141 0.235 0.248 0.277 0.209 0.192 0.235 0.338 1.342

36.340 48.851 4.916 0.882 3.192 5.812 6.357 10.220 1.411 2.171 0.248 2.505 8.589 2.471 4.407 0.105 0.781 1.263 3.725 1.184 1.634 2.216 0.000 0.000 0.000 0.168 0.000 0.000 0.000 0.000 0.000 0.000 0.372 0.032 0.000 0.232 0.105 0.070 0.256 1.382 2.651 1.155 12.940 0.163 1.574 1.978

0.001 0.019 0.024 0.019 0.021 0.024 0.024 0.030 0.023 0.022 0.024 0.019 0.023 0.022 0.016 0.022 0.025 0.023 0.021 0.014 3.646 3.654 2.829 50.741 0.936 0.246 0.294 4.201 3.625 2.262 0.648 0.416 0.392 0.240 0.201 0.183 2.108 1.591 0.510 0.828 0.653 0.475 6.488 3.734 1.640 0.648

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 2.296 29.144 0.877 0.000 0.285 2.088 5.298 1.362 0.335 0.603 0.000 0.189 0.309 0.359 1.691 0.852 0.212 0.989 0.000 8.455 0.000 2.721 0.000 0.912

15.213 20.517 14.714 13.030 9.719 17.143 51.699 41.550 29.152 24.038 19.789 16.663 13.083 12.192 9.172 7.988 7.329 6.067 5.013 3.159 0.907 1.085 0.045 0.034 0.004 0.036 0.004 0.020 0.003 0.003 0.011 0.038 0.020 0.024 0.261 0.408 0.243 0.177 0.384 0.486 0.761 0.581 0.593 0.708 3.933 4.344

15.213 3.809 23.569 45.700 16.478 86.418 139.689 17.161 8.184 3.146 6.202 0.884 5.314 7.000 2.969 24.701 1.392 5.811 2.623 3.177 0.589 1.369 0.033 0.043 0.000 0.006 0.003 0.011 0.000 0.043 0.003 0.011 0.011 0.079 4.395 1.797 0.209 2.184 2.208 2.562 3.779 0.582 4.452 2.073 17.916 15.419

0.187 0.122 0.088 0.080 0.095 0.089 0.379 0.478 0.295 0.154 0.104 0.098 0.075 0.058 0.050 0.072 0.074 0.062 0.060 0.048 0.036 0.061 0.011 0.014 0.054 0.002 0.002 0.022 0.009 0.012 0.030 0.451 0.037 0.042 0.243 1.487 0.923 4.404 9.293 22.851 46.121 67.462 63.186 38.477 11.819 4.345

0.187 0.000 0.158 0.008 0.556 0.428 1.506 1.163 0.227 0.203 0.051 0.347 0.051 0.207 0.052 0.056 0.080 0.175 0.105 0.043 0.010 0.174 0.010 0.043 0.066 0.005 0.005 0.004 0.190 0.016 0.011 0.029 0.005 0.009 0.704 13.427 0.395 8.602 11.079 14.184 1.399 0.009 1.272 0.171 0.000 0.561

R2 0.54 0.89 0.42 0.02

Appendix IV

232

Table A20. Chlorophyll a concentrations in the upper estuary in 1996 (µg L-1). MODEL FIELD MODEL FIELD MODEL FIELD MODEL FIELD DAY Marine


Freshwater Diatoms

Freshwater Diatoms


2 9 16 23 30 37 44 51 56 65 72 76 79 86 93 100 114 121 128 135 142 149 156 163 170 187 205 219 226 234 241 248 254 261 268 275 282 289 296 303 310 317 324 332 338 345 352 358

2.035 0.829 0.423 0.604 1.002 0.778 0.994 0.984 0.544 0.630 0.639 0.723 0.902 0.910 1.277 1.556 2.208 2.363 2.234 1.747 1.531 3.102 7.459 5.037 3.865 0.139 0.003 0.002 0.002 0.024 0.096 0.210 0.200 0.048 0.034 0.316 0.776 3.595 5.362 6.431 12.099 11.888 10.427 4.805 3.265 5.158 5.513 4.084

2.035 1.366 3.892 0.429 0.106 0.327 1.245 0.525 0.175 3.039 0.619 0.335 4.259 0.435 0.947 0.336 0.101 0.473 0.139 0.338 0.131 0.249 0.112 0.054 0.901 63.962 0.000 0.114 3.942 0.000 6.004 0.000 0.000 0.643 0.000 0.000 0.000 0.000 0.326 0.000 0.077 0.025 0.000 0.000 0.058 1.003 0.472 11.073

2.448 0.300 0.097 0.048 0.019 0.020 0.021 0.018 0.016 0.017 0.015 0.015 0.016 0.019 0.016 0.021 0.019 0.019 0.019 0.047 0.031 0.022 0.023 0.027 0.045 0.354 22.355 4.848 8.306 10.916 8.564 4.709 2.791 0.290 0.198 0.707 0.567 0.131 0.089 0.093 0.081 0.403 1.217 0.926 0.196 0.068 0.066 0.190

2.448 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.270 0.070 6.538 11.538 0.000 4.703 0.401 0.000 0.309 5.773 0.542 0.256 0.314 0.956 0.296 3.912 0.726 1.214 1.626 3.540 11.566 8.003

6.598 7.449 11.475 15.936 18.683 25.292 22.035 25.920 34.562 34.132 37.339 33.812 33.930 27.474 23.491 22.448 13.543 12.167 9.344 6.572 4.624 4.106 6.911 3.776 2.650 0.112 0.057 0.044 0.027 0.026 0.040 0.043 0.043 0.028 0.048 0.038 0.052 0.109 0.131 0.164 0.299 0.350 0.336 0.306 0.540 0.894 1.554 1.621

6.598 4.902 9.083 4.173 2.275 4.528 18.504 12.018 0.930 1.304 3.820 3.852 7.957 15.685 14.092 5.062 6.678 6.067 26.904 3.798 1.730 2.403 0.712 2.264 1.045 0.061 0.003 0.000 0.011 0.000 0.003 0.000 0.090 0.008 0.026 0.037 0.022 0.026 3.985 2.509 0.469 2.238 0.791 1.539 2.087 3.299 0.787 9.375

0.361 0.052 0.173 0.376 0.186 0.001 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.030 0.024 0.020 0.036 0.010 0.017 0.060 0.077 0.026 0.031 0.026 0.050 0.096 0.127 0.179 0.283 1.014 7.171 10.736 16.984 4.499 3.025 6.157

0.361 0.028 0.374 0.231 0.027 0.183 0.758 0.068 0.004 0.116 0.117 0.181 1.199 0.459 0.621 0.089 0.209 0.134 0.621 0.857 0.129 0.186 0.111 0.037 0.142 0.046 0.004 0.001 0.010 0.022 0.021 0.015 0.001 0.009 0.018 0.049 0.028 0.179 0.376 2.999 20.769 18.303 1.697 0.573 3.574 1.747 2.139 1.628

R2 0.20 0.06 0.17 0.01

Appendix IV

233

Table A21. Chlorophyll a concentrations in the upper estuary in 1997 (µg L-1). MODEL FIELD MODEL FIELD MODEL FIELD MODEL FIELD DAY Marine


Freshwater Diatoms

Freshwater Diatoms


2 9 16 23 30 36 44 51 66 71 78 85 94 99 106 113 120 127 134 141 148 155 162 169 176 183 192 192 197 204 211 218 225 232 239 246 253 260 267 276 281 288 295 295 309 316 323 330 337 344 351 358 365

8.767 2.616 2.641 3.852 6.252 6.846 9.086 8.721 9.084 13.061 16.125 15.577 11.735 4.773 3.700 3.745 3.279 2.889 2.960 3.459 3.975 2.538 2.693 3.161 2.755 4.131 3.516 3.157 3.710 5.419 3.648 2.152 1.761 1.716 1.145 1.668 0.939 0.926 0.884 0.606 0.452 1.047 2.194 7.305 11.117 13.411 15.387 19.789 12.199 10.659 12.453 13.284 11.953

8.767 2.522 2.022 0.817 7.890 1.324 6.716 1.096 4.418 0.494 3.056 5.824 0.773 0.650 0.556 2.268 0.614 0.326 0.114 0.125 0.082 0.139 2.351 0.517 0.034 0.143 0.701 0.032 0.006 0.185 0.282 1.836 0.417 0.000 0.000 1.439 0.000 0.000 0.000 0.000 1.151 0.492 3.732 0.011 3.061 6.181 7.076 3.394 5.256 9.157 1.889 2.378 1.216

2.280 0.015 0.034 0.047 0.031 0.054 0.037 0.042 0.038 0.026 0.045 0.036 0.035 0.045 0.040 0.042 0.038 0.026 0.017 0.026 0.038 0.025 0.022 0.029 0.023 0.025 0.032 0.062 0.051 0.049 0.057 0.067 0.220 0.127 0.085 0.069 0.125 0.084 0.081 0.078 0.057 0.050 0.042 0.042 0.052 0.047 0.060 0.052 0.057 0.072 0.049 0.039 0.037

2.280 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.032 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.332 0.000 0.000 0.000 0.275 0.068 0.062 0.014 0.000 0.056 0.000 0.204 0.337 0.268 0.156 0.349 0.196 0.492 0.694 0.253 3.894 3.888 7.944 1.344 1.919 0.941 1.292 3.896 0.000 0.000 0.000

5.173 14.000 21.580 20.562 22.535 18.007 20.767 19.894 14.308 14.005 11.480 10.011 7.248 2.846 1.820 2.025 1.796 1.513 1.351 1.305 1.244 1.610 0.803 0.470 0.327 0.436 0.377 0.266 0.193 0.208 0.184 0.255 0.217 0.118 0.122 0.103 0.044 0.061 0.080 0.070 0.064 0.209 0.115 0.188 0.415 0.437 0.490 0.609 0.766 0.957 1.298 1.751 3.155

5.173 16.320 15.211 2.806 2.424 8.275 4.387 3.166 2.395 2.876 2.315 1.546 0.823 1.009 2.175 0.924 3.218 4.536 0.812 2.608 2.124 1.186 1.189 1.086 1.780 1.129 1.522 1.283 0.875 2.248 1.154 2.033 0.011 0.008 0.029 0.398 0.003 0.008 0.016 0.501 2.432 1.728 1.886 14.081 10.564 6.040 9.797 14.807 15.069 27.477 23.294 17.249 24.858

0.121 0.017 0.002 0.001 0.001 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.004 0.025 0.002 0.002 0.032 0.001 0.001 0.001 0.007 0.011 0.004 0.003 0.002 0.005 0.040 0.048 0.049 0.071 0.074 0.012 0.023 0.074 0.113 0.054 0.085 0.187 0.577 1.655 3.776 6.041 2.612 6.706 9.746 3.593 1.295 0.404 0.387 0.002 0.072 0.005

0.121 0.392 3.370 1.037 0.249 0.157 1.921 1.353 0.178 0.667 0.360 0.027 0.006 0.009 0.441 0.122 0.037 0.103 0.026 0.179 0.093 0.020 0.023 0.033 0.006 0.013 0.022 0.031 0.016 0.024 0.018 0.019 0.020 0.019 0.024 0.043 0.013 0.019 0.047 1.160 11.951 0.903 7.362 4.341 0.451 0.376 2.663 0.424 0.058 0.380 0.050 0.267 0.058

R2 0.31 0.03 0.01 0.11

Appendix IV

234

Figure A6. Longitudinal velocities over time in 1997 at the 9 sampling locations for water quality (field velocities were not measured).

phytoplankton dynamics in a seasonal estuaryfreshwater and estuarine species. dinoflagellates have...

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