ASSESSMENT OF RESERVOIR WATER QUALITY USING NUMERICAL MODELS in

LAKE BURRAGORANG AND PROSPECT RESERVOIR IN SYDNEY, AUSTRALIA By Amy Liu Chin Sia 2003 Centre for Water Research University of Western Australia

1 ABSTRACT

Lake Burragorang is Sydney’s most important water source and Prospect Reservoir is Sydney’s emergency water supply. The water quality in both water bodies must meet strict water quality guidelines because they serve as drinking water for over 4 million people. Lake Burragorang’s inflows have a large range of water quality entering from the seven major tributaries. There is poorer water quality in Lake Burragorang during wet years compared to dry years because of pollutant and nutrient loading from the catchment. In contrast, during dry years, internal processes act to purify the reservoir. Prospect Reservoir receives no external inflows and is highly dominated by internal processes. This study utilizes simulations to investigate the implementation of several mitigation methods to improve water quality. DYRESM-CAEDYM, a combination of a 1-D vertical mixing hydrodynamic and an aquatic ecological model served as the simulation framework. The application of bubble plume diffusers and surface mechanical mixers was simulated to evaluate reductions in nutrient concentrations in Prospect Reservoir; and to evaluate algal dynamics. A chemical-based approach was also evaluated to reduce nutrient levels in the water column by surficial sediment treatment. The evaluation of biomanipulation was also simulated for both reservoirs. An adaptive management strategy of selective withdrawal is the best option for abstracting high water quality from Lake Burragorang. For Prospect Reservoir, the mitigation methods were assessed to determine their effects on water quality. This investigation demonstrates the ability of water quality modelling in determining best management practices regarding the optimisation of water quality in drinking water reservoirs.

2 ACKNOWLEDGEMENTS

Heartfelt gratitude to Jose Romero, my supervisor, without whom this thesis would not have taken off the ground. My thanks for his incredible amount of patience and invaluable guidance throughout this entire thesis.

Thank you, Professor Jorg Imberger, for having faith in me and finding me a thesis that I enjoyed worked on.

Special thanks to Mark Nicholls, who has been by my side cheering me on, for his incredible friendship and for helping me in my thesis when I was having difficulties.

Jason Antennucci, Matthew Hipsey and Penny Van Reenen from the CWR for their guide and assistance.

Danny Chan, Ming Zhi Wu, Christina Young, Dina Rahman, Thaddeus Chew and Kenny Lim, special friends and fellow final year students for their selflessness and support.

My mother, who is the sole reason I finished university, for being the incredible woman she is and my sister who sacrificed so much for me.

3 TABLE OF CONTENTS

1 INTRODUCTION...... 1

1.1 LAKE BURRAGORANG ...... 1

1.2 PROSPECT RESERVOIR...... 1 1.3 DYRESM-CAEDYM ...... 2

2 LITERATURE REVIEW ...... 2

2.1 ARTIFICIAL DESTRATIFICATION SYSTEMS ...... 4

2.2 ARTIFICIAL CIRCULATION ...... 6

2.3 SELECTIVE WITHDRAWAL...... 6

2.4 CATCHMENT MANAGEMENT...... 7

2.5 CHEMICAL BASED ALGAL CONTROL METHODS...... 8

2.6 BIOMANIPULATION...... 9

2.7 OTHER METHODS...... 11

3 MODEL VALIDATION ...... 12

3.1 LAKE BURRAGORANG –VALIDATION OF WET YEARS FROM 1998 TO 2000...... 12

3.2 LAKE BURRAGORANG – VALIDATION OF DRY YEARS FROM 2002 - 2003...... 14

3.3 PROSPECT RESERVOIR – VALIDATION OF CURRENT CONDITIONS FROM 2002 – 2004 ...... 16

4 METHODOLOGY...... 18

4.1 INPUT DATA...... 19 4.1.1 Physical data and lake morphometry ...... 19 4.1.2 Inflow data...... 20 4.1.3 Meteorological data ...... 20 4.1.4 Withdrawal data...... 20 4.2 OUTPUT DATA...... 21

4.3 SIMULATION SCENARIOS ...... 22 4.3.1 PR1: 2002-2004 (No inflows) ...... 22 4.3.2 LB1: 1998-2000 (Wet years with floods)...... 22 4.3.3 LB2: 2002-2004 (Dry years without floods) ...... 22 4.3.4 LB3: 1998-2004...... 22 4.4 DESTRATIFICATION SCENARIOS...... 22 4.4.1 Bubble Plume Destratification – PR1 ...... 23 4.4.2 Artificial Mixing by Surface Mechanical Mixers – PR1...... 24 4.4.3 Sediment Treatment by Chemicals – PR1 ...... 26 4.4.4 Biomanipulation – LB3, PR1 ...... 26 4.4.5 Selective Withdrawal – LB1 & LB2...... 27 4.4.6 Catchment Management – LB3...... 31 4.4.7 Evaluation of effect of increased nutrient input into Lake Burragorang – LB3...... 32 4 5 RESULTS AND DISCUSSION – LAKE BURRAGORANG...... 33

5.1 OUTCOMES OF SELECTIVE WITHDRAWAL STRATEGY – LB1 & LB2...... 33

5.2 EVALUATION OF CATCHMENT MANAGEMENT ...... 45

6 RESULTS AND DISCUSSION – PROSPECT RESERVOIR ...... 62

6.1 DESTRATIFICATION BY BUBBLE PLUME AND ARTIFICIAL MIXING...... 62

6.2 SEDIMENT TREATMENT BY CHEMICALS ...... 69 6.3 BIOMANIPULATION IN PROSPECT RESERVOIR ...... 74

7 CONCLUSION...... 78

8 REFERENCES...... 79

5 LIST OF FIGURES

FIGURE _2-1: THE HYSTERISIS RELATION BETWEEN NUTRIENT LEVEL AND EUTROPHICATION MEASURED BY THE

PHYTOPLANKTON CONCENTRATION...... 10

FIGURE _3-1: COMPARISON OF TEMPERATURE PROFILES BETWEEN SIMULATED AND FIELD DATA ...... 13

FIGURE _3-2: COMPARISON OF DISSOLVED OXYGEN PROFILES BETWEEN SIMULATED AND FIELD DATA ...... 13

FIGURE _3-3: COMPARISON OF NITRATE (NO3) CONCENTRATIONS BETWEEN SIMULATED AND FIELD DATA ...... 13 FIGURE _3-4: COMPARISON OF ORGANIC NITROGEN (ON) CONCENTRATIONS BETWEEN SIMULATED AND FIELD

DATA ...... 13

FIGURE _3-5: COMPARISON OF ORGANIC PHOSPHOROUS (ON) CONCENTRATIONS BETWEEN SIMULATED AND FIELD

DATA ...... 13

FIGURE _3-6: COMPARISON OF FILTERABLE REACTIVE PHOSPHOROUS (OR PO4) CONCENTRATIONS BETWEEN SIMULATED AND FIELD DATA ...... 13

FIGURE _3-7: COMPARISON OF TEMPERATURE PROFILES BETWEEN SIMULATED AND FIELD DATA ...... 15

FIGURE _3-8: COMPARISON OF DISSOLVED OXYGEN (DO) CONCENTRATIONS BETWEEN SIMULATED AND FIELD

DATA ...... 15

FIGURE _3-9: COMPARISON OF NITRATE (NO3) CONCENTRATIONS BETWEEN SIMULATED AND FIELD DATA ...... 15 FIGURE _3-10: COMPARISON OF ORGANIC NITROGEN (ON) CONCENTRATIONS BETWEEN SIMULATED AND FIELD

DATA ...... 15

FIGURE _3-11: COMPARISON OF ORGANIC PHOSPHOROUS (OP) CONCENTRATIONS BETWEEN SIMULATED AND

FIELD DATA ...... 15

FIGURE _3-12: COMPARISON OF CHLOROPHYLL-A (CHL-A) CONCENTRATIONS BETWEEN SIMULATED AND FIELD

DATA ...... 15

FIGURE _3-13: COMPARISON OF TEMPERATURE PROFILES BETWEEN SIMULATED AND FIELD DATA ...... 17

FIGURE _3-14: COMPARISON OF DISSOLVED OXYGEN (DO) CONCENTRATIONS BETWEEN SIMULATED AND FIELD

DATA ...... 17

FIGURE _3-15: COMPARISON OF NITRATE (NO3) CONCENTRATIONS BETWEEN SIMULATED AND FIELD DATA ...... 17

FIGURE _3-16: COMPARISON OF AMMONIUM (NH4) CONCENTRATIONS BETWEEN SIMULATED AND FIELD DATA...... 17 FIGURE _3-17: COMPARISON OF ORGANIC PHOSPHOROUS (OP) CONCENTRATIONS BETWEEN SIMULATED AND

FIELD DATA ...... 17

FIGURE _3-18: COMPARISON OF FRP BETWEEN SIMULATED AND FIELD DATA...... 17

FIGURE _4-1: SCHEMATIC DIAGRAM OF THE SURFACE MIXERS, ARROWS INDICATE THE DIRECTION OF FLOW

(TAKEN FROM LEWIS ET AL. 2001) ...... 24

FIGURE _4-2: SIMPLE DIAGRAM DEPICTING OUTLET ELEVATIONS AT THE DAM WALL ...... 29

FIGURE _5-1: OUTCOMES OF TRIAL 2 – SUSPENDED SOLIDS CONCENTRATION FOR (A) LAYER 20 – 50; AND (B)

LAYER 30 - 50...... 34

FIGURE _5-2: OUTCOMES OF TRIAL 2 – ALGAL BIOMASS WITH CHLOROPHYLL-A AS THE INDICATOR FOR (A) L30 –

50; AND (B) L20 - 50...... 35

FIGURE _5-3: INFLOW VOLUME FROM THE 7 TRIBUTARIES OF LAKE BURRAGORANG ...... 35

6 FIGURE _5-4: OUTCOMES OF TRIAL 3 – SUSPENDED SOLIDS CONCENTRATION FOR (A) L5 – 30 AND L40 – 50; AND

(B) L5 - 50 ...... 36

FIGURE _5-5: OUTCOMES OF TRIAL 3 – LABILE PARTICULATE ORGANIC NITROGEN (PONL) CONCENTRATIONS

FOR (A) L5 – 30 AND L40 – 50; AND (B) L5 - 50 ...... 37

FIGURE _5-6: SIMPLE ILLUSTRATION OF THE POSITION OF OUTLETS AND CORRESPONDING LAYERS; AND THE

APPROXIMATE LOCATION OF EPILIMNION AND HYPOLIMNION ...... 40

FIGURE _5-7: THE 1998 – 2000 BASE CASE SELECTIVE WITHDRAWAL STRATEGY...... 42

FIGURE _5-8: THE 1998 – 2000 SIMULATED SELECTIVE WITHDRAWAL STRATEGY ...... 42

FIGURE _5-9: THE 2002 - 2004 BASE CASE SELECTIVE WITHDRAWAL STRATEGY ...... 43

FIGURE _5-10: THE 2002 – 2004 SIMULATED SELECTIVE WITHDRAWAL STRATEGY ...... 43

FIGURE _5-11: TOTAL PHOSPHOROUS (TP) CONCENTRATIONS (MG-P/L) COMPARISON AT THE SURFACE FOR BASE

CASE (0%), 5%, 25%, 50% AND 80% NUTRIENT REDUCTION CASES FOR LAKE BURRAGORANG FROM 1998

TO 2004...... 46

FIGURE _5-12: TOTAL PHOSPHOROUS (TP) CONCENTRATIONS (MG-P/L) COMPARISON AT 70M FROM THE SURFACE

FOR BASE CASE (0%), 5%, 25%, 50% AND 80% NUTRIENT REDUCTION CASES FOR LAKE BURRAGORANG

FROM 1998 TO 2004...... 46

FIGURE _5-13: TOTAL NITROGEN (TN) CONCENTRATIONS (MG-N/L) COMPARISON AT THE SURFACE FOR BASE

CASE (0%), 5%, 25%, 50% AND 80% NUTRIENT REDUCTION CASES FOR LAKE BURRAGORANG FROM 1998

TO 2004...... 47

FIGURE _5-14: TOTAL NITROGEN (TN) CONCENTRATIONS (MG-N/L) COMPARISON AT 70M FROM THE SURFACE

FOR BASE CASE (0%), 5%, 25%, 50% AND 80% NUTRIENT REDUCTION CASES FOR LAKE BURRAGORANG

FROM 1998 TO 2004...... 47

FIGURE _5-15: PERCENTAGE DECREASE OF TOTAL NITROGEN (TN) COMPARED TO BASE CASE FOR 5, 25, 50 AND

80% NUTRIENT REDUCTION CASES (A) AT THE SURFACE; AND (B) 70M FROM THE SURFACE...... 48

FIGURE _5-16: PERCENTAGE DECREASE OF TOTAL PHOSPHOROUS (TP) COMPARED TO BASE CASE FOR 5, 25, 50

AND 80% NUTRIENT REDUCTION CASES (A) AT THE SURFACE; AND (B) 70M FROM THE SURFACE...... 49

FIGURE _5-17: A COMPARISON OF THE PERCENTAGE DECREASE OF TOTAL NITROGEN CONCENTRATIONS AT THE

BOTTOM FOR (A) 5 AND 80% CASE; AND (B) 25 AND 50% CASE...... ERROR! BOOKMARK NOT DEFINED.

FIGURE _5-18: ALGAL CONCENTRATIONS COMPARISON FOR (A) 0 %, 5 % AND 25 % NUTRIENT REDUCTION CASE

AT SURFACE; AND; (B) 0%, 50% AND 80% NUTRIENT REDUCTION CASE AT SURFACE...... 50

FIGURE _5-19: TOTAL PHOSPHOROUS (TP) CONCENTRATIONS FOR (A) 0%, 20% AND 50% NUTRIENT INCREMENT

CASES IN THE EPILIMNION; AND (B) FOR 0%, 20% AND 50% NUTRIENT INCREMENT CASES IN THE

HYPOLIMNION ...... 51

FIGURE _5-20: TOTAL NITROGEN (TN) FOR (A) 0%, 20% AND 50% NUTRIENT INCREMENT FROM CATCHMENT TO

RESERVOIR IN EPILIMNION; AND (B) 0%, 20% AND 50% NUTRIENT INCREMENT FROM CATCHMENT TO

RESERVOIR IN HYPOLIMNION...... 52

FIGURE _5-21: PERCENTAGE INCREASE OF TOTAL PHOSPHOROUS CONCENTRATIONS AT (A) THE SURFACE; AND (B)

AT 70M FROM THE SURFACE FOR 20% AND 50% INCREASED NUTRIENT INPUT...... 52

FIGURE _5-22: PERCENTAGE INCREASE OF TOTAL NITROGEN CONCENTRATIONS AT (A) THE SURFACE; AND (B) AT

70M FROM THE SURFACE FOR 20% AND 50% INCREASED NUTRIENT INPUT...... 53

7 FIGURE _5-23: COMPARISON OF ALGAL CONCENTRATIONS FOR BASE CASE, BIO1 (10% INCREASE IN RESPIRATION

RATE), BIO2 (25% INCREASE IN RESPIRATION RATE) AND BIO3 (50% INCREASE IN RESPIRATION RATE) ...... 55

FIGURE _5-24: COMPARISON OF DISSOLVED INORGANIC NITROGEN (DIN) AND CHLOROPHYLL-A CONCENTRATION

IN THE EPILIMNION FOR THE BASE CASE AND BIO3 CASE ...... 56

FIGURE _5-25: COMPARISON OF (A) NO3 PHYTOPLANKTON UPTAKE; AND (B) NH4 PHYTOPLANKTON UPTAKE FOR BASE CASE AND BIO3 CASE AT THE EPILIMNION...... 57

FIGURE _5-26: COMPARISON OF PO4 AND CHLOROPHYLL-A CONCENTRATION IN THE EPILIMNION FOR THE BASE CASE AND BIO3 CASE...... 58

FIGURE _5-27: COMPARISON OF PO4 PHYTOPLANKTON UPTAKE FOR BASE CASE AND BIO3...... 58 FIGURE _5-28: ALGAL CONCENTRATIONS IN THE LOWER HYPOLIMNION FOR THE BASE CASE, BIO1, BIO2 AND

BIO3 SIMULATIONS...... 59

FIGURE _5-29: COMPARISON OF DISSOLVED INORGANIC NITROGEN (DIN) AND CHLOROPHYLL-A FOR THE BASE

CASE AND THE BIO3 SIMULATION CASE IN THE HYPOLIMNION ...... 60

FIGURE _5-30: COMPARISON OF DISSOLVED INORGANIC PHOSPHOROUS (PO4) AND CHLOROPHYLL-A FOR THE BASE

CASE AND THE BIO3 SIMULATION CASE IN THE HYPOLIMNION ...... 60

FIGURE _6-1: COMPARISON OF DISSOLVED OXYGEN (DO) CONCENTRATIONS AT BOTTOM WATERS, 17M FROM THE

SURFACE FOR THE BASE CASE, BUBBLE PLUME DESTRATIFICATION SCENARIO AND IMPELLER

DESTRATIFICATION SCENARIO...... 63

FIGURE _6-2: COMPARISON OF IRON CONCENTRATION IN HYPOLIMNION FOR BASE CASE, BUBBLER AND IMPELLERS.....64

FIGURE _6-3: COMPARISON OF MANGANESE CONCENTRATION IN HYPOLIMNION FOR BASE CASE, BUBBLER AND

IMPELLERS ...... 64

FIGURE _6-4: COMPARISON OF THE EFFECTS OF DESTRATIFICATION SYSTEMS ON (A) NH4 IN THE HYPOLIMNION;

AND (B) NO3 IN THE HYPOLIMNION ...... 65 FIGURE _6-5: COMPARISON OF DONL FLUX MAGNITUDE AND TREND BEFORE AND AFTER DESTRATIFICATION...... 66

FIGURE _6-6: DO LEVELS FOR BASE CASE AND IMPELLER DESTRATIFICATION CASE ...... 66

FIGURE _6-7: TOTAL NITROGEN (TN) CONCENTRATIONS AT (A) EPILIMNION; AND (B) HYPOLIMNION FOR BASE

CASE, BUBBLE PLUME DESTRATIFICATION (BUBBLER) AND ARTIFICIAL MIXING (IMPELLER)...... 67

FIGURE _6-8: EFFECTIVENESS OF BUBBLE PLUME VERSUS MECHANICAL MIXERS IN REDUCING PEAK NUTRIENT

LEVELS FOR (A) TOTAL PHOSPHOROUS; AND (B) TOTAL NITROGEN...... 67

FIGURE _6-9: COMPARISON OF THE EFFECT OF BUBBLERS AND IMPELLERS ON PO4 CONCENTRATION IN HYPOLIMNION ...... 68

FIGURE _6-10: PO4 CONCENTRATION IN WATER COLUMN AT DEPTH 15M FROM SURFACE FOR (A) BASE CASE AND (B) SEDIMENT TREATMENT SCENARIO...... 69

FIGURE _6-11: PO4 CONCENTRATION IN WATER COLUMN AT SURFACE FOR (A) BASE CASE AND (B) SEDIMENT TREATMENT SCENARIO ...... ERROR! BOOKMARK NOT DEFINED.

FIGURE _6-12: PO4 FLUX FROM SEDIMENTS IN WATER COLUMN AT DEPTH 15M FROM SURFACE FOR (A) BASE CASE AND (B) SEDIMENT TREATMENT SCENARIO...... 70

FIGURE _6-13: PO4 FLUX FROM SEDIMENTS AT SURFACE FOR (A) BASE CASE AND (B) AFTER SEDIMENT TREATMENT ...... 71

FIGURE _6-14: DISSOLVED OXYGEN (DO) LEVELS FOR SEDIMENT TREATMENT SCENARIO AT 0M AND 15M FROM

THE SURFACE ...... 71

8 FIGURE _6-15: COMPARISON OF PO4 CONCENTRATIONS AT THE SURFACE WATERS FOR THE BASE CASE AND THE SEDIMENT TREATMENT SCENARIO...... 72

FIGURE _6-16: COMPARISON OF TOTAL PHOSPHOROUS IN EPILIMNION FOR BASE CASE AND SEDIMENT TREATMENT

SCENARIO...... 73

FIGURE _6-17: TOTAL PHOSPHOROUS (TP) IN THE HYPOLIMNION FOR BASE CASE AND SEDIMENT TREATMENT

SCENARIO...... 73

FIGURE _6-18: COMPARISONS OF CHLOROPHYLL-A CONCENTRATION IN THE EPILIMNION FOR BASE CASE, BIO1,

BIO2 AND BIO3 CASES...... 75

FIGURE _6-19: COMPARISON OF DISSOLVED INORGANIC NITROGEN (DIN) AND ALGAL CONCENTRATIONS IN THE

EPILIMNION FOR THE BASE CASE, BIO1, BIO2 AND BIO3...... 76

FIGURE _6-20: COMPARISON OF PO4 AND ALGAL CONCENTRATIONS IN THE EPILIMNION FOR THE BASE CASE,

BIO1, BIO2 AND BIO3...... 76

FIGURE _6-21: COMPARISON OF NO3 PHYTOPLANKTON UPTAKE BETWEEN (A) BASE CASE AND BIO2; AND (B) BASE CASE AND BIO3...... 77

9 LIST OF TABLES

TABLE _2-1: AUSTRALIAN DRINKING WATER GUIDELINES ...... 3

TABLE _4-1: SCENARIOS AND AMELIORATION METHODS TO BE SIMULATED IN PROSPECT RESERVOIR AND LAKE

BURRAGORANG WHERE COLOURED BOXES REFER TO APPLICATION OF SIMULATIONS: ...... 19

TABLE _4-2: DESCRIPTIONS OF WATER QUALITY PARAMETERS...... 21

TABLE _4-3: ORIGINAL AND ALTERED RESPIRATION RATE COEFFICIENT FOR BIOMANIPULATION STRATEGIES ...... 27

TABLE _4-4: CURRENT WITHDRAWAL STRATEGY FROM LAKE BURRAGORANG AT DAM WALL ...... 28

TABLE _5-1: OVERALL WATER QUALITY RANGES OF EXTRACTED WATER AFTER APPLICATION OF SELECTIVE

WITHDRAWAL STRATEGY FOR BOTH WET AND DRY YEARS (ALL CONCENTRATIONS ARE IN UNITS OF MG/L)...... 43

TABLE _5-2: OVERALL WATER QUALITY RANGES OF EXTRACTED WATER FOR BASE CASE OF WET AND DRY YEARS

(ALL CONCENTRATIONS ARE IN UNITS OF MG/L)...... 45

TABLE _5-3: LONG-TERM AVERAGES FOR TP, TN AND CHLOROPHYLL-A FOR DIFFERENT NUTRIENT REDUCTION

CASES ...... 48

TABLE _5-4: LONG-TERM AVERAGES FOR TP, TN AND CHLOROPHYLL-A FOR DIFFERENT NUTRIENT INPUT

INCREMENT CASES ...... 53 Assessment of Water Quality Using Numerical Models Literature Review

1 INTRODUCTION

The main objective of this thesis is to assess the impacts of applying a variety of amelioration methods to improve water quality in Lake Burragorang and Prospect Reservoir, situated in New South Wales, Australia. This was done by using a validated hydrodynamic-ecological model to simulate the various proposed methods. Another objective is to provide a guideline on simulation methods for future studies on other drinking reservoirs.

1.1 Lake Burragorang

Lake Burragorang, is an impoundment behind Warragamba Dam, and has 7 tributaries (Wollondilly, Coxs, Kedumba, Nattai, Kowmung, Werriberri and Little River) from the Warragamba Catchment with a total area of 9051 km2 (SCA 2002). The reservoir has a total capacity of 2,031,000 ML, surface area of 75 km2, length of 52 km and maximum depth of 105m (SCA 2002). It is the largest supplier of drinking water to Sydney, providing about 80% of the water supply (SCA 2002) to over 4 million people in Sydney. The maintenance of high water quality is of crucial concern because Sydney is highly dependent on reservoirs for their source of drinking water, unlike other cities such as Perth with groundwater resources.

Lake Burragorang is an oligotrophic system with generally good water quality and has monomictic stratification cycles (complete mixing once a year in winter). The Sydney Catchment Authority’s (SCA) Annual Water Quality Monitoring Report 2000-2001 states that the water quality was generally good during dry weather but had elevated levels of nutrients and suspended solids during periods of wet weather. Because Lake Burragorang receives its inflows from the Warragamba Catchment, it is highly susceptible to large variations of inflows caused by changes in precipitation, thus the occurrences of floods during years of high inflow and dry years when there are low inflows.

1.2 Prospect Reservoir

Prospect Reservoir is much smaller compared to Lake Burragorang with a total capacity of 50,000 ML. It has a surface area of about 5.25 x 106 m2 and a maximum depth of about 24 m. Prior to 1996, Prospect Reservoir received inflows from the Warragamba Pipeline (WP) and the Upper Canal (UC). With the commissioning of the Prospect Water Filtration Plant (WFP) in 1996, these inflows were directly piped to the WFP, by-passing Prospect Reservoir (SCA 2002). Currently, Prospect Reservoir receives no external inflows from WP and UC and lake levels are maintained by inflows from the local catchment and precipitation.

1 Assessment of Water Quality Using Numerical Models Literature Review

Prospect Reservoir plays an integral role as an emergency storage or supply facility. For example, in the event of contamination in pipelines, the tainted water can be diverted to Prospect Reservoir instead of the WFP and then distribution system in Sydney. In the case of contamination of Lake Burragorang (e.g. by unsafe levels of Cryptosporidium or Giardia during floods), Prospect Reservoir can act as an emergency supply of drinking water for up to 1 month. The utilization of Prospect Reservoir to supply water in times of high water demand (e.g. when Warragamba dam levels are low or periods of low rainfall) is a third case. The Prospect Reservoir filtration plant can draw water directly from the reservoir, thereby granting flexibility in sourcing water with best water quality (SCA 2001).

The overall objective of water quality improvement is to provide higher quality drinking water with lower pollutant concentrations to ease the processes of water treatment at the water treatment plants.

1.3 DYRESM-CAEDYM

The model used for the simulations of different mitigation methods is a coupled hydrodynamic and ecological model consisting of DYnamic REservoir Simulation Model (DYRESM) and Computational Aquatic Ecosystem DYnamics Model (CAEDYM).

DYRESM is a 1-dimensional vertical mixing hydrodynamic model that is able to predict the vertical profiles of temperature, salinity and density in lakes and reservoirs (CWR 2002). It can be used for simulations extending from weeks to periods and provides a capability to forecast seasonal and inter- annual variation (CWR 2002).

CAEDYM is an aquatic ecological model that consists of a series of mathematical equations representing the major biogeochemical processes influencing water quality.

Together, DYRESM-CAEDYM allows for the investigations into the relationships between physical, biological and chemical variables in water bodies over seasonal and inter-annual timescales.

2 LITERATURE REVIEW

An overview of commonly applied water quality improvement techniques are assessed in this section to determine the most effective strategies that are appropriate for application to both Lake Burragorang and Prospect Reservoir. Physical, chemical and biological methods were considered and their effectiveness evaluated by analysing case studies to determine the advantages and disadvantages of implemented strategies. The studied approaches are artificial destratification systems, adaptive selective withdrawal and catchment management strategies, chemical treatments and biomanipulation.

2 Assessment of Water Quality Using Numerical Models Literature Review

Firstly, an overview of the Australian Drinking Water Guidelines is provided for comparison purposes to appraise the present and simulated water quality parameters as depicted in Table 2-1 followed by the different studied amelioration methods.

Table 2-1: Australian Drinking Water Guidelines

Water Parameter Australian Drinking Water Standard

> 85% saturation based on aesthetic considerations Dissolved Oxygen (DO)
no guidelines for health considerations

Temperature Not more than 20°C pH 6.5 – 8.5

Turbidity < 5 NTU

Total Dissolved Solids < 500 mg/L

Taste & Odour should be acceptable to most people

True Colour < 15 HU

< 50 mg-NO3/L (infants under 3 months old) NO3
< 100 mg-NO3/L (adults and children over 3 months old)

NO2 < 3 mg-NO2/L

NH4 < 0.5 mg/L

concentration of < 0.2 mg/L Al preferably < 0.1 mg/L

guideline concentration at < 0.1 mg/L based on aesthetic consideration Mn guideline concentration at < 0.5 mg/L based on health consideration

Fe < 0.3 mg/L

No set guideline values. Algal Standards (Cyanobacteria) Advisory level for concern at counts over 2000 cells/mL

(Source: Australian Drinking Water Guidelines 1996)

3 Assessment of Water Quality Using Numerical Models Literature Review

2.1 Artificial Destratification Systems

Destratification of reservoirs and lakes can be achieved with pumps, water jets, or air bubbles; but this is usually more effective for shallow water bodies. The main objectives of artificial destratification are to inhibit thermal stratification by promoting vertical mixing (Beutel 2002) and increase bottom water

DO by redistributing photosynthetically produced O2 (Beutel 2002). Burns (1994) stated that artificial destratification can decrease blue-green algae blooms and prevent reducing conditions at sediment- water interface (in small reservoirs and lakes). According to Simmons (1998), it can also influence phytoplankton growth by limiting an environmental condition to promote phytoplankton competitive stress, thus resulting in lower overall annual yield of algal biomass.

There are many advantages to artificial destratification. These include increased DO levels in the hypolimnion to enhance conditions for many aquatic organisms and to improve decomposition of organic matter. Bowersox (2000) lists several benefits of increased DO levels which include the prevention of fish kills due to anoxic conditions, reduced release of nutrients from anaerobic sediments, encouragement of more favourable species of algae, and reduction of iron, sulphur, and manganese compounds. The destratification process also acts to reduce the relative abundance of blue- green algal species by mixing as high energy mixing conditions are unfavourable to buoyant blue- green algal species (Fast et al. 1976). Fast et al. (1976) continues to express that hypolimnetic aeration may encourage the growth of herbivorous zooplankton, which increases the rate of grazing. However, there are cases that have shown that zooplankton densities are not greatly increased and algal populations were not much affected by hypolimnetic aeration (Fast et al. 1976).

Some disadvantages of artificial destratification will be the increased summer temperatures in bottom waters due to destratification, which tends to degrade cold-water fishery habitat as the warm discharges of water from the destratified water columns may impair downstream biota (Beutel 2002). A homogenized water column in drinking water reservoirs proves to be unsuitable for selective depth withdrawal aiming at optimal raw water quality (Beutel 2002 and Fast et al. 1976). In addition, the costs of bubbler diffusers including the construction of bubblers (line diffusers, air distribution piping system, air blowers and a blower building), and the operation and maintenance costs can be quite high (Bowersox 2000).

There have been a number of studies that have looked into the effectiveness of different strategies in breaking up the stratification of water bodies. The following section reviews these strategies to build an understanding of which methods have been found to be successful in other reservoirs, so that simulations of these methods can be run for Lake Burragorang and Prospect Reservoir.

4 Assessment of Water Quality Using Numerical Models Literature Review

One such study was performed on NT2, a small reservoir in Taiwan. Two simulated experiments were conducted using bubble plume systems to mix the water column (Romero et al. 2000). The first experiment pumped high airflow rates through the water column while the second used lower airflow rates. The first simulation was found to be more effective in destratifying the water column than the low airflow (Romero et al. 2000). The increased effectiveness was due to the fact that the higher airflow rate led to shorter periods of time when the thermal stratification persisted so that aeration of the water column could take place. This meant release of PO4 and NH4 from sediments during anoxic conditions associated with stratification were reduced (Romero et al. 2000). However, the simulated high airflow bubble plume had the disadvantage of increasing the temperature of the waters in the hypolimnion because of the efficient mixing (Romero et al. 2000). The low rate bubble diffusers were less effective because they were found to have little effect on diatoms and blue-green algae concentrations. The same problem of increased hypolimnion temperatures was also found to have occurred with the slow rate diffusers (Romero et al. 2000). However the low rate diffusers had the advantage of simulating slightly decreased deep water concentrations of PO4 and NH4.

The Hanningfield Reservoir is another example of a study that looked into the effects of destratification on water quality. An intermittent destratification strategy was found to improve water quality by limiting the thermal stratification periods in summer so that the overall biomass of phytoplankton was decreased (Simmons 1998). This led to lower overall annual algal biomass yields (Simmons 1998). The problem with this study was that these conclusions were drawn from only 3 years of data. At least 5 more years of data would be required to confirm the effects of the artificial destratification (Simmons 1998).

Bubble plumes were trialled in the Waco Reservoir in Texas in 1970. The reservoir has an average depth of 11 metres and provided 6.57x108 m3 of water storage (Bowersox 2000). The bubble plumes were implemented because of algal, taste and odour problems (Bowersox 2000). Following the implementation of the bubble plume strategy, algal blooms were a rare occurrence and the taste and odour problems became milder and lasted for shorter periods of time. The air bubbling was also found to have no effect on pH, alkalinity, hardness and chloride characteristics of the reservoir water but had a positive effect on the diffused oxygen concentrations (Bowersox 2000).

In 1982, Lake Eureka in Illinois employed aerators to destratify the lake. Copper sulphate was also added to target problems with algae (Bowersox 2000). The aerator was found to have completely destratified the lake while maintaining the dissolved oxygen concentration. The blue-green algae problems were also fixed, and there were no more complaints about the taste or smell of the water.

Another example of bubbler-induced mixing is in Lake Nepean, Australia. Following the implementation of bubble plumes the deep-water temperatures became elevated above historical values and the heat storage in the lake was increased (Schladow and Fischer 1995). The mixing of the

5 Assessment of Water Quality Using Numerical Models Literature Review water column breaks thermal stratification rapidly over spring and summer so that the turnover time is brought forward by 2.5 months (Schladow and Fischer 1995). It was found that it made little difference to the ultimate thermal state of the lake whether destratification was started early in the stratifying season and used intermittently as required, or used later in the season when the maximum thermal gradient had been established (Schladow and Fischer 1995).

2.2 Artificial circulation

While destratification and hypolimnetic aeration is accomplished by the injection of air and oxygen, artificial circulation is achieved by injecting surface waters to deeper waters to encourage circulation. Surface mixers are an example of artificial circulation, which can be used to complement existing aerators for destratifying water column to control algal growth (Lewis et al. 2001). Similar to destratification systems, artificial circulation aims to weaken the thermal stratification (Lewis et al. 2001) or reducing blue-green algal growth by disrupting the ability of algae to maintain favourable position in water column (Bowersox 2000).

Among the advantages of the mechanical mixers over bubble destratification systems are the range and flexibility of available mixers plus the economic savings (Lewis et al. 2001).

Disadvantages of artificial circulation include the mixing of bottom nutrient-rich waters during periods of active photosynthesis and the increase in temperature of whole water body, which may benefit blue- green algae (as they prefer higher temperatures) (Bowersox 2000). Another disadvantage is the size of mechanical mixers varies and hence, the cost for installation and operation which depends on the amount of water to be circulated. For example, reservoirs like Myponga and Happy Valley Reservoir requires physically large surface mixers (Lewis et al. 2001).

Simulations of mechanical mixers in the Myponga Reservoir found that artificial mixers were successful in weakening thermocline and dramatically decreasing the temperature differences between the surface and the hypolimnion. However, case studies of the lakes of the English Lake District found that there was little change in the algal biomass in oligotrophic lakes despite the application of artificial circulation strategies.

2.3 Selective Withdrawal

Hypolimnetic withdrawal is aimed at removing low quality water from the hypolimnion (for treatment or for release downstream), hence retaining epilimnetic water, which is rich in dissolved oxygen (DO). Nurnberg (1987) states that the main objective of hypolimnetic withdrawal is to reduce anoxic

6 Assessment of Water Quality Using Numerical Models Literature Review conditions in lakes, thus limiting release of phosphorus from sediments. Consequently, there will be a reduction of nutrient cycling from the hypolimnion to the epilimnion, which can encourage blooms of blue-green algae causing taste and odour problems (Bowersox 2000). Anoxic conditions can also lead to the accumulation of iron, manganese and sulphides in the hypolimnion (Beutel 2002).

Another management aim with selective withdrawal is to extract high quality water from the reservoir for domestic purposes. Often the epilimnion of reservoirs and lakes have deleterious algal blooms, whereas bottom waters near the sediments may have high suspended solids and low dissolved oxygen. Under these conditions selective withdrawal of mid-depth water often provides optimal quality water from the reservoir.

Romero et al. (2000) details the outcomes of simulations from the application of selective withdrawal strategy to Lake Pamvotis in Greece. Selective withdrawal of poor quality water in the hypolimnion resulted in weaker and shorter durations of thermal stratification (Romero et al. 2000). The extent and duration of anoxia were successfully reduced in initial years and subsequent years saw eliminated anoxic conditions (Romero et al. 2000). There was an overall lack of high bottom water concentrations of dissolved inorganic nutrients like PO4 and NH4 but little effect on other water quality parameters (Romero et al. 2000). Among the disadvantages found from the simulations were that there was no change in the predicted algal biomass and that the success of the strategy depended greatly on very high extraction volumes from bottom waters (Romero et al. 2000). Hence, the current configuration of selective withdrawal was not found to be a practical solution (Romero et al. 2000). Bowersox (2000) also deduced that the selective withdrawal strategy of poor quality water was not likely to be successful in cases of water bodies with inconsistent strong stratification.

2.4 Catchment management

Catchment management to reduce the amount of nutrients entering water bodies is becoming more important in preventing eutrophication. It is not acceptable to do nothing to prevent the degradation of lakes and reservoirs as without further action, there would be reduced natural resources and this will impact greatly on the water supply in Sydney.

Madgwick (1999) believes that the reduction of external and internal nutrient loading is one of the essential steps in restoring and maintaining pristine water bodies.

7 Assessment of Water Quality Using Numerical Models Literature Review 2.5 Chemical based algal control methods

Chemical based methods can be used for a range of purposes. Among the most common applications is the use for algal control, flocculation and sediment treatment.

Copper sulphate is a popular algaecide, which is generally applied to water surface at a dosage of 0.25 to 0.5 mg/L (Kadlec and Knight 1996 as cited in Bowersox 2000). Copper remains soluble at low pH and is as an effective algaecide but as pH increases, copper chelates and becomes insoluble (Kadlec and Knight 1996 as cited in Bowersox 2000). This management practice is temporary and requires re- application throughout the period of poor water quality caused by algae. Algae can also build up algaecide tolerance, thus requiring higher dosages and more frequent applications to be effective on a long-term basis in a particular reservoir.

Copper is an unfavourable algaecide due to negative impacts on non-target organisms and the environment (Cooke 1993 as cited in Bowersox 2000). Levels of 0.14 mg/L of copper sulphate will kill trout and 0.33 mg/L will kill carp (Meadows 1987), which illustrates that careful application must occur to avoid overdose. More recently, the control of cyanobacterial blooms by CuSO4 is being eliminated from lake management practices because of the copper ion toxicity for many hydrobionts and the possibility of intensive release of toxins by dead cyanobacteria cells (Wisniewski 1999).

Application of copper sulphate to Lake Waco in Texas and Lake Hennessy in California, successfully reduced algal populations which were causing taste and odour problems (Bowersox 2000). The application of this algaecide to the Metropolitan Water District in Southern California resulted in the elimination of blue-green algae within 5 days and reported no fish kills (Bowersox 2000). However, algaecides may be an unfavourable method for the removal of algae because of the designated use of water for domestic purposes.

Aluminium sulphate (alum) is another chemical normally used for flocculation. It is usually applied to surface waters in granular form to control the internal loading of phosphorus (release of phosphorus from sediments) (Bowersox 2000). Upon application, flocs form within the water column and settle to the bottom sediments. While these flocs settle, inorganic and organic phosphorus adsorb to these flocs, hence the treatment strips phosphorus from the water column. Low temperatures decrease the rate of flocculation and deposition and increase the chances of toxic aluminium species forming. The pH also controls the effectiveness of alum applications. For example, soluble aluminium species may dominate when alum is added to poorly buffered waters (HDR 2001 as cited in Bowersox 2000).

Sediment treatment can be achieved by the application of iron chloride (FeCl3), which acts to control and reduce the release of phosphate from sediments (Wisniewski 1999). The iron chloride application to Lake Gutowo and Lake Lasinskie led to a seven-fold decrease in phosphate concentration in interstitial water and only slight changes in physical and chemical parameters unless applied in high

8 Assessment of Water Quality Using Numerical Models Literature Review concentrations (Wisniewski 1999). Randall, Harper & Brierley (1999) also state that soluble iron is unlikely to have direct toxic effects on plankton or fish if applied below safe levels.

In general, the use of chemical treatment methods are not favoured because of possible toxicity effects and strict regulations against high levels of residuals in drinking water. Even though some chemicals may have no immediate negative effect on lakes and its biota, the risk of accumulation of these chemicals in bottom sediments needs to be considered (Sigee et al. 1999).

2.6 Biomanipulation

Biomanipulation is another mechanism for controlling algae dominance by influencing the biota within a system (Bowersox 2000) or food web manipulation (Romero et al. 2002). The main objective for biomanipulation in lakes and reservoirs that are used for drinking water is to induce a state with low nutrients and turbidity. In such conditions, it is usual to be able to observe high light availability, large biomass of herbivorous zooplankton, existence of submerged macrophytes, and a high piscivore to planktivore-benthivore fish biomass ratio (Romero et al. 2002).

Trophic cascade

The trophic cascade hypothesis states that a perturbation to one trophic level will impact other trophic levels, thus cascading through the food web (Bergman, Hansson & Andersson 1999). For example, if there is an increase in piscivore biomass, there will be a decrease in planktivore biomass, thus resulting in an increase in herbivore biomass and a subsequent decrease in phytoplankton biomass (Bergman, Hansson & Andersson 1999).

Bottom up versus top down theory

“Bottom-up” refers to resource availability or ‘from the bottom of the food chain upwards’ while “top- down” focuses on predator influences or ‘from the top of the food chain downwards’ (Bergman, Hansson & Andersson 1999). This theory predicts that at high nutrient availability, the system will be controlled by bottom-up forces and hence, fish will have no influence on algae (i.e. top-down control has little influence in eutrophic waters but can influence in oligotrophic waters or fish control will have no effect in eutrophic waters but can be important for algal control in oligotrophic waters) (Bergman, Hansson & Andersson 1999).

Possible approaches to implementation of biomanipulation in reservoirs includes:

9 Assessment of Water Quality Using Numerical Models Literature Review

The management of fisheries to promote a favourable algal species composition or creating a favourable environment for grazing (versus predatory) zooplanktons. An example of biomanipulation is the enhancement of piscivorous fish that may reduce the number of zooplanktivores, thus increasing the concentration of zooplanktons (Bowersox 2000).

Elimination of bottom feeders that tend to stir up bottom sediments and cause resuspension of nutrients (Bowersox 2000).

Biomanipulation is a favoured management strategy because there is no direct chemical pollution to harm biota or humans (Sigee et al. 1999). Furthermore, biological control of cyanobacteria can be highly specific to the target organism without destroying other organisms (Sigee et al. 1999).

Because biomanipulation is a relatively new management strategy, there are many complexities involved. While the food chain theory (i.e. trophic cascade hypothesis) is important, other processes such as the dynamics in littoral and benthic zones should be considered, which introduces a complex spatial component (Bergman, Hansson & Andersson 1999). For the biological control of cyanobacteria, Sigee et al. 1999 outline the following potential problems with biomanipulation:

i. Limited destruction of target organisms

ii. Limited survival of biological agent

iii. Removal of biological agent by other organisms

iv. Difficulties of producing, storing and applying biological agent on a large-scale

Figure 2-1: The hysterisis relation between nutrient level and eutrophication measured by the phytoplankton concentration

10 Assessment of Water Quality Using Numerical Models Literature Review

Figure 2-1 can be used as a guide to determine whether biomanipulation is an efficient method for improving water quality. There is a range over which biomanipulation will be an effective remediation strategy depending on the extent of eutrophication and the nutrient concentration in water bodies. As indicated in the diagram, an effect of biomanipulation can hardly be expected if nutrient concentrations were above a certain level (Jorgensen & Bernardi 1998).

2.7 Other methods

There are many other methods not included in this study. For example, these include:

1. Sediment Removal: Removal of top 30 – 50 cm of sediments to eliminate a significant volume of labile nutrients from the system. Careful consideration needs to be taken to prevent nutrient release into the water column during the process of sediment removal and dredged sediments need to be disposed of properly.

2. Algae harvesting to remove nutrients and algae from water bodies. This method usually requires high accumulation of algae biomass within a water basin.

3. Dilution and flushing acts to reduce water temperature and concentration of limiting nutrient and increases rate of exchange of water. Flushing rates must be at 10 to 20 % of reservoir volume daily to coincide with algal growth rates. Dilution water must have nutrient concentrations that are low enough to limit algal growth. However, there is high cost associated with this method besides the need for readily available water supply and uncertainty on the level of control this method has on algae (Bowersox 2000).

Chemical-based nutrient control methods are also possible, such as:

1. Sediment oxidation reduces the chances of reduction at sediment-water interface, which may release phosphorus back into the water column. Ferric chloride is injected into the top 15 to 20 cm of sediment, forming iron hydroxide, which absorbs phosphorus and holds it in the sediments. Calcium nitrate can encourage the breakdown of organic material and denitrification processes. Nitrogen has the potential to be drawn into sediments because nitrogen is the preferred electron acceptor within a liquid state. This method can be effective in a system that controls internal loading by iron redox reactions but is ineffective if the system is controlled by pH and temperature (D’Angelo and Reddy 1993 as cited in Bowersox 2000). Sediment treatments can only temporarily reduce nutrient concentrations in targeted area because of high external loading and may be effective for only a few days (Bowersox 2000).

2. Phosphorus stripping clay: modified bentonite clay has the ability to remove filterable reactive phosphorus (FRP) from water column (Douglas et al. 1999). 11 Assessment of Water Quality Using Numerical Models Model Validation

3 MODEL VALIDATION

As mentioned, the model used for simulating and evaluating the studied amelioration methods is DYRESM-CAEDYM. The second stage of this study was to validate the ability of the model to reproduce field observations. The following field data were provided courtesy of Jose Romero from the Contract Research Group in the Centre for Water Research in the University of Western Australia.

The main focus of this study was to assess the performance of the proposed strategies for Lake Burragorang and Prospect Reservoir under different scenarios. As mentioned previously, Lake Burragorang is subject to varying inflow volumes and experiences flood years and dry years. Simulations for the studied strategies were validated for wet years of 1998 to 2000 and dry years of 2002 to 2003.

3.1 Lake Burragorang –Validation of wet years from 1998 to 2000

The following plots are simulations of physical, biological and nutrient profiles at the surface and at the bottom layer. The temperature and dissolved oxygen ranges and seasonality were reproduced well as seen in the comparison plots. The comparison of nitrates (NO3) indicated that the model was successful in predicting the peaks and declines of NO3 as well as the ranges of the concentrations. The simulation for organic nitrogen (ON) was also quite accurate with the model being close to predicting the trends of increase and decrease. Observations for filterable reactive phosphorous (FRP), used as an indicator of phosphates (PO4), found that the model over-predicts in the winter months. This is because the water quality parameter constants file used for the simulations were the same for both Lake Burragorang and Prospect Reservoir and had to be adjusted to be able to model Prospect Reservoir conditions as well. However, the differences between the field data and simulated data are small. The plots for organic phosphorous (OP) also tended to over predict the concentrations of OP. For the same reason as before, this is due to need to balance between the parameters for the two reservoirs. In conclusion, the model is able to simulate the wet years for Lake Burragorang confidently.

12 Assessment of Water Quality Using Numerical Models Model Validation

Temperature Dissolved Oxygen Concentration

30 14 28 12 26 24 10 22 8 20 6 18 DO (mg/L) 16 Temperature (deg C) 4 14 12 2

10 0 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug-99 19-Feb-00 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug-99 19-Feb-00 Sim T at surface (0m) Sim T at bottom (80m) Sim DO at surface (0m) Sim DO at bottom (60m) Field T at surface (0m) Field T at bottom (80m) Field DO at surface (0m) Field DO at bottom (60m)

Figure 3-1: Comparison of temperature Figure 3-2: Comparison of dissolved oxygen profiles between simulated and field data profiles between simulated and field data

Nitrates (NO3) Organic Nitrogen Concentrations

0.6 1 0.9 0.5 0.8 0.7 0.4 0.6

0.3 0.5

N (mg-N/L) 0.4 NO3 (mg-N/L) 0.2 0.3 0.2 0.1 0.1

0 0 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug-99 19-Feb-00 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug-99 19-Feb-00 Sim NO3 at surface (0m) Sim NO3 at bottom (78m) Sim ON at surface (0m) Sim ONat bottom (78m) Field NO3 at surface (0m) Field NO3 at bottom (78m) Field ON at surface (0m) Field ON at bottom (78m)

Figure 3-3: Comparison of nitrate (NO3) Figure 3-4: Comparison of organic nitrogen concentrations between simulated and field (ON) concentrations between simulated and data field data

Organic Phosphorous Concentrations Dissolved Filterable Reactive Phosphorus Concentrations 0.08 0.1 0.07 0.09 0.06 0.08 0.07 0.05 0.06 0.04 0.05

0.03 0.04 0.03 Organic Phosphorous 0.02 Phosphorous (mg-P/L) 0.02 0.01 0.01

0.00 0 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug-99 19-Feb-00 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug-99 19-Feb-00 Sim OP at surface (0m) Sim OP at bottom (78m) Sim FRP (0m) Field FRP (0m) Sim FRP (78m) Field FRP (78m) Field OP at surface (0m) Field OP at bottom (78m)

Figure 3-5: Comparison of organic Figure 3-6: Comparison of filterable reactive phosphorous (ON) concentrations between phosphorous (or PO4) concentrations between simulated and field data simulated and field data

13 Assessment of Water Quality Using Numerical Models Model Validation

3.2 Lake Burragorang – Validation of dry years from 2002 - 2003

The temperature and dissolved oxygen concentrations were represented very well by the model with accurate predictions of the DO seasonality and thermal variations. The model was capable of reproducing these field observations to an accurate range and portrays the variations of temperature and DO concentrations well.

As for the NO3 plots, the model under predicts slightly for the bottom concentrations of NO3 but manages to capture the trend and ranges for surface simulations of NO3. The ON simulation was also within the range but did not capture the trends very successfully.

The plots for OP showed a peak of OP in the simulations that was not found in the field observations. Once again, this is due to the need to adjust water quality parameters to simulate for both reservoirs accurately.

The chlorophyll-a comparison indicate that the model manages to capture the trend in bottom waters but did not capture the peaks in surface waters although the difference in range was not very significant.

As an overall, the model simulates the dry years well although the nitrogen and chlorophyll-a parameters need to be further investigated.

14 Assessment of Water Quality Using Numerical Models Model Validation

Temperature Comparison Dissolved Organic Concentration 14 26 12 24

22 10

20 8 18 6 16 4 Temperature (deg C)

14 Dissolved Oxygen (mg/L)

12 2

10 0 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 21-Feb-02 22-Apr-02 21-Jun-02 20-Aug-02 19-Oct-02 Sim T at surface (0m) Sim T at bottom (75m) Sim DO at surface (0m) Sim DO at bottom (75m) Field T at surface (0m) Field T at bottom (75m) Field DO at surface (0m) Field DO at bottom (75m)

Figure 3-7: Comparison of temperature Figure 3-8: Comparison of dissolved oxygen profiles between simulated and field data (DO) concentrations between simulated and field data

NO3 Concentrations Organic Nitrogen Concentrations 0.18

0.16 0.28

0.14 0.26 0.24 0.12 0.22 0.1 0.2 0.08 0.18 NO3 (mg-N/L) 0.06 0.16 0.04 0.14 Organic Nitrogen (mg-N/L) 0.02 0.12 0 0.1 21-Feb-02 22-Apr-02 21-Jun-02 20-Aug-02 19-Oct-02 21-Feb-02 22-Apr-02 21-Jun-02 20-Aug-02 19-Oct-02 Sim NO3 at surface (0m) Sim NO3 at bottom (60m) Sim ON at surface (0m) Sim ON at bottom (60m) Field NO3 at surface (0m) Field NO3 at bottom (60m) Field ON at surface (0m) Field ON at bottom (60m)

Figure 3-9: Comparison of nitrate (NO3) Figure 3-10: Comparison of organic nitrogen concentrations between simulated and field (ON) concentrations between simulated and data field data

Organic Phosphorus Concentration Chl a Concentrations

0.020 6

0.016 5

4 0.012

3 0.008 Chl a (ug/L) 2

0.004

Organic Phosphorous (mg-P/L) 1

0.000 0 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02

Sim DOPL+POPL at surface Sim DOPL+POPL at bottom Sim chla (0m) Sim chl a (60m) Field chl a at surface (0m) Field chl a at bottom (60m) Field OP at surface Field OP at bottom

Figure 3-11: Comparison of organic Figure 3-12: Comparison of chlorophyll-a (chl- phosphorous (OP) concentrations between a) concentrations between simulated and field simulated and field data data

15 Assessment of Water Quality Using Numerical Models Model Validation

3.3 Prospect Reservoir – Validation of current conditions from 2002 – 2004

The model was successful in predicting the temperature profiles in Prospect Reservoir, exhibiting signs of accurate prediction of seasonality in the 1 year comparison. It was also able to effectively capture the trends of peaks and declines for DO concentrations.

Observations of NO3 and NH4 show that there are slight differences in the comparisons between simulated and field data but on an overall, the range was captured although there was too few field data to confirm this. Furthermore, the field data were measured at differences of 0.005 mg-N/L, which makes it more difficult to determine trends.

The comparison for simulated OP shows a peak occurring in early April with the rest of the simulation representing field data closely. The peak corresponds to an adjustment between reservoirs as mentioned before and did not have significant differences in its deviation. In addition, the same problem with the lack of field data contributed to the difficulty in ascertaining trends.

The simulations of FRP indicate that the difference between simulated and field data were quite small although the lack of more detailed data would be required to strengthen this assumption.

In general, however, the physical profile was able to be modelled successfully and the nutrient concentrations were within a close enough range, ensuring the confidence of the model to reproduce field observations.

16 Assessment of Water Quality Using Numerical Models Model Validation

Temperature Comparison Dissolved Oxygen Concentration 26 12 24 10 22

20 8

18 6

16 DO (mg/L) 4 Temperature (deg C) 14 2 12 0 10 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 10-Dec-02 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 10-Dec-02 Sim DO (0m) Sim DO (20m) Field DO (0m) Field DO (20m) Sim T (0m) Sim T (20m) Field T (0m) Field T (20m)

Figure 3-13: Comparison of temperature Figure 3-14: Comparison of dissolved oxygen profiles between simulated and field data (DO) concentrations between simulated and field data

NO3 Concentration NH4 Concentration

0.035 0.04

0.03 0.035

0.03 0.025 0.025 0.02 0.02 0.015 NO3 (mg-N/L) NH4 (mg-N/L) 0.015 0.01 0.01 0.005 0.005

0 0 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 10-Dec-02 8-Feb-03 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 10-Dec-02 8-Feb-03

Sim NO3 (3m) Sim NO3 (18m) Field NO3 (3m) Field NO3 (18m) Sim NH4 (3m) Sim NH4 (18m) Field NH4 (3m) Field NH4 (18m)

Figure 3-15: Comparison of nitrate (NO3) Figure 3-16: Comparison of ammonium (NH4) concentrations between simulated and field data concentrations between simulated and field data

Organic Phosphorus comparison Filterable Reactive Phosphorous Concentration

0.016 0.005

0.014 0.004 0.012

0.01 0.003

0.008

P (mg-P/L) 0.002 OP (mg-P/L) 0.006

0.004 0.001

0.002 0 0 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 10-Dec-02 13-Feb-02 14-Apr-02 13-Jun-02 12-Aug-02 11-Oct-02 10-Dec-02 Sim OP (0m) Sim OP (18m) Field OP (0m) Field OP (18m) Sim FRP (3m) Sim FRP (18m ) Field FRP (3m) Field FRP (18m)

Figure 3-17: Comparison of organic Figure 3-18: Comparison of FRP between phosphorous (OP) concentrations between simulated and field data simulated and field data

17 Assessment of Water Quality Using Numerical Models Methodology

4 METHODOLOGY

The literature review and validation section mentioned briefly on the scenario conditions studied in this thesis. The methodology section aims at providing further insight into the simulation conditions by presenting the methods and assumptions made during the modelling of the different strategies to improve water quality. Firstly, there will be an account of the different scenarios for simulations, followed by the description of input and output data. Then, the different management strategies are discussed and the main assumptions and methodology for simulations are presented.

This study looked at 2 different annual variations in weather conditions for Lake Burragorang, specifically periods of high inflow (or flood conditions) and periods of low inflows (without floods). For years of high inflows, the data set from years 1998 to 2000 was used because there was a major flood occurrence in August 1998. The Lake Burragorang flood scenario aims at assessing the impacts specific management strategies can have on water quality during the occurrence of high inflows into the reservoir whereas the dry years evaluate the outcomes of the same strategies on current conditions. For the assessment of dry years, the data set from 2002 to 2003 was extrapolated to year 2004 to gain insight into future predictions of water quality. A long-term perspective of 6 years covering both the wet and dry years was also included for several of the strategies.

As for Prospect Reservoir which presently receives no external inflows, the studied management strategies were considered for a data set from 2002 to 2003, extrapolated to 2004 for the same reasons as Lake Burragorang.

In summary, all simulations of water quality improvement techniques were compared to these base case scenarios:

Lake Burragorang

i) 1998 to 2000 – Years with high inflows (with floods)

ii) 2002 – 2004 – Years with low inflows (without floods)

iii) 1998 – 2004 – Long-term evaluation (with and without floods)

Prospect Reservoir

i) 2002 to 2004 – No inflows from Warragamba Pipeline (WP) and Upper Canal (UC)

Table 4-1 summarizes the simulation scenarios and implemented management strategy.

18 Assessment of Water Quality Using Numerical Models Methodology

Table 4-1: Scenarios and amelioration methods to be simulated in Prospect Reservoir and Lake Burragorang where coloured boxes refer to application of simulations:

Prospect Reservoir Lake Burragorang

No inflows Wet years Dry years Long-term

PR1 LB1 LB2 LB3

Selective withdrawal

Catchment management to reduce nutrient input

Evaluation of increased nutrient input

Biomanipulation

Bubble plume destratification

Artificial mixing by surface mechanical mixers

Sediment treatment with chemicals

4.1 INPUT DATA

As mentioned previously, a number of data sets for Prospect Reservoir and Lake Burragorang were compiled and simulations were run for a duration of 2 years and 6 years for some strategies. The original data set was used as a base test to validate the ability of the model to reproduce field observations in Lake Burragorang and Prospect Reservoir. Secondly, these validation cases are the base cases to evaluate the various water quality improvement techniques. All physical profile, meteorological, inflow and withdrawal data were collected by the Sydney Catchment Authority and collected and organized by Jose Romero from the Contract Research Group from the Centre for Water Research, University of Western Australia.

4.1.1 Physical data and lake morphometry

The morphometry of Prospect Reservoir and Lake Burragorang consists of: (1) the height of the crest of the lake above mean sea level (MSL); (2) number of inflows (if any) followed by type of inflow and streambed properties; (3) the elevation of the bottom of the lake above Australian Height Datum (AHD); (4) crest elevation or elevation of spillway (for dams); (5) number of outlets from water body

19 Assessment of Water Quality Using Numerical Models Methodology

(if any) and corresponding elevation; and (6) a matrix of elevation (mAHD) and surface area (m2) at that elevation followed by corresponding volume of water (m3) under the surface area.

4.1.2 Inflow data

Inflow data consists of

• nutrients – NH4, NO3, PONL, DONL, PO4, POPL, DOPL, DIC, DOCL, POCL, DIC (detailed in Table 4-2)

• physical data – Inflow volume, temperature, salinity, pH, colour, colloidal and non-colloidal

suspended solids, SiO2

• phytoplankton – Cyanobacteria, chlorophytes, fresh-water diatoms

4.1.3 Meteorological data

The meteorological data consists of:

-2 • Short-wave and long-wave radiation power flux density (Wm );

• Air temperature averaged over daily maximum and minimum temperature (°C);

• Average water vapour pressure (hPa);

-1 • Average wind speed (ms ); all averaged over input time-step

• Total daily rainfall (m)

The thermal structure and meteorological conditions of Prospect Reservoir were recorded by a Lake Diagnostic System (LDS) located at the centre of the lake from 2002 – 2003. Meteorological data for Lake Burragorang was recorded by the LDS located 3.5 km up the reservoir of Warragamba Dam during 2002-2003.

4.1.4 Withdrawal data

For Prospect Reservoir, there were 4 outlets; PP1800, PP2100, Lower Canal and WPS with outlet elevations of 58.30, 58.30, 52.15 and 58.30 which can be adjusted if outlets were found to go dry (i.e. water level lower than outlet elevation).

20 Assessment of Water Quality Using Numerical Models Methodology 4.2 OUTPUT DATA

The water parameters chosen to be simulated by DYRESM-CAEDYM include:

Table 4-2: Descriptions of water quality parameters

Water Parameter Description Units

DO Dissolved Oxygen mg-O/L

POPL Particulate Organic Phosphorous (labile) mg-P/L

DOPL Dissolved Organic Phosphorous (labile) mg-P/L

PO4 Filterable Reactive Phosphorous mg-P/L

NH4 Ammonium mg-N/L

NO3 Nitrate mg-N/L

DONL Dissolved Organic Nitrogen (labile) mg-N/L

PONL Particulate Organic Nitrogen (labile) mg-N/L

SSOL1 Suspended Solids mg/L

Mn2 Dissolved Mn2+ mg-Mn/L

TMN Total Manganese mg-Mn/L

Fe2 Dissolved Fe2+ mg-Fe/L

TFE Total Iron mg-Fe/L

CHLOR Chlorophytes µg-chla/L

CYANO Cyanobacteria µg-chla/L

FDIAT Freshwater diatoms µg-chla/L

21 Assessment of Water Quality Using Numerical Models Methodology 4.3 Simulation scenarios

4.3.1 PR1: 2002-2004 (No inflows)

As mentioned before, there were no inflows into Prospect Reservoir after 1996. The meteorological data set from March 2002 to February 2003 was extended to April 2004 by repeating 2002-3 meteorological data to fill in for 2003-4 data and filling in missing values with data from previous weeks. 2003-4 inflow data was constructed by repeating 2002-3 inflow data. The simulation for this scenario was run from 15 March 2002 to 13 April 2004, a period of 760 days.

4.3.2 LB1: 1998-2000 (Wet years with floods)

Meteorological, inflow and withdrawal data from 1998 to 2000 was used as the data set to simulate years with high inflows and floods in Lake Burragorang. The data set spans 21 February 1998 to 20 February 2000 for a period of 730 days, fully covering 2 stratification periods. This data set includes a major flood period occurring on August 1998 and is important in assessing the impact floods and high inflows have on the proposed mitigation methods.

4.3.3 LB2: 2002-2004 (Dry years without floods)

Simulations from 21 February 2002 to 20 February 2004 covered a period of 730 days. Data sets consisted of meteorological, withdrawal and inflow data from 21 February 2002 to 24 October 2002 were extended to February 2004 by repeating values from 24 October 2002 to 31 December 2002 using values from previous weeks. This is because the missing data occurs during stratification period and the previous data used were also from the stratification period. However, data for 1 January 2003 to February 2004 was repeated from the available data from 1 January 2002 onwards, assuming that there would be no major changes in meteorological, inflow or withdrawal during that period.

4.3.4 LB3: 1998-2004

Simulations begin from 21 February 1998 to 19th February 2004 covering 2190 days, or 6 years. Meteorological, inflow and withdrawal were provided and used as the data set for the base case.

4.4 DESTRATIFICATION SCENARIOS

The following details of bubble plume destratification and surface mechanical mixers was taken from the Dyresm Science Manual by Antenucci & Imerito (2002) and describes the equations and processes used in the DYRESM model to simulate the effects of destratification by bubble plumes and impellers.

22 Assessment of Water Quality Using Numerical Models Methodology

4.4.1 Bubble Plume Destratification – PR1

The destratification of water column, in Prospect Reservoir, by bubble plumes (or bubbler for short) is simulated in DYRESM-CAEDYM by introducing a .MIX file containing information such as the number of diffusers, number of ports on each diffuser, height of diffuser from bed as well as airflow rate at diffuser level.

The bubble plume destratifies the water column by pumping air to the bottom of the reservoir and releasing it as tiny bubbles via ports in diffusers (Antenucci & Imerito 2002). The bubbles rise and entrain ambient water, bringing it upwards due to its buoyancy flux, then release the entrained water (Antenucci & Imerito 2002). The released water falls back to its neutrally buoyant level and the whole process of entrainment by bubble plume occurs again (Antenucci & Imerito 2002).

Initialising bubble plume destratification

The bubble plume destratification in DYRESM uses simple buoyant plume equations (Antenucci & Imerito 2002). The upwards buoyancy flux is computed by:

Bair = g Qdiff 4 3 where Bair is buoyancy flux due to air (m /s )

3 Qdiff is airflow rate at diffuser level (m /s)

g is gravity acceleration (m/s2)

(Fischer et al. 1979 as cited in Antenucci & Imerito 2002)

The airflow rate, Qdiff, is the diffuser airflow rate. The airflow rate per port is determined by dividing

Qdiff by the number of ports in the diffuser (Antenucci & Imerito 2002). The following calculations will be using the airflow rate per port, which will be multiplied by the total number of ports to get the total destratification effect (Antenucci & Imerito 2002).

23 Assessment of Water Quality Using Numerical Models Methodology

Determining entrainment flow rate

The flow rate of entrained water by bubble plume can be determined by:

1 5 6π where Q is flow rate of entrained water Q =α b L B 3 z 3 P P 5 1 R α is the entrainment coefficient

b1 is a constant (assumed 4.7 from Fischer et al. 1979 as cited in Antenucci & Imerito 2002)

LR is the ratio of plume radius to plume length (assumed to be 0.1)

B is the buoyancy flux (m4/s3)

z is the bottom layer thickness (m) (Antenucci & Imerito 2002).

Determining number of diffusers, ports and airflow rate to achieve destratification

Trials of 2 and 6 diffusers with 30, 60 and 120 ports were run for different airflow rates (at the diffuser) of 0.03, 0.05, 0.1, 1, 3 and 5m3/s at zero height from the bed (i.e. diffusers lying on lake bottom). To fully destratify the water column, 6 diffusers with 120 ports at an airflow rate of 3m3/s at the diffuser level, was found to suffice at completely destratifying the water column.

4.4.2 Artificial Mixing by Surface Mechanical Mixers – PR1

Figure 4-1: Schematic diagram of the surface mixers, arrows indicate the direction of flow (Taken from Lewis et al. 2001)

The surface mechanical mixer consists of a draft tube with a large impeller at the surface of the tube pointing vertically downwards (Antenucci & Imerito 2002). The water is forced down through the draft tube, escaping at the end where it rises due to the density difference, thus entraining water and mixing water column (Antenucci & Imerito 2002). Upon exiting the draft tube, water is assumed to

24 Assessment of Water Quality Using Numerical Models Methodology behave like buoyant plume (Antenucci & Imerito 2002). The artificial mixing simulation does not model jet behaviour but instead, the plume is modelled as a line plume, wrapped around the circumference of a circular draft tube (Antenucci & Imerito 2002).

Initialising surface mechanical mixers

The upwards buoyancy flux occurs because of the density difference between water in the draft tube, which is assumed to hold similar properties as density of water at surface, and water at the base of the draft tube (Antenucci & Imerito 2002).

3 3 ρbase − ρ plume Q where B is the buoyancy flux per unit length for line plume (m /s ) B = g P ρbase π D ρplume is plume density (ρ of water at top of draft tube) (kg/m3)

3 ρbase is ambient water density at base of draft tube (kg/m )

3 QP is the daily average flow rate of impeller (m /s)

D is the diameter of draft tube (m)

(Fischer et al. 1979 as cited in Antenucci & Imerito 2002)

Determining flow rate due to entrainment

The new flow rate of water exiting the draft tube is computed by the following equations:

Q 3.32 ( D)B1/3 z Q P1 = α π ∆ j + P where zj is starting layer thickness at base of draft tube

α is the entrainment coefficient (assumed experimentally to be 0.1024). Note: this value has been halved because of the assumption that ambient water is only entrained along one side of the plume due to the vertical barrier of the draft tube

B is the buoyancy flux per unit length for line plume (m3/s3)

3 QP is the daily average flow rate of impeller (m /s)

3 QP1 is the new flow rate of impeller (m /s)

D is the diameter of draft tube (m)

(Fischer et al. 1979 as cited in Antenucci & Imerito 2002)

Determining number of impellers, draft tube length and diameter and water flow rate

To fully destratify the water column, 15 impellers with draft tubes of 17m in length and 5m in diameter and a water flow rate of 5m3/s, which is sufficient due to the weak thermal stratification in the shallow Prospect Reservoir. 25 Assessment of Water Quality Using Numerical Models Methodology

4.4.3 Sediment Treatment by Chemicals – PR1

DYRESM-CAEDYM is used to evaluate the effects of sediment treatment in Prospect Reservoir by altering the parameters of nutrient flux at the sediment-water interface. The model will attempt to evaluate the effect of applying chemicals such as iron chloride or aluminium sulphate to reduce the

PO4 flux released from sediments to water column. Wisniewski (1999) studied the effects of phosphate inactivation by direct application of ferric chloride onto sediments and concluded that a combination of FeCl3 dosage and controlled resuspension can lead to a 7-fold decrease of PO4 in water.

This thesis will attempt to assess the outcome of a 50% decrease of PO4 flux from sediments to the water column at the sediment-water interface. The original release rate of PO4 from sediments to water of 0.0026 g/m2/day was altered to 0.0013 g/m2/day. Other sediment parameters were unaltered under the assumption that low dosage addition of chemicals such as FeCl3 has no impact on physical and chemical properties as predicted by Wisniewski (1999). Another assumption made is that the chemicals will have no other effect on sediment parameters and that other nutrient fluxes from the sediments remain unchanged after sediment treatment.

4.4.4 Biomanipulation – LB3, PR1

DYRESM-CAEDYM has the capacity of modelling the biological variables in the water column such as the interaction between trophic levels (i.e. processes between phytoplankton, zooplankton and fish species). However, due to the level of complexity of these variables in the model and the lack of zooplankton and fish species data to validate the model, the higher trophic levels of zooplankton and fish were not modelled. Instead, the effect of biomanipulation was evaluated by manipulating the parameters representing phytoplankton growth. DYRESM-CAEDYM has the capacity to model 7 phytoplankton groups. For the purpose of modelling Lake Burragorang and Prospect Reservoir, only 3 groups of phytoplankton will be simulated:

CYANO – freshwater cyanobacteria

CHLOR – chlorophytes

FDIAT – freshwater diatoms

26 Assessment of Water Quality Using Numerical Models Methodology

The state equation governing phytoplankton growth is:

∂Ai SET RES =U DIC (Ai ) − RDIC (Ai ) − EDOCL (Ai ) − EPOCL (Ai )+ f A −Gi + f A ∂t i i

∂A i.e. i = photosynthesis − respiration − (mortality &excretion) ∂t + (settling & migration) − grazing + resuspension where A is the total algal biomass (mg C L-1)

Among the assumptions made are:

1. there is no resuspension of phytoplankton

2. there is no grazing on phytoplankton by zooplankton

3. there is no migration of phytoplankton

The effect of biomanipulation by mechanisms such as introduction of foreign species to control algal growth, or fish removal to increase the number of herbivorous zooplankton was simulated in DYRESM-CAEDYM through increases to the respiration rate of the phytoplankton growth equation to simulate the effects of grazing on phytoplankton groups. For this study, simulations of 10, 25 and 50% increase in respiration rate was conducted. The table below illustrates the original respiration rates of the 3 groups of phytoplankton and the new respiration rates used in the simulation of increased predation on phytoplankton:

Table 4-3: Original and altered respiration rate coefficient for biomanipulation strategies

Phytoplankton Original 10% increase in 25% increase in 50% increase in species respiration rate respiration rate respiration rate respiration rate coefficient (/day) (/day) (/day) (/day)

Cyanobacteria 0.09 0.099 0.1125 0.135

Chlorophytes 0.06 0.066 0.075 0.09

Freshwater diatoms 0.08 0.088 0.10 0.12

4.4.5 Selective Withdrawal – LB1 & LB2

One objective of this thesis is to construct an adaptive management strategy of selective withdrawal to maintain the extraction of best quality water at all times from Lake Burragorang. Because DYRESM-

CAEDYM actually models the water parameters in specific layers, much like in

27 Assessment of Water Quality Using Numerical Models Methodology

Figure 4-2, this model is ideal for the analysis of selective withdrawal strategies. The current practice of selective withdrawal at the Warragamba Dam is to be evaluated to determine the best strategy for years of high and low inflows. Analysis of withdrawal data of the dam outlets provided a general idea of the applied withdrawal strategy presently in practice:

Table 4-4: Current withdrawal strategy from Lake Burragorang at dam wall

Lake Burragorang (1998 to 2000) Lake Burragorang (2002 to 2004)

Flood withdrawal L5-30 (epilimnion) L40-45 (middle hypolimnion)

L25-50 (upper & middle Other withdrawal L50-55 (lower hypolimnion) hypolimnion)

1st Approach: Averaged withdrawal volume

This method is aimed at examining the water quality if the same volume of water was withdrawn from every outlet elevation. This was to determine which outlet provided water with best water quality and the corresponding period at which water should be extracted from the specified outlet. Lake Burragorang has a total of 14 outlets from which water can be withdrawn which can be seen in the illustration below (excluding HEPS withdrawal):

28 Assessment of Water Quality Using Numerical Models Methodology

DYRESM Corresponding outlet elevation (m) Withdrawal layer L 0 - 5 114.22

L 5 - 10 109.22

L 10 - 15 104.22

L 15 - 20 99.22

L 20 - 25 94.22

L 25 - 30 89.22

L 30 - 35 84.22

L 35 - 40 79.22

L 40 - 45 74.22

L 45 - 50 69.22

L 50 - 55 64.22

L 55 - 60 59.22

L 60 - 65 54.22

Figure 4-2: Simple diagram depicting outlet elevations at the dam wall

Another outlet is the HEPS (Hydro-Electric Power Station) outlet which only extracts water during flood periods when there is too much water in the reservoir. This outlet is at an elevation of 71.17 m.

Water quality of NH4, NO3, DONL, PONL, PO4, DOPL, POPL, SS, NODUL, CYANO, FDIAT, TMN, TFE & DO was obtained from DYRESM-CAEDYM for the years of high inflow (1998 – 2000) and low inflow (2002-2004) and analysed to determine which layer would provide water with optimal quality.

Outcomes from the 1st approach were incorporated into the 2nd and 3rd approach.

2nd Approach: Winter vs. Summer withdrawals

Another approach is to consider different layers to extract water from during winter and summer months. Several assumptions are made for this approach: 29 Assessment of Water Quality Using Numerical Models Methodology

1. Winter period causes higher inflow to the reservoir due to higher precipitation at catchment levels and summer months are drier with lower inflows.

2. Higher inflows (or floods) introduce high levels of suspended solids and pollutants such as nitrogen, phosphorus and metals attaches to these particles. Inflows into the reservoir travel along the reservoir bed signifying that suspended solids are at the lower layers of hypolimnion when it reaches the dam wall.

3. Summer stratification causes the growth of algae in the epilimnion.

Therefore, based on these assumptions, winter withdrawals are to be taken from higher hypolimnion layers where suspended solids levels are lower. Summer withdrawals can be taken from lower hypolimnion layers where there are lower concentrations of algae compared to the top layers.

Based on the outcomes of the 1st approach, several trials were run to select the layer with best water quality. The following trials are based on the LB1 1998 to 2000 wet year simulation.

Trial 1

Winter withdrawal: L5-30

Summer withdrawal: L5-50 (taken from almost all outlets except bottom few to determine which had best water quality during summer periods. Bottom outlets were excluded because of generally higher suspended solids concentration and the problem of anoxia causing poor water quality)

Trial 2

Winter withdrawal: L20-30 (L5-20 found to have comparatively poorer water quality)

Summer withdrawal: L25-50

3rd Approach: Winter vs. Summer and Flood/Storms

This approach takes into consideration the effects of flood on water quality at different layers. Using the same assumption that floods bring high amounts of suspended solids that sink and travel along the river-bed, the proposed selective withdrawal strategy extracts water from top layers (epilimnion) during floods. The 2002-2004 low inflow years indicated that summer thunderstorms also have to be considered in the management strategy.

30 Assessment of Water Quality Using Numerical Models Methodology

Trial 3

Winter withdrawal: L30-40

Summer withdrawal: L35-50

Flood withdrawal: HEPS + L5-20

Trial 4

Winter withdrawal: L35-45

Summer withdrawal: L35-50

Flood withdrawal: L5-20

Trial 5

Winter withdrawal: L25-35

Summer withdrawal: L35-50

Flood withdrawal: L5-20

The outcomes from the trials were evaluated and a general selective withdrawal strategy was determined for scenario LB2, years with low inflows.

4.4.6 Catchment Management – LB3

The objective of catchment management is to reduce the amount of nutrients and pollutants entering the reservoir. Assuming that catchment management is successful in reducing nitrogen and phosphorous levels (in the form of PO4, PONL, NH4, NO3 & POPL), the model evaluates the effects of nutrient reduction by numerically reducing the nutrient concentrations by 5%, 25%, 50% and 80%. However, major assumptions were made for this simulation:

1. Catchment management reduced nutrients at an equal rate (i.e. equal percentage reduction of

PO4, PONL, NH4, NO3 & POPL) and does not take into account the fact that, for example, re- vegetation aims at up-taking dissolved forms of inorganic nitrogen, thus altering the ratio of different species of N & P entering water bodies.

2. Catchment management does not reduce the amount of suspended solids entering the reservoir

3. The simulations of nutrient reduction are after the implementation of catchment management when input rates have stabilised. 31 Assessment of Water Quality Using Numerical Models Methodology

Simulations covering a period of 6 years, from 1998 to 2004, were run to evaluate the long-term impacts of nutrient reduction. The years 1998 to 2004 were chosen because 1998 represented years of high inflows while 2003 represented relatively dry years. By envisaging the 1998 to 2004 simulation as future predictions (i.e. using 1998 to 2004 data to represent future conditions and predicting for the future), the possible effects of catchment management can be evaluated.

4.4.7 Evaluation of effect of increased nutrient input into Lake Burragorang – LB3

This scenario is aimed at studying the long-term impact of increased external nutrient loading on water quality in Lake Burragorang as opposed to catchment management. Similar to the reduced nutrient input simulation methodology, this scenario was simulated by increasing nutrient concentrations entering Lake Burragorang by 20% and 50% over a period of 6 years (1998 to 2004). The objective of this simulation is to understand the impacts of increased nutrient input in order to assess possible mitigation methods targeted at the identified problems. Assumptions made for this scenario are:

1. Increased nutrient loading is at the same rate for different species of N & P (i.e. dissolved and particulate forms of N or P are increasing at the same rate)

2. There is no increase in concentration of suspended solids entering the reservoir.

32 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

5 RESULTS AND DISCUSSION – LAKE BURRAGORANG

5.1 Outcomes of Selective Withdrawal Strategy – LB1 & LB2

An optimal selective withdrawal strategy was developed with the hydrodynamic-ecological, DYRESM-CAEDYM, through simulations of water quality at the water layers corresponding to outlet elevations. The following results were obtained by manually choosing specific layers and comparing the simulated water quality data.

1st Approach: Averaged withdrawal volume

This approach uses DYRESM-CAEDYM to predict water quality from layers 0-5 (L0-5), or topmost layer, to layers 60-65 (L60-65) averaged over an entire year. The approach takes the same volume of water per outlet through current configuration of 13 outlets.

Several trends were able to be deduced from this approach

I. The concentration of phytoplankton decreased from L0-5 to L60-65

II. Higher concentrations of particulate nutrients (PONL, POPL), suspended solids (SS) and metals (TMn, TFe) at lower layers (L25-65) during floods

During floods, colder waters from the catchment travel along the reservoir bed, hence the higher concentrations of suspended solids and particulate nutrients. Nutrients, metals and other pollutants are usually found to be adsorbed to suspended solids or sediments washed down the reservoir during flood periods, hence, with increasing levels of SS, there is an associated increase of nutrients and metal concentrations.

III. NH4, NO3 levels generally increase further down the water column

2nd Approach: Winter vs. Summer withdrawals

Based on the outcomes of the 1st approach, several trials were simulated as specified in the selective withdrawal methodology section but here, seasonal averages during the summer and winter were computed.

The general approach of selective withdrawal strategy is to maintain low suspended solids because of the association with lower concentrations of nutrients and metals. Hence, the focus here was minimizing suspended solids of extracted water.

Results of Trial 2:

33 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Suspendedso lids for Trial 2 (L20-50)

160

110

60 SS (mg/L) 10

21-Feb-98 22-Aug-9820-Feb-9921-Aug-9919-Feb-00 -40

L20-25 L25-30 L30-35 L35-40 L40-45 L45-50

Suspendedso lids for Trial 2 (L30-50)

20

15

10 SS (mg/L) 5

0 21-Feb-9822-Aug-9820-Feb-9921-Aug-9919-Feb-00

L30-35 L35-40 L40-45 L45-50

(a) (b)

Figure 5-1: Outcomes of Trial 2 – Suspended solids concentration for (a) Layer 20 – 50; and (b) Layer 30 - 50

Algalbi oma s sfor Trial 2 (L30-50)

1

0.8

0.6

0.4 (ug chl-a/L) 0.2

0 21-Feb-9822-Aug-9820-Feb-9921-Aug-9919-Feb-00

L30-35 L35-40 L40-45 L45-50

101221(ug chl-a/L) 02468-FeL2b-0-298225 AlL25ga-30lAubig- 982omassL30-350- foFerb- TrL35992ial-4 20 (L201-Au-5L40-g-0)45991L49-5-Fe50b-00

(a) (b)

34 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Figure 5-2: Outcomes of Trial 2 – Algal biomass with chlorophyll-a as the indicator for (a) L30 – 50; and (b) L20 - 50

Inflow olumev romf Lake Bur ragorangs' 7 tr ibutaries

3.00E+08

2.50E+08

2.00E+08

1.50E+08 1.00E+08 Volume (m^3)

5.00E+07

0.00E+00 21-Feb-98 22-Aug-98 20-Feb-99 21-Aug- 9 19-Feb- 0

Figure 5-3: Inflow volume from the 7 tributaries of Lake Burragorang

Figure 5-1 and Figure 5-2 shows that suspended solids at L20-30 are found to have concentrations 7 times more during the August 1998 and October 1999 floods, depicted in Figure 5-3, than other periods of low inflow. However, compared to the concentrations of suspended solids at the layers 30- 65, L20-30 still proved to be the optimal extraction level.

3rd Approach: Winter vs. Summer and Flood/Storms

The 3rd approach exhibited the correlation between high inflow and high suspended solids concentration confirming that with the input of high volumes of water into the reservoir comes high influxes of suspended solids.

35 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Suspendedso lids Trial (L35-30 &40L-50)

20

15

10 SS (mg/L) 5

0

21-Feb-9822-Aug-9820-Feb-9921-Aug-9919-Feb-00

L5-10 L10-15 L15-20 L20-25 L25-30 L40-45 L45-50

Suspendedso lids Trial 3L5(-50)

200

150

100

SS (mg/L) 50

0

21-Feb-98 22-Aug-9820-Feb-9921-Aug-9919-Feb-00

L5-10 L10-15 L15-20 L20-25 L25-30 L30-35 L35-40 L40-45 L45-50

(a) (b)

Figure 5-4: Outcomes of Trial 3 – Suspended solids concentration for (a) L5 – 30 and L40 – 50; and (b) L5 - 50

PONL Trial (L35-30 &30L-40)

0.25

0.2 0.15

0.1

0.PONL (mg-N/L) 05 0

21-Feb-98 22-Aug-98 20-Feb-9921-Aug-9919-Feb-00

L5-10 L10-15 L15-20 L20-25 L25-30 L40-45 L45-50

PONL (mg-N/L) 0.1.210512-Feb-9822L5L3-100-35 -AuPONLg-L1035982-1-450 Tria0-lFeL15-40-3 b-2045L5(992-50)L2L41-0-25-5Au50g-991L25-309-Feb-00

(a) (b)

36 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Figure 5-5: Outcomes of Trial 3 – Labile Particulate Organic Nitrogen (PONL) concentrations for (a) L5 – 30 and L40 – 50; and (b) L5 - 50

The outcomes of Trial 3 indicates that layers 30-40 had higher suspended solids and particulate nutrient concentrations compared to the other layers. Hence, the winter withdrawals from layers 35-45 should be changed to alternative layers.

Trials 4 and 5 aimed to determine whether winter withdrawals should be at higher layers or lower layers. Trial 4 was a simulation of winter withdrawals at L40-50 and Trial 5 simulated winter withdrawal at L25-35.

Trial 4

Trial 5

37 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Trial 4:

Trial 5:

38 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Trial 4:

Trial 5:

From the comparisons of PONL and DONL (particulate and dissolved organic nitrogen), it is clear that the water quality is best at L25-35 compared to L40-50. The same comparison and conclusions also resulted for NO3, PO4 and NH4. However, suspended solids were higher during the flood periods for L25-35, going as high as 180 mg/L compared to the 140 mg/L at L40-50. Therefore, for a wet year simulation using 1998 to 2000 data set, the Trial 5 withdrawal strategy provided optimal water quality.

The purpose of examining which outlet provides the highest water quality is to provide a general strategy for selective withdrawal. For the scenario LB1, or wet years with high inflows, an effective selective withdrawal strategy is as follows:

39 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Winter withdrawal: Upper hypolimnion (corresponding to outlet elevation of 84.22 and 89.22m)

Summer withdrawal: Middle hypolimnion (corresponding to outlet elevation of 69.22, 74.22 and 79.22m)

Flood withdrawal: Epilimnion (corresponding to outlet elevation of 99.22, 104.22 and 109.22m)

DYRESM Corresponding outlet Withdrawal layer elevation (m)

L 0 - 5 114.22

L 5 - 10 109.22 Epilimnion L 10 - 15 104.22

L 15 - 20 99.22

L 20 - 25 94.22 Upper hypolimnion L 25 - 30 89.22

L 30 - 35 84.22

L 35 - 40 79.22 Middle hypolimnion L 40 - 45 74.22

L 45 - 50 69.22

L 50 - 55 64.22 Lower hypolimnion L 55 - 60 59.22

L 60 - 65 54.22

Figure 5-6: Simple illustration of the position of outlets and corresponding layers; and the approximate location of epilimnion and hypolimnion

The general selective withdrawal strategy realized by scenario LB1 was applied to scenario LB2, years with low inflows with slight modifications. Dry years result in lower water levels, thus altering the withdrawal strategy by extracting water from lower layers. While the general rule of winter withdrawal from the upper hypolimnion and summer withdrawal from lower hypolimnion still applies, the corresponding outlet elevation shifted downwards. The best selective withdrawal strategy for years with low inflows is as follows: 40 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Winter withdrawal: L30-40

Summer withdrawal: L45-55

Storm withdrawal: L50-55

A new consideration during dry years is the occurrence of summer thunderstorms that may generate more inflow during dry years although the inflow volume would be far smaller than a flood volume in wet years. For the case of thunderstorms, water quality was found to be best at the lower hypolimnion.

In conclusion, the proposed selective withdrawal strategy is winter withdrawal from upper hypolimnion, summer withdrawal from middle hypolimnion, flood withdrawal from epilimnion and summer storm withdrawal from middle hypolimnion.

Ranges of water quality for these different strategies and conditions are summarized in Table 5-1 in comparison with the current withdrawal strategy in

41 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Table 5-2. A simplified diagram of selected water outlets for withdrawal during different periods can be found in Figure 5-7 to Figure 5-10.

1998-2000 Base case Selective Withdrawal

50

45

40 L5-10 35 L10-15 30 L15-20 L20-25 25

Layers L25-30 20 L35-40 L40-45 15 L45-50 10

5

0 5-Jan-98 24-Jul-98 9-Feb-99 28-Aug-99 15-Mar-00 1-Oct-00

Figure 5-7: The 1998 – 2000 base case selective withdrawal strategy

1998 - 2000 Simulated Selective Withdrawal

50

45 L0_5 L5_10 40 L10_15 35 L15_20 L20_25 30 L25_30 25 L30_35 Layers L35_40 20 L40_45 15 L45_50 L50_55 10 L55_60 5 L60_65

0 18-Dec-68 2-May-70 14-Sep-71 26-Jan-73 10-Jun-74 23-Oct-75 6-Mar-77

Figure 5-8: The 1998 – 2000 simulated selective withdrawal strategy

42 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

2002 - 2004 Base Case Selective Withdrawal

60

50

40

L20-25 30 L40-45 Layers L50-55

20

10

0 5-Nov-01 24-May-02 10-Dec-02 28-Jun-03 14-Jan-04 1-Aug-04

Figure 5-9: The 2002 - 2004 base case selective withdrawal strategy

2002 - 2004 Simulated Selective Withdrawal

60

50

40 L30-35 L35-40 30

Layers L45-50 L50-55 20

10

0 5-Nov-01 24-May-02 10-Dec-02 28-Jun-03 14-Jan-04 1-Aug-04

Figure 5-10: The 2002 – 2004 simulated selective withdrawal strategy

Table 5-1: Overall water quality ranges of extracted water after application of selective withdrawal strategy for both wet and dry years (all concentrations are in units of mg/L).

Burragorang 2002-2004 (Selective Withdrawal) NH4 NO3 PONL DONL TFe winter withdrawals 0.0045 - 0.015 0.040 - 0.21 0.001 - 0.017 0.14 - 0.24 0.029 - 0.13

43 Assessment of Water Quality Using Numerical Models Results and Discussion – LB summer withdrawals 0.0076 - 0.018 0.059 - 0.13 6.0E-4 - 0.046 0.13 - 0.23 0.022 - 0.043 PO4 POPL DOPL SS TMn winter withdrawals 1.7E-4 - 0.0049 2.9E-5 - 5.5E-4 6.5E-4 - 0.0076 0.014 - 0.58 0.0024 - 0.0078 summer withdrawals 0.0015 - 0.006 1.5E-5 - 0.0036 5E-4 - 0.0074 0.011 - 2.23 0.0031 - 0.0060

Burragorang 1998-2000 (Selective Withdrawal) NH4 NO3 PONL DONL TFe winter withdrawals 0.0042 - 0.033 0.013 - 0.52 0.0016 - 1.61 0.033 - 0.30 0.015 - 0.41 summer withdrawals 0.0076 - 0.025 0.048 - 0.47 0.0088 - 0.23 0.13 - 0.31 0.032 - 0.34 flood withdrawals 0.0054 - 0.011 0.029 - 0.15 0.023 - 0.21 0.12- 0.18 0.063 - 0.17 PO4 POPL DOPL SS TMn winter withdrawals 1.6E-4 - 0.018 1.5E-4 - 0.25 6.8E-4 - 0.015 0.054 - 190 0.0026 - 0.041 summer withdrawals 0.002 - 0.035 9.3E-5 - 0.025 5E-4 - 0.027 0.35 - 19 0.0025 - 0.036 flood withdrawals 1.3E-4 - 0.0049 8.9E-4 - 0.018 0.0026 - 0.0058 0.38 - 9.2 0.0083 - 0.018

44 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Table 5-2: Overall water quality ranges of extracted water for base case of wet and dry years (all concentrations are in units of mg/L).

Burragorang 2002- 2004 (Base Case) NH4 NO3 PONL DONL TFe withdrawals 0.0076 - 0.02 0.03 - 0.14 5.3E-4 - 0.063 0.12 - 0.24 0.021 - 0.15 flood withdrawals 0.009-0.017 0.046 - 0.13 0.00053-0.063 0.13 - 0.24 0.021 - 0.15 PO4 POPL DOPL SS TMn withdrawals 1.9E-4 - 0.0072 1.5E-5 - 0.0063 5E-4 - 0.0081 0.012 - 2.3 0.0026 - 0.062 flood withdrawals 1.9E-4 - 0.0049 1.5E-5 - 0.0063 6.1E-4 - 0.0081 0.012 - 2.3 0.0026 - 0.062

Burragorang 1998- 2000 (Base Case) NH4 NO3 PONL DONL TFe 0.036 - withdrawals 0.0061 - 0.034 0.027 - 0.55 0.0039 - 1.4 0.23 0.016 - 0.40 flood withdrawals 0.0061 - 0.03 0.027 - 0.55 0.0039 - 1.4 0.036- 0.23 0.016 - 0.40 PO4 POPL DOPL SS TMn withdrawals 0.002 - 0.019 9.4E-5 - 0.25 5E-4 - 0.015 0.089 - 177 0.0025 - 0.040 flood withdrawals 0.002 - 0.016 3.6E-4 - 0.25 6.3E-4 - 0.011 0.089 - 177 0.0047 - 0.040

From the comparison of base case and selective withdrawal application scenarios, the water quality does not significantly change after application of the proposed strategy. Some characteristics of the water (e.g. suspended solids (mg/L), are found to be at lower levels with the proposed strategy.

The optimal water strategy was found to be the current water strategy. In this case the model results confirmed that the current practice, base on engineering experience, is the optimal one.

5.2 Evaluation of catchment management

The main objective of the catchment management scenarios is to evaluate reductions in external nutrient inputs with DYRESM-CAEDYM to predict improvement to water quality. The catchment management scenarios were simulated by decreasing the amount of nutrients in inflows by 0% (base case), 5%, 25%, 50% and 80%. The simulations evaluated the effects of management strategies to reduce catchment inputs.

In general, simulated total phosphorous (TP) and total nitrogen (TN) trends at the surface and bottom of the Lake Burragorang (Figure 5-11 and Figure 5-13) illustrate a sharp increase in total nitrogen and total phosphorous during a major flood. Otherwise, nutrient levels generally increased over time in the simulations of surface waters (Figure 5-11 and Figure 5-13). Figure 5-12 and Figure 5-14 depict nutrient dynamics in the bottom waters with the minimum concentrations occurring during winter mixing. Seasonal stratification prevents mixing in the water column and the build-up of nutrients in the hypolimnion. With winter mixing, the nutrients are mixed throughout the entire water column during winter.

45 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

TPat sur face (0m romf sur face) 0.100

0.080

0.060

0.040 TP (mg-P/L) 0.020

0.000

21-Feb-98 21-Feb-00 20-Feb-02 20- Feb-4 0 Bas e Ca s e (0%) 5% 25% 50% 80%

Figure 5-11: Total phosphorous (TP) concentrations (mg-P/L) comparison at the surface for base case (0%), 5%, 25%, 50% and 80% nutrient reduction cases for Lake Burragorang from 1998 to 2004

TPat botom t ( 70m from surf ace) 0.14 0.12 0.10 0.08 0.06

TP (mg-P/L) 0.04 0.02 0.00 21- Feb- 98 21-Feb- 0020-Feb-02 20- Feb- 04

Bas e case (0% ) 5% 25% 50% 80%

Figure 5-12: Total phosphorous (TP) concentrations (mg-P/L) comparison at 70m from the surface for base case (0%), 5%, 25%, 50% and 80% nutrient reduction cases for Lake Burragorang from 1998 to 2004

46 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

TNat het surface

1.00 0.80 0.60 0.40 TN (mg-N/L) 0.20 0.00 21- Feb- 98 21- Feb-00 20-Feb-02 20-Feb-04 Bas e Ca s e (0%) 5% 25% 50% 80%

Figure 5-13: Total nitrogen (TN) concentrations (mg-N/L) comparison at the surface for base case (0%), 5%, 25%, 50% and 80% nutrient reduction cases for Lake Burragorang from 1998 to 2004

TN at het bottom(70m from su rf ace)

1.00

0.80 0.60 0.40

TN (mg-N/L) 0.20

0.00 21- Feb-98 21-Feb- 00 20-Feb- 02 20-Feb- 04 Base Case (0%) 5% 25% 50% 80%

Figure 5-14: Total nitrogen (TN) concentrations (mg-N/L) comparison at 70m from the surface for base case (0%), 5%, 25%, 50% and 80% nutrient reduction cases for Lake Burragorang from 1998 to 2004

Figure 5-11 to Figure 5-14 illustrates the effect of nutrient reduction by 0% (base case), 5%, 25%, 50% and 80% at the surface layer and at the bottom of the reservoir. In general the nutrient reduction simulations illustrate that less nutrient input into the reservoir results in lower reservoir levels. The simulations confirm that greater nutrient decreases to inflows through catchment inputs reduces the overall nutrient levels in the reservoir a similar amount relative to the current base case of no nutrient reduction. 47 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

The water quality in Lake Burragorang is good in years with low inflows. Flood events, such as the August 1998 flood, causes nutrient levels to increase, 3 to 4 times in the case of the 1998 flood (Figure 5-11 and Figure 5-13). TN increased from 0.31 to 0.83 mg N/L over 10 days during this flood while TP increased from 0.015 to 0.076 mg-P/L. Simulations predicted that 5, 25 and 50% nutrient reductions to inflow levels did not greatly affect water quality. However, the 80% nutrient reduction strategy had a greater reduction with a long-term average decrease of 52% for algae and 63% for nutrients.

Table 5-3 presents the long-term averages for total phosphorous, total nitrogen and chlorophyll a over a period of the 6 year simulation to provide a general understanding of the effect of nutrient reduction.

Table 5-3: Long-term averages for TP, TN and Chlorophyll-a for different nutrient reduction cases

Average TP* (mg-P/L) Average TN* (mg-N/L) Average Chla* (µg-chla/L) Nutrient Reduction cases 0m 70m 0m 70m 0m 70m

Base Case (0%) 0.025 0.046 0.40 0.53 3.84 1.36

5% 0.024 0.043 0.39 0.51 3.67 1.33

25% 0.020 0.038 0.33 0.44 3.08 1.12

50% 0.015 0.033 0.26 0.36 2.43 0.87

80% 0.010 0.026 0.18 0.18 1.49 0.46

*Values based on averages over 6 years

% decrease in TN concentration at surface % decrease in TN concentration at bottom (70m from surface) 80% 100%

60% 50%

40% 0% 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 20% -50%

0% -100% 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 -20% -150% 5% case 25% case 50% case 80% case 5% case 25% case 50% case 80% case

(a) (b)

Figure 5-15: Percentage decrease of total nitrogen (TN) compared to base case for 5, 25, 50 and 80% nutrient reduction cases (a) at the surface; and (b) 70m from the surface

48 Assessment of Water Quality Using Numerical Models Results and Discussion – LB

Percentage decrease in TP at surface Percentage decrease in TP concentrations at bottom (70m from surface) 80% 120%

60% 80%

40% 40%

20% 0% 21-Feb-98 21-Feb-00 20-Feb-02 0% -40% 21-Feb-98 21-Feb-00 20-Feb-02 -20% -80%

-40% -120%

5% case 25% case 50% case 80% case 5% case 25% case 50% case 80% case

(a) (b)

Figure 5-16: Percentage decrease of total phosphorous (TP) compared to base case for 5, 25, 50 and 80% nutrient reduction cases (a) at the surface; and (b) 70m from the surface

Figure 5-15 and Figure 5-16 shows the percentage decrease of total nitrogen and total phosphorous, at surface waters and bottom waters, compared to the base case. The 5% case has almost no influence, while the 25, 50 and 80% simulated a reduction in nutrients in surface layers by an average of 20, 40 and 60% respectively. However, while the general pattern of increasing nutrient reductions increased nutrient levels in the reservoir, there appeared to be an increase of nutrient concentrations in the bottom waters during the winter mixing events where the 25 and 50% case shows an increase of nutrient levels compared to base case as indicated in Error! Reference source not found.. This is due to the timing of predicted nutrient levels by the model being 1 day earlier or later and corresponds to a difference of 0.26 mg-N/L (base) and 0.66 mg-N/L (25% case).

% decrease in TN concentration at bottom (70m % decrease in TN concentration at bottom (70m from surface) from surface) 100% 100%

50% 50%

0% 0% 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 -50% -50%

-100% -100%

-150% -150% 5% case 80% case 25% case 50% case

(a) (b)

Figure 5-17: A comparison of the percentage decrease of total nitrogen concentrations at the bottom for (a) 5 and 80% case; and (b) 25 and 50% case

49 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Simulations of catchment management on algal levels (Figure 5-18) with chlorophyll a as the indicator indicated that the peaks correlated to the onset of summer stratification. The August 1998 flood had no direct impact on algal growth despite the increased nutrient inputs because these nutrients were simulated to remain in the hypolimnion. Direct comparison of base case and nutrient reduction cases confirmed that both the 5% and 25% nutrient reduction scenarios had little impact on algal concentrations at the surface layer. (Figure 5-18) illustrates that reductions of algal concentrations in the surface waters for the 50% and 80% nutrient reduction cases correspond to an average of 28% reduction of peak chlorophyll a for the 50% case, and an average of 45% reduction in peak chlorophyll a for the 80% case.

Algal concentrations at surface (0m) Algal concentrations at surface (0m)

15 15

10 10

5 5 Chl-a (ug-chl-a/L) Chl-a (ug-chl-a/L) 0 0 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04

Base Case (0%) 5% 25% Base Case (0%) 50% 80%

(a) (b)

Figure 5-18: Algal concentrations comparison for (a) 0 %, 5 % and 25 % nutrient reduction case at surface; and; (b) 0%, 50% and 80% nutrient reduction case at surface

A catchment management scenario with an 80% reduction of nutrients was necessary to significantly reduce the nutrient and algal levels in the water column. The 5, 25 and 50% nutrient reduction cases were found to have little effect on water quality over the 6 year simulations. However, the particular effect of nutrient reduction is difficult to quantify and determine because of the low initial nutrient concentrations, i.e. a small change in nutrient levels can lead to a high percentage change. Furthermore, the implementation of a catchment management strategy to reduce inflow nutrients would take a long period of time to implement prior to reductions considered here. Another shortcoming of this strategy is that catchment management is difficult to simulate the dynamic nature of this remediation process. For example, re-vegetation will constantly alter the amount of nutrients entering the reservoir. Initial stages of re-vegetation may have no influence on nutrient reduction until they are fully established and capable in up-taking nutrients and preventing leaching of nutrients into water-bodies. Hence, the direct nutrient reduction of 5, 25, 50 and 80% is under the assumption that

50 Assessment of Water Quality Using Numerical Models Results and Discussion – PR catchment management has been implemented and that the model is simulating effects of fully established management strategies.

Recommendations

An improved model simulation of the effects of catchment management requires more detailed understanding of the source of nutrients and the fractions of nutrient species entering the reservoir. A basic model to simulate nutrient generation rates on sub-catchment levels, including parameters such as soil types, slopes and land use will assist in assessing the effectiveness of management practices and determine the amount of nutrient input into the reservoir.

To further comprehend the effect of nutrient change on the reservoir water quality, simulations of increased nutrient input are considered next.

Effects of increased nutrient input from catchment

The same comparison and analysis, as before, was conducted to evaluate the effect of increased nutrient inputs from the catchments by 20 and 50%. Figure 5-19 and Figure 5-20 depicts that increasing TP and TN concentrations trend in the epilimnion from 1998 to 2004 were predicted, which clearly shows the increase in total nutrient levels during the August 1998 flood. The hypolimnion peaks and troughs shown in Figure 5-19 and Figure 5-20 are associated with summer stratification and winter mixing and the processes were explained in the previous section “Evaluation of catchment management”.

TP at surface (0m from surface) TP at bottom (70m from surface) 0.120 0.16 0.14 0.100 0.12 0.080 0.10 0.060 0.08 0.06 TP (mg-P/L) TP (mg-P/L) 0.040 0.04 0.020 0.02 0.000 0.00 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 Base Case (0%) 20% increment 50% increment Base case (0%) 20% increment 50% increment

(a) (b)

Figure 5-19: Total phosphorous (TP) concentrations for (a) 0%, 20% and 50% nutrient increment cases in the epilimnion; and (b) for 0%, 20% and 50% nutrient increment cases in the hypolimnion

51 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Total Nitrogen at surface TN at bottom (70m from surface) 1.20 1.20 1.00 1.00 0.80 0.80 0.60 0.60

TN (mg-N/L) 0.40

TN (mg-N/L) 0.40 0.20 0.20 0.00 0.00 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 Base Case (0%) 20% increment 50% increment Base Case (0%) 20% increment 50% increment

(a) (b)

Figure 5-20: Total nitrogen (TN) for (a) 0%, 20% and 50% nutrient increment from catchment to reservoir in epilimnion; and (b) 0%, 20% and 50% nutrient increment from catchment to reservoir in hypolimnion

Analysis of the simulation results indicate that the increased nutrient inputs from the catchment caused a 12% increase in nutrients and algae concentrations in the reservoir for the 20% nutrient increment scenario, and about a 25% increase for the 50% nutrient increment scenario, based on long-term averages (Figure 5-21 and Figure 5-22).

Percentage increase in TP concentrations at the Percentage increase of TP at bottom (70m from surface surface)

40% 80%

30%

20% 40%

10% 0% 0% 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 -10% -40%

20% increment 50% increment 20% increment 50% increment

(a) (b)

Figure 5-21: Percentage increase of total phosphorous concentrations at (a) the surface; and (b) at 70m from the surface for 20% and 50% increased nutrient input

52 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

% increase in TN concentration at the surface % increase in TN concentration at the bottom (70m from surface) 40% 80%

30% 60%

20% 40%

20% 10%

0% 0% 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 21-Feb-98 21-Feb-00 20-Feb-02 20-Feb-04 -20%

20% nutrient increment case 50% nutrient increment case 20% nutrient increment case 50% nutrient increment case

(a) (b)

Figure 5-22: Percentage increase of total nitrogen concentrations at (a) the surface; and (b) at 70m from the surface for 20% and 50% increased nutrient input

Again, the 20 and 50% nutrient increment scenario had little effect on the water quality because the nutrient levels were low. In

Table 5-3 the long-term averages of total phosphorous, total nitrogen and algal levels (represented by chlorophyll a concentrations) at the epilimnion and hypolimnion show that the levels are small and any changes in simulated concentrations of increased nutrient input do not result in significant nutrient level changes, but substantial changes in percentages.

Table 5-4: Long-term averages for TP, TN and Chlorophyll-a for different nutrient input increment cases

Average TP* (mg-P/L) Average TN* (mg-N/L) Average Chla* (µg-chla/L) Nutrient input increment scenarios 0m 70m 0m 70m 0m 70m

Base Case (0%) 0.025 0.046 0.40 0.53 3.84 1.36

20% 0.028 0.052 0.46 0.61 4.37 1.53

50% 0.034 0.060 0.55 0.71 5.18 1.79

* based on 6 year simulation averages

The general conclusion from the evaluation of catchment loading increases is that there would be little impact on nutrient levels as illustrated by TN and TP (Table 5-4). A 50% increase of nutrient inputs from the catchments would correspond to an increase of overall nutrient levels in the reservoir waters of 25 percent, which is a small change considering the low nutrient levels.

53 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

BIOMANIPULATION IN LAKE BURRAGORANG

The main purpose for the implementation of biomanipulation strategies is to use management strategies to manipulate higher trophic levels to reduce nuisance algal concentrations in the water column. One manner of biomanipulation is to increase herbivorous zooplankton to consume targeted species of phytoplankton. The simulations for this strategy were conducted in Lake Burragorang over a 6 year period, from 1998 to 2004 to produce a preliminary study of the possible effects of biomanipulation

Increased grazing on phytoplankton was simulated by increasing the respiration rate of algae by 10, 25 and 50%. The 10% increase in respiration rate will be referred to as BIO1, the 25% as BIO2 and the 50% as BIO3. The simulated phytoplankton concentration in water column is portrayed in Figure 5-23. Chlorophyll-a is used as an indicator for algal concentrations in the water column. The peaks of algal concentrations correspond to winter mixing events during May to October during complete mixing during annual maxima of nutrient levels with an increase of 10 to 20 times summer levels. The 1998 winter algal levels were the highest because of the August 1998 flood, which introduced high loads of nutrients into the reservoir. In subsequent years, the absence of flood events resulted in lower algal levels. The summer period of November to March always had minima algal levels because of nutrient limitation over the 6 year period.

Figure 5-23 also illustrates the effects of the 10, 25 and 50 % increase in respiration rate. In the first year (1998), the biomanipulation strategy predicted a decrease in algae with BIO1 less successful than BIO3. In 1999, BIO1 and BIO2 had no effect on peak concentrations while BIO3 reduced algal concentrations. However, in subsequent years, BIO1 to BIO3 strategies had a completely opposite effect, with peak concentrations higher than the base case with the BIO3 strategy contributing to highest peak concentration and BIO1, the lowest. Also, peaks occurred later than the base case. In summary, simulated biomanipulation strategies were successful in overall lower algal concentrations during the summer, but higher peaks in winter concentrations. The other trend was that higher respiration rates lead to lower summer and autumn concentrations. Hence, biomanipulation strategies decreased summer and autumn levels, but increased winter and spring maximal algal levels, presumably from changes in the nutrient regime.

54 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Comparison of algal concentrations in the epilimnion

25

20

15

10 Chlorophyll - a (ug-chla/L) 5

0 21-Feb-98 21-Feb-99 21-Feb-00 20-Feb-01 20-Feb-02 20-Feb-03 20-Feb-04

BIO1 chl-a (0m) BIO2 chl-a (0m) BIO3 chl-a (0m) BASE chl-a (0m)

Figure 5-23: Comparison of algal concentrations for base case, BIO1 (10% increase in respiration rate), BIO2 (25% increase in respiration rate) and BIO3 (50% increase in respiration rate)

The slight shift of algal concentrations to a later date resulted from changes in nutrient regime, through less nutrient uptake because of less phytoplankton growth. A comparison of nutrient availability in the form of dissolved inorganic nitrogen (DIN) and algal growth is illustrated in Figure 5-24.

Phytoplankton uptake dissolved inorganic nutrients (NO3, NH4 and PO4).

55 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

DIN comparison with chl-a concentration in epilimnion

0.35 25

0.3 20 0.25

15 0.2

0.15 10 DIN (mg-N/L)

0.1 Chlorophyll-a (ug-chl-a/L) 5 0.05

0 0

Feb-98 Feb-99 Feb-00 Feb-01 Feb-02 Feb-03 Feb-04

BASE DIN (0m) BIO3 DIN (0m) BASE chl-a(0m) BIO3 chl-a (0m)

Figure 5-24: Comparison of dissolved inorganic nitrogen (DIN) and chlorophyll-a concentration in the epilimnion for the base case and BIO3 case

Figure 5-24 shows that DIN in the epilimnion for the base case increased over the winter complete mixing period, and was relatively low in summer. The increased algal respiration rates, to simulate greater biomanipulation, had the effect of increasing levels of DIN later in the year as illustrate in the comparison between the base case and BIO3. This shift resulted from reduced algal growth that caused a decrease in the uptake of DIN, DIN in the water column increased relative to the base case. Hence, the effect of biomanipulation as simulated here was an overall higher and later occurrence of peak DIN concentration in all simulated biomanipulation scenario.

In Figure 5-25, time series point in the epilimnion illustrates NO3 (sub-panel a) and NH4 (sub-panel b) uptake by phytoplankton from the water column, with a greater preference for NH4 uptake. The effect of the BIO3 case was to increase rates DIN uptake because of greater availability. Hence, the same shift to the right, indicating a later period of maximal uptake of nutrients, is evident in both NO3 and

NH4 phytoplankton uptake rates.

Figure 5-25 (a) indicates that NO3 is being removed from the water column. However, Figure 5-25 (b) shows that there is either more NH4 uptake or the same as base case but this does not impact on DIN concentrations as much as NO3 because NO3 concentrations are an order of magnitude higher than

NH4 concentrations. The same shift to the right, indicating a slower uptake of nutrients, is evident in both NO3 and NH4 phytoplankton uptake, confirming that a slower algal growth rate results in reduced nutrient uptake. This has implications on the water quality because of the increase of NO3 and NH4 in water column, which is undesirable. 56 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

(a) (b)

Figure 5-25: Comparison of (a) NO3 phytoplankton uptake; and (b) NH4 phytoplankton uptake for base case and BIO3 case at the epilimnion

The higher peak concentrations of chlorophyll-a of biomanipulation simulations in Figure 5-24, relative to the base case, has been demonstrated to result from higher amount of available nutrients. In the base case, phytoplankton growth occurs during winter months because the water column becomes fully mixed and allows nutrients to be mixed from the hypolimnion to the epilimnion. Because of the increased algal growth, nutrient limitation occurs. In the case of BIO3, the reduced algal growth allows a higher concentration of available nutrients for uptake to remain in the water column, and peak algal levels occur later when warmer conditions become favourable to the phytoplankton. Because of the readily available amount of nutrients and warmer conditions, phytoplankton growth reaches a peak higher than the base case with less nutrients and colder conditions. Although the BIO3 algal peak occurred later, the decline in algal concentrations occurred at the same time as the base case implying that summer stratification developed and prevented further nutrient supply to the epilimnion. In general, BIO1 contributed to an insignificant increase of algal concentrations while BIO2 was associated with a small reduction in 1998, not in 1999, and a steady increase of peak concentrations in following years. The BIO3 simulations indicated an effective reduction of algal concentration in the first year but had higher peak concentrations as well in subsequent years of about 40 % increase.

57 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

PO4 comparison with chlorophyll-a at epilimnion

0.025 25

0.02 20

0.015 15

0.01 10 PO4 (mg-P/L)

0.005 5 Chlorophyll-a (ug-chl-a/L)

0 0

Feb-98 Feb-99 Feb-00 Feb-01 Feb-02 Feb-03 Feb-04

BASE PO4 (0m) BIO3 PO4 (0m) BASE chl-a (0m) BIO3 chl-a (0m)

Figure 5-26: Comparison of PO4 and chlorophyll-a concentration in the epilimnion for the base case and BIO3 case

Figure 5-27: Comparison of PO4 phytoplankton uptake for base case and BIO3

Similar outcomes were found in the comparison between PO4 concentrations and chlorophyll-a in the epilimnion as illustrated in Figure 5-26. A slower growth rate corresponds to less PO4 uptake by phytoplankton and thus higher PO4 concentrations than the base case. The PO4 concentration decreases when algal concentrations increased because of higher uptake rates of phosphorous. The

PO4 uptake rates (Figure 5-27) had an average reduction of about 70% for the simulation of BIO3, while BIO1 and BIO2 returns an average reduction of 25 % and 45 %, exclusive of the flood event.

58 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Comparison of algal concentrations in lower hypolimnion

10

8

6

ug-chla/L 4

2

0 21-Feb-98 21-Feb-99 21-Feb-00 20-Feb-01 20-Feb-02 20-Feb-03 20-Feb-04

BASE chl-a (70m) BIO1 chl-a (70m) BIO2 chl-a (70m) BIO3 chl-a (70m)

Figure 5-28: Algal concentrations in the lower hypolimnion for the base case, BIO1, BIO2 and BIO3 simulations

In the hypolimnion (70m below the surface) the BIO3 simulation reduced peak algal concentrations from 40-83 %. BIO1 appeared to have no impacts on algal concentrations while the BIO2 strategy significantly lowered the peak concentrations by an average of 40 %. The overall trend of algal concentrations was high winter levels and low in summer. The following years had the same peak concentrations for the base case and simulated cases of BIO1-3.

Dissolved inorganic nitrogen and phosphorous concentrations in the hypolimnion were higher than the base case because of reduced growth and less nutrient uptake. The BIO1 case showed the least increase of dissolved inorganic nutrients while BIO3 showed the largest deviation from the base case.

59 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

DIN comparison with chlorophyll-a in the hypolimnion

0.7 10

9 0.6 8

0.5 7

6 0.4

5

0.3 4 DIN (mg-N/L)

3 0.2 Chlorophyll-a (ug-chla/L) 2 0.1 1

0 0

Feb-98 Feb-99 Feb-00 Feb-01 Feb-02 Feb-03 Feb-04

BASE DIN (70m) BIO3 DIN (70m) BASE chl-a (70m) BIO3 chl-a (70m)

Figure 5-29: Comparison of dissolved inorganic nitrogen (DIN) and chlorophyll-a for the base case and the BIO3 simulation case in the hypolimnion

PO4 comparison with chlorophyll-a in hypolimnion

0.1 10

0.09 9

0.08 8

0.07 7

0.06 6

0.05 5

0.04 4 PO4 (mg-P/L) 0.03 3 Chlorophyll-a (ug-chla/L) 0.02 2

0.01 1

0 0

Feb-98 Feb-99 Feb-00 Feb-01 Feb-02 Feb-03 Feb-04

BASE PO4 (70m) BIO3 PO4 (70m) BASE chl-a (70m) BIO3 chl-a (70m)

Figure 5-30: Comparison of dissolved inorganic phosphorous (PO4) and chlorophyll-a for the base case and the BIO3 simulation case in the hypolimnion

60 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

The general conclusion from these biomanipulation simulations was:

1) BIO1, or a 10% increase in respiration rate, had almost no impact on algal concentrations in the water column.

2) Simulations of BIO2, or a 25% increase of respiration rate, suggest an increase of peak concentrations in the epilimnion by up to 35%. However, in the hypolimnion, BIO2 simulations were found to reduce algal concentrations by an average of 40%.

3) The BIO3 case had the most significant effect of increasing peak algal concentrations by an average of 35% during 2000 to 2004 but also reduced hypolimnion concentrations by 40 – 80%.

4) Although BIO1-BIO3 induced higher peak algal concentrations in subsequent winter years but exhibited lower algal concentrations at other periods.

For the purpose of controlling or reducing the algal population in surface waters for health and aesthetic purposes, the simulation concluded that the biomanipulation strategy may produce generally lower algal concentrations, except for winter months, when observed over a long-term period. However, if the aim is to produce high water quality for drinking purposes, this strategy may prove useful when used in conjunction with selective withdrawal if water was extracted from the hypolimnion with lower algal concentrations due to biomanipulation but is hindered by the increase of dissolved inorganic nutrient concentrations in the water column.

Among the limitations of these simulations is that the model does not simulate actual zooplankton predation due to lack of zooplankton data and the level of complexity involved. The simplistic approach here of increasing respiration rates to simulate biomanipulation may not produce the same conclusions if accurate zooplankton modelling was possible because of the complex interaction between trophic levels. The overall reduction of algal growth in the simulations may not be realistic as zooplankton species have preferences over specific phytoplankton groups. This preference may lead to the dominance of certain phytoplankton groups depending on the grazing pressure induced in the system.

61 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

6 RESULTS AND DISCUSSION – PROSPECT RESERVOIR

6.1 Destratification by bubble plume and artificial mixing

The destratification of the water column in Prospect Reservoir is simulated with bubble plumes through bed diffusers and artificial mixing by surface mechanical mixers (or impellers). The main objective of destratification is to induce mixing in the water column to increase DO levels in the hypolimnion, which is usually low in stratified conditions. Theoretically, an increase in dissolved oxygen levels will lead to an improvement in water quality by suppressing phosphorous, manganese and iron release from the sediments (Burns 1994). This section describes the effects of both these destratification technologies through evaluation with simulations for Prospect Reservoir.

The base thermal profile in Prospect Reservoir shows slight stratification with a temperature difference of up to 4°C between the hypolimnion and epilimnion, which occurs after the mixing events in winter. This occurrence of stratification occurs from August to February with little signs of stratification after February. Winter temperatures are about 12°C throughout the water column with summer temperatures ranging from 16°C in the epilimnion to 20°C in the hypolimnion during the strongest stratification conditions

In Figure 6-1, the base case (without destratification) demonstrates that the drop in dissolved oxygen levels during summer stratification prevents mixing of oxygen from surface layers to bottom layers. Simulations with the destratifiers indicated a break down of the thermocline during the spring and summer periods that resulted in complete mixing of the water column. Dissolved oxygen concentrations in the bottom layers of the reservoir (Figure 6-1) show that both systems demonstrate the same level of efficiency with about a 40 % DO increase of the bottom layers during stratification, which corresponds to an increase from about 5 mg/L to 8 mg/L.

Simulations of the implemented destratifiers indicate a break down of the thermocline during the stratification periods of September to December, allowing the mixing of the epilimnion and hypolimnion. The observed trend after the implementation of destratification devices indicate that there is a decrease in DO levels occurring from the onset of spring in September and an increase in autumn (April). This trend of lower DO levels in summer and higher DO levels in winter is due to the wind stress which is lower in summer and higher in winter. The more windy conditions in winter increases the rate of absorption of DO from the atmosphere into the surface water, thereby generally increasing the DO levels in water column. The less windy conditions in summer leads to less oxygen incorporation into the surface mixed layer, hence the overall lower DO levels compared to the winter periods.

62 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

DO concentration in hypolimnion

11

10

9

8

7

6 DO (mg/L) 5

4

3

2 14-Mar-02 13-Jun-02 12-Sep-02 12-Dec-02 13-Mar-03 12-Jun-03 11-Sep-03 11-Dec-03 11-Mar-04

Base (without destratification) Bubbler Impeller

Figure 6-1: Comparison of dissolved oxygen (DO) concentrations at bottom waters, 17m from the surface for the base case, bubble plume destratification scenario and impeller destratification scenario

Several trials were run by varying numbers of diffusers and ports (for bubblers) and different numbers of impellers to observe the efficiency of each destratification system. It was found that a combination of 6 diffusers with 120 ports and an airflow rate of 3 m3/s, for the bubble plume simulations, were sufficient to maintain well-mixed conditions in the reservoir throughout the stratification period. The surface mechanical mixers (or impellers) required up to 50 impellers with a length of 17m and a diameter of 5 m and a water flow rate of 5 m3/s to mix the water column to about 19m.

Evaluation of the simulation outcomes indicated that the destratification strategies were successful in:

i. Preventing reducing conditions

ii. Decreasing concentration of dissolved inorganic nitrogen

iii. Decreasing PO4 flux from sediments within the hypolimnion during artificial destratification in summer periods. This confirms the studies and observations made by Burns (1994) and Bowersox (2000).

Preventing reducing conditions

63 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Dissolved iron (Fe2+) comparison in hypolimnion

0.025

0.02

0.015

Fe (mg/L) 0.01

0.005

0 13-Mar-02 18-Mar-02 23-Mar-02 28-Mar-02 2-Apr-02 7-Apr-02 12-Apr-02

Base (without destratification) Bubbler Impeller

Figure 6-2: Comparison of iron concentration in hypolimnion for base case, bubbler and impellers

Dissolved manganese (Mn2+) concentration in hypolimnion

0.01

0.008

0.006

Mn (mg/L) 0.004

0.002

0 11-Mar-02 10-Apr-02 10-May-02

Base (without destratification) Bubbler Impeller

Figure 6-3: Comparison of manganese concentration in hypolimnion for base case, bubbler and impellers

64 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Figure 6-2 and Figure 6-3 demonstrate the effectiveness of destratification systems in reducing the amount of iron and manganese in the hypolimnion in the first few months because subsequent concentrations were zero. The iron and manganese concentrations are maintained at low concentrations in conditions of high dissolved oxygen content in water correlating to findings by Burns (1994) and Bowersox (2002). The peak of iron and manganese in the beginning of the simulation corresponds to a low DO concentration in the water which caused anoxic and thus reducing conditions allowing the release from the sediments. The bubbler is more effective at reducing iron and manganese concentrations in the hypolimnion compared to the impeller as it was able to reduce the concentrations at a faster rate.

Decreasing concentration of dissolved inorganic nitrogen

NH4 concentrations at bottom (17m from surface) NO3 concentrations at bottom (17m from surface) 0.04

0.016 0.03 0.012

0.02 0.008 NO3 (mg-N/L) NHG4 (mg-N/L) 0.004 0.01

0 5-Nov-01 24-May- 10-Dec- 28-Jun- 14-Jan- 1-Aug-04 0 02 02 03 04 14-Mar-02 12-Sep-02 13-Mar-03 11-Sep-03 11-Mar-04

Base case Bubbler Impeller Base case Bubbler Impeller

(a) (b)

Figure 6-4: Comparison of the effects of destratification systems on (a) NH4 in the hypolimnion; and (b)

NO3 in the hypolimnion

(a) (b)

Figure 6-4 clearly show a reduction of NH4 and NO3 concentrations in the hypolimnion during the destratification period occurring in summer, though decreases are not large. Because Prospect Reservoir mixes during the winter there was little change during winter periods. This is most likely due to suppression of dissolved inorganic nutrients being released from the sediments as an effect of increased DO levels, a result also confirmed by Burns (1994) and Bowersox (2002).

65 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Figure 6-5: Comparison of DONL flux magnitude and trend before and after destratification.

Figure 6-6: DO levels for base case and impeller destratification case

Figure 6-5 illustrates DONL release from sediments for base case and the destratification case differs slightly with the base case exhibiting a higher flux than the impeller case. The relationship between DONL and DO can be seen by comparing the max and mins with those in the DO graphs in Figure 6-6.

Throughout the rest of the simulation, the destratification systems demonstrates a more variable flux of nutrient release especially during periods of lower DO levels associated with summer. This suggests that although the destratification systems are capable of fully mixing the water column, its level of effectiveness in destratification of the thermocline in summer is not as high as during winter.

66 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Total nitrogen at surface Total nitrogen (TN) concentrations at bottom 0.33 (15m from surface) 0.33 0.31 0.31 0.29 0.29

0.27 TN (mg-N/L) 0.27 TN (mg-N/L)

0.25 0.25

0.23 0.23 14-Mar-02 12-Sep-02 13-Mar-03 11-Sep-03 11-Mar-04 14-Mar-02 12-Sep-02 13-Mar-03 11-Sep-03 11-Mar-04

Base 0m Bubbler Impeller Base case Bubbler Impeller

Figure 6-7: Total nitrogen (TN) concentrations at (a) epilimnion; and (b) hypolimnion for base case, bubble plume destratification (bubbler) and artificial mixing (impeller)

Comparisons between the base case and destratification scenarios indicate that the bubbler is more effective in reducing the nitrogen levels than the impellers. The overall trend after application of bubblers was generally lower than the base case while the impeller cases were mostly greater. Further, at the beginning of the simulations a peak and then sudden drop in total nitrogen concentrations was predicted, whereas the bubbler reduced this peak at a faster rate than the impellers. Similar simulated dynamics were made for the peaks of total phosphorous at the beginning of the simulation as seen in Figure 6-8. The effect of the destratifiers on total phosphorous followed the same trend as that of total nitrogen.

Total phosphorous (TP) concentrations at bottom Total nitrogen concentrations at bottom (15m (15m from surface) from surface)

0.016 0.28

0.012 0.27

0.008 0.26 TN (mg-N/L) 0.004 TN (mg-N/L) 0.25

0 14-Mar- 19-Mar- 24-Mar- 29-Mar- 3-Apr-02 8-Apr-02 13-Apr- 0.24 02 02 02 02 02 14-Mar-02 19-Mar-02 24-Mar-02 29-Mar-02 3-Apr-02 8-Apr-02 Base case Bubbler Impeller Base case Bubbler Impeller

Figure 6-8: Effectiveness of bubble plume versus mechanical mixers in reducing peak nutrient levels for (a) total phosphorous; and (b) total nitrogen

Decreasing PO4 flux from sediments

67 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

PO4 comparison before & after destratification at bottom layers

0.0045

0.004

0.0035

0.003

0.0025

0.002 PO4 (mg-P/L)

0.0015

0.001

0.0005

0 21-Feb-02 22-Aug-02 20-Feb-03 21-Aug-03 19-Feb-04

Base (without destratification) Bubbler Impeller

Figure 6-9: Comparison of the effect of bubblers and impellers on PO4 concentration in hypolimnion

Figure 6-9 clearly demonstrates the reduction of PO4 during the destratification in summer periods because of the maintenance of complete mixing in the water column, which reduced anoxic conditions with resultant lower phosphorous release from the sediments. However, the destratification system also caused an increase in PO4 during winter periods where destratification was still applied over the winter mixing events. Hence, a trial simulation to apply destratification only during the summer and not the well-mixed winter period was conducted. Outcomes of this trial resulted in a scenario with no increase in PO4 during winter, but it was also less effective in PO4 suppression during summer. Furthermore, the summer application of destratification simulation resulted in almost no change of total nutrient concentrations in the epilimnion and hypolimnion unlike the whole year destratification scenario.

In general, the simulations of destratification systems concluded that they not very effective in reducing the total nutrient in the water column although the bubble plume proved to be more efficient than the mechanical mixers. The reduction of total nutrient concentrations was not significant given the number of destratifiers that was simulated. The cost of implementing these destratifiers does not justify the small changes in water quality that may occur. Again, the excellent water quality in Prospect Reservoir currently precludes justification of these measures.

68 Assessment of Water Quality Using Numerical Models Results and Discussion – PR 6.2 Sediment treatment by chemicals

The sediment treatment by chemicals such as iron chloride or iron sulphate dosing was aimed at reducing the nutrient fluxes from the sediments. This was simulated in DYRESM-CAEDYM by the alteration of nutrient flux parameters.

It was found that PO4 concentrations at the bottom and surface waters peak during winter. PO4 accumulates in the hypolimnion during stratification from higher sediment release rates because of low DO conditions during summer stratification, which is then mixed throughout the entire water column during the winter. Comparisons of PO4 concentration in water column between the base case

(without sediment treatment) and after simulations of 50% decrease of PO4 flux indicated that peak concentrations at bottom waters decreased a little in the first year but returned to base case concentrations during the second year ( Figure 6-10). However, the surface PO4 concentration decreased about 20% (after sediment treatment) after winter mixing.

(a) (b)

Figure 6-10: PO4 concentration in water column at depth 15m from surface for (a) base case and (b) sediment treatment scenario

69 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

(a) (b)

Figure 6-11: PO4 concentration in water column at surface for (a) base case and (b) sediment treatment scenario

2 2 The 50% decrease of PO4 release rate from sediments from 0.0026 g/m /day to 0.0013 g/m /day -5 -6 resulted in a 50% decrease of PO4 flux in the bottom waters from 1.5 x 10 mg-P/L/day to 8 x 10 mg-P/L/day (

(a) (b)

Figure 6-12) and 1.2 x 10-6 mg-P/L/day to 6 x 10-7 mg-P/L/day at the surface (Figure 6-13). Higher sediment release rates of PO4 from the bottom sediments than the top sediments resulted because of the low DO levels in the sediments in the deeper locations of the reservoir.

(a) (b)

70 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Figure 6-12: PO4 flux from sediments in water column at depth 15m from surface for (a) base case and (b) sediment treatment scenario

(a) (b)

Figure 6-13: PO4 flux from sediments at surface for (a) base case and (b) after sediment treatment

Figure 6-13 shows an increase in PO4 sediment flux in the sediment treatment scenario, at the surface, which was not found in the bottom waters of the same simulation. The sediment flux of PO4 in the model was dependent on dissolved oxygen levels only, as the model was configured not to consider pH. The increase in the PO4 sediment flux was from a decrease in dissolved oxygen concentrations. Figure 6-14 shows the concentration of dissolved oxygen at 0m and 15m depth, which illustrates no decrease in DO and thus the phosphorous release rate.

Figure 6-14: Dissolved oxygen (DO) levels for sediment treatment scenario at 0m and 15m from the surface

71 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

PO4 at surface 0.004

0.003

0.002 PO4 at bottom(15 m romf surface)

PO4 (mg-P/L) 0.001 0.004

0 0.003 0200 400 600 800 Base Case Sedimenttre atme n tscenario 0.002 PO4 (mg-P/L) 0.001

0 0200400600800 Base asec Sediment treatment

(a) (b)

Figure 6-15: Comparison of PO4 concentrations at the surface waters for the base case and the sediment treatment scenario

The small difference in PO4 concentrations between the base case and the sediment treatment scenario in Figure 6-15 highlights that sediment release does not greatly influence the overall phosphorus dynamics of Prospect Reservoir. Because of the relatively small influence of sediment release on the overall PO4 dynamics, the decrease of PO4 sediment flux also had no impact on algal growth, where the decrease of available filterable reactive phosphorous had not significant effect on algal dynamics.

Again, the decrease in the PO4 sediment flux had only a slight decrease in PO4 levels in the water column. Other than the decrease of PO4 flux and slight decrease in PO4 concentrations, the phosphorous dynamics do not change except for the slight increase in PO4 sediment flux in Figure 6-13 (b). POPL and DOPL concentrations remain unchanged and the same goes for other processes such as:

i. phytoplankton excretion of organic phosphorous,

ii. mineralization of dissolved organic phosphorous to filterable reactive phosphorous, and

iii. decomposition of particulate organic phosphorous to dissolved organic phosphorous

72 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Total Phosphor ous at surface

0.012

0.008

TP (mg-P/L) 0.004

0 0 200 400 600 800

Tim eseries (in J u lia n days) Bas e Ca s e Sediment reatt ment scenario

Figure 6-16: Comparison of total phosphorous in epilimnion for base case and sediment treatment scenario

TotlPhh tbt (15f f ) T P0.0 00

BaTseciasei iJ dSedm)enreamen i tt t t

Figure 6-17: Total Phosphorous (TP) in the hypolimnion for base case and sediment treatment scenario

Figure 6-16 and Figure 6-17 of the surface and bottom TP levels illustrate the lack of effect that sediment treatment had on the total phosphorous content of the water column. The slight decline in total phosphorous concentrations corresponds to winter mixing which increases overall oxygen content in the water column.

73 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

In conclusion, the sediment treatment scenario simulated by 50% decrease of PO4 flux from sediments is ineffective. The scenario was found to have no impact on the phosphorous dynamics in the simulation and does not affect the algae concentrations at the surface waters. This is because the sediment flux is a small flux compared to other processes in Prospect Reservoir.

6.3 BIOMANIPULATION IN PROSPECT RESERVOIR

The simulations of biomanipulation strategies for Prospect Reservoir were conducted over a period of 2 years to assess the impacts of increased zooplankton grazing in a closed system dominated by internal processes. The same methodology applied for simulations in Lake Burragorang were applied to Prospect Reservoir. The DYRESM-CAEDYM model was used to simulate the effect of increased grazing on phytoplankton by zooplankton through the increase of the respiration rate of phytoplankton leading to reduced algal growth rate.

As mentioned before, there were 3 simulations to determine the effects of increasing respiration rate by 10, 25 and 50%. For future references, these are termed BIO1 (10% increase in respiration rates), BIO2 (25% increase in respiration rates) and BIO3 (50% increase in respiration rates). The simulations were run from February 2002 to February 2004, fully covering 2 winters and 1 summer period.

Figure 6-18 shows one of the outcomes of the simulations of algal concentrations (represented by chlorophyll-a concentration) in the epilimnion for the different specified cases. The general trend observed in all the simulations is the peak in winter periods of May to September and the low concentrations in the summer months of October to March. The peaks are related to mixing events that occur during winter due to increases wind stress mixing the water column and breaking the destratified profile. Nutrient-rich water that accumulated over the summer period is made available to the epilimnion when the thermocline is removed and becomes readily available for phytoplankton uptake causing the peak in algal concentrations. During summer, the warming of surface layers forms the thermocline that prevents the upward flow of nutrient-rich waters, hence the reduction of algal concentrations.

74 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

Chlorophyll-a concentrations in the epilimnion

7

6

5

4

3

Chlorophyl-a (ug-chla/L) 2

1

0 21-Feb-02 22-Aug-02 20-Feb-03 21-Aug-03 19-Feb-04

BASE Chl-a (0m) BIO1 Chl-a (0m) BIO2 Chl-a (0m) BIO3 Chl-a (0m)

Figure 6-18: Comparisons of chlorophyll-a concentration in the epilimnion for base case, BIO1, BIO2 and BIO3 cases.

Because of the reduced algal growth rate due to increased respiration rates, BIO1 to BIO3 cases exhibits reduced algal concentrations in the beginning of the simulations with BIO showing the least deviation and BIO3, the highest. The shift of peak algal concentrations to a later period found in the Lake Burragorang simulations of biomanipulation was observed in the first winter period in Prospect Reservoir. The slower growth of phytoplankton causes a decrease in nutrient uptake, leading to an increase of dissolved inorganic nutrients in the water column as depicted in Figure 6-19 and Figure 6-20. The increase in available nutrient concentrations caused a higher peak for BIO1 to BIO3 than the base case during the winter mixing events. The shift to the right for peak concentrations is due to the reduced algal growth and the sudden peak occurs when conditions are favourable, such as warmer conditions at the later period. The base case and all the simulated cases decline at the same time, indicating that thermal stratification had formed, preventing nutrient entrainment from hypolimnion.

The second year of the simulations exhibits a different trend, compared to Lake Burragorang, where the shift to the right is no longer detected in the peak concentrations of BIO1 and BIO2 cases. Instead, BIO1 and BIO2 cases are seen to decrease in the period leading to the peak, same as the first year, and continues to stay lower than the base case, leading to a reduced peak concentration with BIO2 showing most reduction. The BIO3 case still shows the shift to the right with the increased peak concentration much higher than the base case.

75 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

DIN comparison with chlorophyll-a in the epilimnion

7 0.16

6 0.14

0.12 5 0.1 4 0.08 3

0.06 DIN (mg-N/L) 2 Chlorophyll (ug-chla/L) 0.04

1 0.02

0 0

Feb-02 Aug-02 Feb-03 Aug-03 Feb-04

BASE Chl-a (0m) BIO1 Chl-a (0m) BIO2 Chl-a (0m) BIO3 Chl-a (0m) BASE DIN (0m) BIO1 DIN (0m) BIO2 DIN (0m) BIO3 DIN (0m)

Figure 6-19: Comparison of dissolved inorganic nitrogen (DIN) and algal concentrations in the epilimnion for the base case, BIO1, BIO2 and BIO3

PO4 comparison with chlorophyll-a in the epilimnion

7 0.0045

0.004 6 0.0035 5 0.003

4 0.0025

3 0.002 DIN (mg-N/L) 0.0015 2

Chlorophyll-a (ug-chla/L) 0.001 1 0.0005

0 0 2 3 2 0 3 0 4 -0 - -0 - -0 b g b g b e u e u e F A F A F

BASE Chl-a (0m) BIO1 Chl-a (0m) BIO2 Chl-a (0m) BIO3 Chl-a (0m) BASE PO4 (0m) BIO1 PO4 (0m) BIO2 PO4 (0m) BIO3 PO4 (0m)

Figure 6-20: Comparison of PO4 and algal concentrations in the epilimnion for the base case, BIO1, BIO2 and BIO3

76 Assessment of Water Quality Using Numerical Models Results and Discussion – PR

In the base case and all the simulated cases, Figure 6-19 and Figure 6-20 shows the relation between the increase in algal concentrations and the subsequent decrease in dissolved inorganic nutrient concentrations. Looking at Figure 6-21, the phytoplankton uptake of NO3for the BIO3 case depicts the shift and the reduction of uptake while the BIO2 case only exhibits this pattern in the first year. The subsequent year for BIO2 simulation deviates from this trend and has the same average uptake rate as the base case. The same was found for the BIO1 case as well as for PO4 uptake. The NH4 uptake by phytoplankton was reduced for all cases by up to 80% which was found to be very different from the

Lake Burragorang simulations which had no differences in NH4 uptake.

Figure 6-21: Comparison of NO3 phytoplankton uptake between (a) base case and BIO2; and (b) base case and BIO3

The differences in the trends of biomanipulation simulations between Lake Burragorang and Prospect Reservoir suggests that biomanipulation strategies implemented in Prospect Reservoir, which is dominated by internal processes, will have different impacts compared to strategies applied to Lake Burragorang, which is subject to external forcing. The impacts of reducing algal growth in Prospect Reservoir in the 2 year simulations are not conclusive and require further simulations over the long- term. More importantly, the difference that biomanipulation has on open and closed systems indicate that Prospect Reservoir will have to be assessed further on its suitability for this management strategy because it serves as an emergency storage and supply reservoir. Hence, Prospect Reservoir may be subject to inflows during times of emergency or may be required to act as a back-up water supply during periods of high demand, which may change the dynamics from an internally dominated system to an externally dominated one.

77 Assessment of Water Quality Using Numerical Models Conclusions

7 CONCLUSION

The most obvious result of all the simulations is that the water quality in Lake Burragorang and Prospect Reservoir has been maintained at high quality. Most of the simulated management strategies had little to no impact on the improvement of water quality especially if weighed against the costs of implementation. For Lake Burragorang, the simulated strategies included the evaluation of an adaptive selective withdrawal approach, catchment management to reduce the amount of nutrient input into the reservoir as well as an evaluation of increased nutrient input scenario. Both reservoirs were subject to simulations of biomanipulation strategies to assess the impacts of food web manipulation. Prospect Reservoir was assessed for simulations of application of destratification strategies, namely bubble plume destratification and artificial mixing. The effect of sediment treatment by chemicals was also analysed for Prospect Reservoir.

An adaptive management strategy of selective withdrawal was found to be the best option for the extraction of high quality water. The findings of this thesis confirmed that the current selective withdrawal strategy practiced in Lake Burragorang provided the best water quality when compared to various versions of simulated withdrawal from different water outlets. Further findings concluded that a reduction of up to 80% of nutrients from catchment levels into the reservoir would be required to cause significant impact on the reservoir water quality while an increase of nutrient input by up to 50% has almost no effect on the nutrient concentrations in water column.

The simulated application of destratification technologies and sediment treatment concluded in results that do not justify the cost of implementation because of the insignificant changes in water quality. The biomanipulation strategies for both reservoirs require further investigation into the feasibility of this approach in the maintenance or further enhancement of high quality drinking water supply to Sydney.

While the general water quality in Lake Burragorang was of good conditions, there is still a problem of inflows of high levels of suspended solids during periods of high inflows. A possible approach is the implementation of silt trapping curtains that act to prevent the influx of suspended material to the dam wall. This requires a three-dimensional hydrodynamic-ecological model such as ELCOM- CAEDYM.

78 Assessment of Water Quality Using Numerical Models Conclusions

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