Water quality modelling for Gardens by the Bay, Singapore€¦ · a change in the concentration from Marina Bay Reservoir is less ampliﬁed and is around 0.05 mgL 1for total phosphorus

Water quality modelling

for Gardens by the Bay, Singapore

G. Pijcke

May 23, 2014

[email protected]

National University of Singapore Delft University of TechnologyAssoc.Prof.dr.ir. Vladan Babovic Prof.dr.ir. N.C. van de Giesen

Dr. Stéphane Bayen Prof.dr.ir. A.W. Heemink

Kalyan C. Mynampati Dr.ir. G.H.W. Schoups

Water quality modelling

for Gardens by the Bay, Singapore

G. Pijcke

Graduation committee

National University of Singapore Delft University of TechnologyAssoc.Prof.dr.ir. Vladan Babovic Prof.dr.ir. N.C. van de Giesen

Dr. Stéphane Bayen Prof.dr.ir. A.W. Heemink

Kalyan C. Mynampati Dr.ir. G.H.W. Schoups

A dissertation submitted in partial fulfillment of the degree of Master of ScienceHydraulic Engineering and Water Resources Management

April 2014

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Abstract

Eutrophication is a natural process that describes the development of a pristine olig-otrophic water body into a nourished eutrophic system. Anthropogenic influences canaccelerate the eutrophication process with negative consequences for aquatic life andbiodiversity as a result. Water quality management for lakes in tropical regions getscomplicated by all year round high temperatures and catchment inflows of high volumeand intensity. Water quality modelling studies assist in understanding the water qualityprocesses that take place in natural water bodies and are relevant for forecasting andimpact assessment studies. This thesis aims to model the water quality for the tropicalshallow lake in Gardens by the Bay, Singapore. The model will be used in the future forthe above mentioned purposes.

Monitoring data is an indispensable source of information for the representativenessand reliability of water quality models. First of all, this involves monitoring data forinternal and external loads for the respective water body as these drive the water quality.A model’s ability to represent the water quality accurately depends on the quantity andquality of the available data. Secondly, monitoring data from at least one location in awater body is required for model calibration and validation purposes. This study hasaddressed the first issue by doing measurements for external inflows in Gardens by theBay. This has helped to reduce uncertainties in Because of the importance of goodquality data with respect to modelling studies, this study has taken an experimentalapproach by which two main data related issues were addressed. This has resulted incharacterisation of the external load of the Gardens by the Bay lake system with the aimto reduce model uncertainties. Secondly, the integrity of water quality data collectedby two online water quality monitoring stations in Gardens by the Bay was verified bycomparing the measurements with an independent, well-calibrated measurement devicemeasuring the same water quality characteristics.

Nutrient inflows into the lake system in Gardens by the Bay exceeds threshold valuesfor total nitrogen (1 mg/L) and total phosphorus (0.06 mg/L) by Singapore’s PublicUtility Board (PUB) for all measured events at all locations. The load from point-sources in kg/yr and kg/yr/ha is highest. This is assuming a 45% reduction for thenutrient load for filter bed locations and bioswales.

Student’s t-test shows there are significant differences between pH and dissolved oxy-gen measured by the monitoring stations and the verification probe. The instrument biasmeasured at the end of the calibration period is smaller than the bias between the verifi-cation instrument and the monitoring station. This suggests that the sensor response isnot only affected by an instrumental drift. The bias between the measurements is presentfrom the beginning and only changes significantly for dissolved oxygen for the monitor-ing station in Kingfisher Lake and electronic conductivity for the monitoring station inDragonfly Lake according to Mann-Kendall’s test. However, the presence of initial biassuggests that also environmental drift is not able to fully explain the deviation betweenmeasurements by the monitoring station and those by the verification instrument.

A combined hydrodynamic model in Delft3D and water quality model in DELWAQwas used to model water column temperature [oC], dissolved oxygen [mgL−1], chlorophyll-a [µgL−1], total nitrogen [mgL−1] and total phosphorus [mgL−1]. The model is ableto represent the water column temperature at both locations accurately with underes-

G. Pijcke MSc thesis

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timation during distinct periods. The fluctuation in the concentration for dissolvedoxygen, chlorophyll-a, total nitrogen and total phosphorus are not well captured bythe water quality model. The model shows a gradual change in the concentration fordissolved oxygen, chlorophyll-a, total nitrogen and total phosphorus from KingfisherLake through the Saraca Channel into Dragonfly Lake. The concentration differencesin Kingfisher Lake and between the end-point of Saraca stream to the outflow at Drag-onfly Lake are small. Vertical concentration differences for the modelled variables atthe deepest location (4 metres) in Dragonfly Lake are highest for dissolved oxygen, 0.20mgL−1, and chlorophyll-a, 0.37µgL−1.

The monthly external load of the system is larger than the total nutrient outflow.There seems to be no increase of the amount of nitrogen and phosphorus kept in thewater column. As a results it follows that nutrients are accumulating in the biomassand sediment in the lakes in Gardens by the Bay. Accumulation of nutrients in thesediment could lead to higher internal nutrient loading in the future and thereby higherconcentrations in the water column.

A sensitivity analysis shows that the water quality is responsive to changes in thecatchment loading. Scenario WQ03-01 shows that when the filter bed locations areassumed to have zero inflow, the total phosphorus concentration in Dragonfly dropswhereas the total nitrogen concentration increases. This is due to the higher rela-tive influence the inflow from the Frog Pond has on the total nitrogen concentrationas compared to the total phosphorus concentration. Kingfisher Lake is relatively non-responsive to such changes because most inflow locations are located downstream fromthe lake. Changes in the nutrient concentration from the inflow from Marina Bay Reser-voir are almost followed exactly in Kingfisher Lake. In Dragonfly Lake, the influence ofa change in the concentration from Marina Bay Reservoir is less amplified and is around0.05 mgL−1 for total phosphorus and 0.40 mgL−1 for total nitrogen.

Results from the measurements as well as the modelling study give input for contin-uation of the research efforts for Gardens by the Bay project. First of all, the measurednutrient loads should be verified against the land-use types in the associated catchmentsas well as fertilizer application in those catchments. The influence inflow from theFrog Pond has on the water quality in Kingfisher Lake could be verified by compar-ing measurements in the Frog Pond with those in Kingfisher Lake. The accumulationof nutrients in the sediment layer is to be verified through determination of nutrientconcentrations in the sediment samples for a certain period of time.

The model’s sensitivity for time-varying water quality data could be verified by usingthe six inflow measurements at the Frog Pond and Rainforest Lily Pond for a simulationfor the period 26 November 2013 till 6 January 2014. This would results in additionalinformation with respect to the response of Kingfisher Lake to the characteristics ofthis inflow location. The model results show hardly any differences for important waterquality variables in the vertical. Also, there are hardly any spatial differences observedfor the water quality in Dragonfly and Kingfisher Lake. Three dimensional modellingdoes not seem necessary to model the water quality for the shallow lakes accurately.It is of importance to assess the occurrence of more dominant spatial differences fortime-varying input data which currently is lacking in the model. If differences in thevertical remain small, the model set-up could possibly be simplified whereby Dragonflyand Kingfisher Lake can be assumed to behave as two fully-mixed reservoirs.


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Acknowledgments

The completion of this work would not have been possible without the help of many.I am grateful to all who have had a contribution by being a source of inspiration, bygiving guidance and direction, by providing insight and assistance, or by expressingtheir support in me throughout the period this thesis work was fulfilled.

First of all I would like to address the members of my graduation committee, Profes-sor Nick van de Giesen, Professor Arnold Heemink and Dr. Ir. Gerrit Schoups from TUDelft and Associate Professor Vladan Babovic, Dr. Stéphane Bayen and Dr. Kalyan C.Mynampati who’s guidance and ideas have greatly benefited this thesis. I would like toaddress Kalyan specifically for the daily supervision I received from him.

Thankful I am as well to all colleagues in Singapore Delft Water Alliance who havebeen attentive in helping me throughout the period I worked on this thesis. Siaw FunChen showed me how to carry out the quality control of the water quality monitoringstations and taught me most of the laboratory work that had to be done within the scopeof this thesis. Carol Han has been of great help in the laboratory by sharing her ex-perience, answering the many questions I had and for carrying out part of the sampleanalysis for this project. At last I am greatly thankful to Umid Joshi Man who grantedme access to his lab facilities and who shared with me his views on the field work inGardens by the Bay. Alam Kurniawan and Serene Tay have been of great help for theirexperience with hydrodynamic modelling and Jingjie Zhang’s knowledge was greatlyappreciated during the development of the water quality model.

A special address is made as well to Ghada El Serafy from Deltares with whom I haddiscussion for the modelling part of this study. In addition I would like to specificallymention her colleague, Firmijn Zijl (Deltares), for the necessary advice on heat fluxmodelling in Delft3D Flow.

Furthermore I am thankful to Gardens by the Bay, Andrea Kee specifically, for pro-viding me a seat in their office while I was carrying out the fieldwork. Furthermore,Derek Wee has given a lot of input about the functioning of the lake system which wasrelevant for the completion of this work.

I would like to make use of the opportunity as well to say thanks to Cecilia Dewi,program coordinator of the double degree program jointly offered by NUS and TUDelft, for her great help from the moment I decided to participate in this program. Inthis respect, I would also want to mention Dr. Ir. Wim Luxemburg and AssociateProfessor Vladan Babovic once more, as they have been the key persons that got meonto the journeys to Singapore and The Netherlands back and forth.

Words of great thanks are also going out to my family who have not only been ofsupport while I was working on my thesis, but who have been of unconditional supportthroughout the last six years I have been studying, throughout the last 23 years of mylife. They are the basis of where I am standing now.

Also I want to mention my friends, for their presence and support, for the talks andmany discussions. At last I would like to address my girlfriend for her unconditionalsupport throughout the completion of this work.


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Contents

Abstract 3

Acknowledgments 5

1 Introduction 81.1 Gardens by the Bay, Singapore . . . . . . . . . . . . . . . . . . . . . . 81.2 Relevance of study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.3 Purpose of study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.4 Report structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Uncertainty reduction 112.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.1.1 Gardens by the Bay lake system . . . . . . . . . . . . . . . . . 112.1.2 Limitations of the existing hydrodynamic model . . . . . . . . 112.1.3 Limitations of the existing water quality model . . . . . . . . . 11

2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.1 Data acquisition for the improvement of the existing hydrody-

namic model . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.2 Experimental study for the improvements of the existing water

quality model . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.3 Measurement and sampling practices . . . . . . . . . . . . . . 152.2.4 Interpretation of the results by ANOVA . . . . . . . . . . . . . 19

2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.1 Nutrient loading from the catchment . . . . . . . . . . . . . . . 202.3.2 Characterisation of the inflows by analysis of variance . . . . . 20

2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.4.1 Load calculations for surface water inflows . . . . . . . . . . . 24

3 Data correction 263.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1.1 Water quality monitoring in Gardens by the Bay . . . . . . . . 263.1.2 Integrity of data from the monitoring stations in Gardens by the

Bay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.1.3 Data cleaning and correction for environmental data . . . . . . 27

3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.1 Experimental study for the collection drift-free verification mea-

surements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.2 Statistical analysis for the comparison of verification measure-

ments with data from the monitoring stations . . . . . . . . . . 323.2.3 Spatial linear regression for the recovery of temperature data . . 32

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.3.1 Visual comparison of verification measurements with data from

the monitoring stations . . . . . . . . . . . . . . . . . . . . . . 343.3.2 Comparison of the mean: results of Student’s t-test . . . . . . . 34


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3.3.3 Detection of sloping trend in residual values: results of Mann-Kendall test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3.4 Comparison of the sensor response before and after calibration . 363.3.5 Results of spatial linear regression for temperature data . . . . . 43

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.5 Conclusions and recommendations . . . . . . . . . . . . . . . . . . . . 49

4 Water quality modelling for Gardens by the Bay 514.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.2.1 Hydrodynamic modelling . . . . . . . . . . . . . . . . . . . . 544.2.2 Water quality modelling . . . . . . . . . . . . . . . . . . . . . 58

4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.3.1 Results from the model test configurations . . . . . . . . . . . . 634.3.2 Results hydrodynamic modelling . . . . . . . . . . . . . . . . 634.3.3 Results water quality modelling . . . . . . . . . . . . . . . . . 664.3.4 Sensitivity analysis for the water quality model . . . . . . . . . 67

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824.4.1 Model predictive capacity . . . . . . . . . . . . . . . . . . . . 824.4.2 Discussion of the results from sensitivity analysis . . . . . . . . 824.4.3 Mass balances for total nitrogen and total phosphorus . . . . . . 83

4.5 Conclusions and recommendations . . . . . . . . . . . . . . . . . . . . 87

5 Recommendations and directions for future work 885.1 Recommendations for further reduction of modelling uncertainties . . . 88

5.1.1 Handling of samples . . . . . . . . . . . . . . . . . . . . . . . 885.1.2 Chlorophyll-a analysis . . . . . . . . . . . . . . . . . . . . . . 885.1.3 Total phosphorus analysis . . . . . . . . . . . . . . . . . . . . 895.1.4 Nitrite, nitrate and total nitrogen analysis . . . . . . . . . . . . 895.1.5 Phosphate analysis . . . . . . . . . . . . . . . . . . . . . . . . 895.1.6 Additional data collection . . . . . . . . . . . . . . . . . . . . 90

5.2 Recommendations experimental and statistical data correction . . . . . 905.2.1 Data correction by verification measurements . . . . . . . . . . 905.2.2 Data correction by linear regression . . . . . . . . . . . . . . . 90

5.3 Recommendations for further water quality modelling . . . . . . . . . . 915.3.1 Collection of time-series data in the Frog Pond . . . . . . . . . 915.3.2 Monthly Secchi disk measurement . . . . . . . . . . . . . . . . 925.3.3 Modelling water quality with time-varying input data . . . . . . 925.3.4 Assess the necessity of three-dimensional hydrodynamic and

water quality modelling . . . . . . . . . . . . . . . . . . . . . 92

References 94


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1 Introduction

1.1 Gardens by the Bay, Singapore

Singapore’s Gardens by the Bay is a one year old tropical garden located on artificialreclaimed land in the city’s Marina Bay area. The Gardens are spread over three dis-tinctive locations: Gardens at Marina South (Bay South), Gardens at Marina East (BayEast) and Gardens at Marina Centre (Bay Central). It opened its doors for the public inJuly 2012 and since then has served as place for leisure for Singapore residents and oneof the country’s important tourist draws.

Gardens by the Bay South is the largest of the three sites covering 54 hectares. Mostconspicuous are the two conservatories, Flower Dome and Cloud Dome, and twelvevertical structures (Supertrees) of 25 to 50 metres high, covered with planting panels toprovide them with a green and living surface. The site is further composed of a numberof thematic gardens, open green spaces, foot paths and a lake system.

The lake system consists of two major ponds, Dragonfly and Kingfisher Lake, con-nected through a channel that encompasses the garden area. Dragonfly and KingfisherLake communicate with the neighbouring Marina Bay Reservoir. The lake system cov-ers about five hectares and stretches a distance of about two kilometres.

1.2 Relevance of study

Tropical shallow lakes and ponds in Singapore mostly are eutrophic of nature. Highambient temperature causes high water column temperature which makes that the ratesof chemical and biological processes are high as compared to similar systems in temper-ate zones. In the recent past, the water quality of numerous reservoirs, lakes and pondsin Singapore has been examined. Examples include the Marina Bay Reservoir, Up-per Peirce Reservoir (Smits, 2007), Punggol-Serangoon Reservoir and the water bodylocated in East Coast Park.

In order to study the water quality for both lakes, a water quality monitoring programwas started in August 2012 in Dragonfly Lake and February 2013 in Kingfisher Lake.Since then the water quality monitoring stations have provided data for electronic con-ductivity [mS/cm], temperature [oC], dissolved oxygen [mgL−1], pH [-], chlorophyll-a[µgL−1] and turbidity [NTU] every ten minutes.

Water quality monitoring gives information about the real-time water quality of bothlakes. Modelling helps in addition to that as a tool to study (Bayen, 2012):

• water appearance and transparency, and impacts associated with high turbidity(brown water), high algal biomass, and algal scum;

• oxygen levels and potential deoxygenation events which may lead to fish killevents, odour production and enhanced internal loading;

• external pollutant loading associated with catchment land use (garden mainte-nance) practices, and their impacts on water column nutrient concentrations andin turn algae production;


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• human health concerns, such as the presence of natural cyanotoxins produced byalgal species, and bacteria biomass; and

• low or loss of ecological biodiversity and ecological habitats.

Furthermore water quality modelling can assist to study how the water quality isinfluenced for a variety of scenarios, such as extreme weather conditions, changes inthe pollutant loading and alternative lake flushing scenarios. It therefore is a useful toolfor water quality management purposes and the assessment of proposed operationalmeasures.

1.3 Purpose of study

This study aims to develop a water quality model for Gardens by the Bay. The followingobjectives are defined within the scope of this thesis:

1. reduce the uncertainty in the existing water quality model for more accurate rep-resentation of the physical processes and biochemical kinetics in the lake system;

2. correct the data from real-time online water quality monitoring stations in orderto improve the quality of data for which the model will be calibrated;

3. develop a three dimensional hydrodynamic model and assess the sensitivity of themodel for different catchment inflow quantities and intensities; and

4. develop a three dimensional water quality model that describes the water columnstates for dissolved oxygen, chlorophyll-a, total nitrogen and total phosphorus.

The uncertainty in the existing water quality model is addressed through an exper-imental study that has resulted in water quality input data for the model. The secondobjective addresses the integrity of data collected by the monitoring stations in Gardensby the Bay by an experimental approach. Furthermore it introduces a regression modelto correct faulty data and recover missing data. Objective three and four concern theexpansion of the existing water quality model (presented in Pijcke (2013) is a generaldissolved oxygen model according to Deltares (2011a)) into a more complete modelthat takes into account the interaction between important water quality variables. Thetarget state-variables include dissolved oxygen, chlorophyll-a, total nitrogen and totalphosphorus.

1.4 Report structure

This report is structured along the objectives that were identified for this study. Theintroduction is followed by an overview of the uncertainties in the existing water qual-ity model and addresses how this study deals with these uncertainties (chapter 2). Itexplains the methodology and discusses the results of the water quality data acquisi-tion carried out in the field and the laboratory. Chapter 3 deals with correction of datacollected by water quality monitoring stations in Gardens by the Bay. It presents theexperimental study that was carried out to identify the performance of the water qual-ity monitoring stations. Furthermore it shows how a regression model can be used for


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Figure 1: Uncertainty reduction and data correction are two steps preceding the final results of thisstudy: a water quality model for Gardens by the Bay.

the correction of water quality data. Chapter 4 shows the configuration of the waterquality model for Dragonfly and Kingfisher Lake and it shows the performance of themodel as compared to observation data. It also shows the results of a number of dif-ferent nutrient loading scenarios. Chapter four uses the results from the preceding twochapters. The results from chapter 2 (uncertainty reduction) yields input data for thewater quality model; the results from chapter 3 (data correction) yields additional in-formation for comparison of modelled data with processed, measured data. At last thisreport concludes with a number of recommendations and directions for the continuationof Gardens by the Bay project in chapter 5.


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2 Uncertainty reduction

This chapter starts with a description of the lake system in Gardens by the Bay. Thisis followed by an overview of the uncertainties in the existing water quality model andhow these are addressed in the current study. It describes the experimental design offield measurements and water sample analysis for the determination of the water qualitycharacteristics of catchment inflow locations. The results section gives an overview ofthe data that was obtained from the field, followed by an interpretation of the results inthe discussion section.

2.1 Introduction

2.1.1 Gardens by the Bay lake system

Gardens by the Bay South is schematised in figure 2. The lake system comprises twoponds, Kingfisher and Dragonfly Lake, which are connected through the Saraca stream.The lake system receives water from a 54 hectare large catchment of which 75% isin use for horticulture. In addition to catchment runoff, Kingfisher Lake indirectly re-ceives water from the Marina Bay Reservoir. Water from Kingfisher Lake flows throughthe channel to Dragonfly Lake. In Dragonfly Lake, water is withdrawn from the lakefor irrigation purposes in the garden. Surplus water spills from Dragonfly Lake backinto the Marina Bay Reservoir. Runoff from the catchment is entering at numerous lo-cations along Kingfisher Lake, Saraca stream and Dragonfly Lake through point andnon-point sources. Diffuse sources include inflows directly from the banks of the lakebut also comprises inflow through water sensitive urban design (WSUD) features suchas filter bed and lake edge filter bed structures and bioswales. Point inflows are pre-dominantly found in Kingfisher Lake and along the Saraca stream. The former receiveswater through the Frog Pond and Rainforest Lily Pond (figure 2). Water from the Ma-rina Bay Reservoir first enters the Frog Pond, from where it is directed into KingfisherLake.

2.1.2 Limitations of the existing hydrodynamic model

An existing two dimensional hydrodynamic model for Gardens by the Bay has the fol-lowing important limitations:

• incomplete overview of inflow locations and unknown inflow magnitude;

• unknown inflow magnitude from Marina Bay Reservoir;

• calibrated for measured temperature from Dragonfly Lake only; and

• meteorological forcing data from a different time period than the simulation timeframe.

2.1.3 Limitations of the existing water quality model

A conceptual description of the existing water quality model for Gardens by the Bay ispresented in figure 3. The following major limitations are identified in this model:


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Figure 2: Schematisation of Gardens by the Bay

• the model only addresses water column temperature and dissolved oxygen anddoes not give insight in any other important water quality variables such as chlorophyll-a and nutrient concentrations;

• the catchment inputs were unknown and were therefore assumed from Thompson(2008); and

• the model gives limited insight in the relevance of water quality processes andcannot be utilised effectively for scenario analysis.

In the existing model oxygen is consumed by mineralisation, nitrification and sedi-ment oxygen demand. The only source of oxygen comes from the reaeration process.Concentrations for ammonium and cBOD for inflows from the catchment are based onthe study by Thompson (2008). These values were based on assumptions with regard tothe expected land-use type and experience from similar environments.

Temperature and dissolved oxygen are important variables as they have a lot of influ-ence on processes that determine the water quality. However, their representation onlyis insufficient to understand the processes that determine the water quality in Gardensby the Bay. Therefore expansion of the water quality model was identified as one of thekey objectives of this study.

The paragraphs above show the major uncertainty in both the hydrodynamic modeland water quality model is associated with inflow locations and signifies the importanceof additional monitoring at catchment inflow locations to obtain information about theexternal loading entering Dragonfly and Kingfisher Lake.


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Figure 3: Existing water quality model for Gardens by the Bay with water column temperature anddissolved oxygen as output variables.


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2.2 Methodology

The uncertainties in the existing hydrodynamic and water quality model as identified inthe introduction, stresses the importance of information about characteristics of inflowlocations for understanding of the water quality processes in Gardens by the Bay. Forthe hydrodynamic model this mainly concerns the discharge amount and intensity andfor water quality it is the load of nutrients and contaminants coming into the lakes.Improvements in the hydrodynamic model are obtained through consultation of Gardensby the Bay staff members. Uncertainties in the water quality model are addressed bywater quality measurements and sample analysis for discharges from the catchment andfrom the Marina Bay Reservoir.

2.2.1 Data acquisition for the improvement of the existing hydrodynamicmodel

Improvements to the hydrodynamic model have been made based on additional infor-mation that was supplied by Gardens by the Bay. This included:

• updated drainage maps of Gardens by the Bay South;

• time-series data for pumping from Marina Bay Reservoir;

• time-series data for the inflow from the lake transfer system; and

• time-series data for extraction of water from Dragonfly Lake by the irrigationofftake.

The drainage maps assisted in finding outflow locations and for verification of thedivision in subcatchments in a study by Thompson (2008). Inflow from the Marina BayReservoir amounts 20 L/s for 24 hours per day. The irrigation offtake taps a variableamount from the Dragonfly Lake every day. A time-series was available starting from 01March 2013 till 31 October 2013. The relevant information for inflow from the MarinaBay Reservoir and the lake transfer system was provided on personal note by DerekWee (2013).

2.2.2 Experimental study for the improvements of the existing water qualitymodel

The uncertainty in the water quality model was reduced by measuring the water qualityat twelve locations (figure 4) for three wet (SW1, SW2 and SW3) and three dry events(SD1, SD2 and SD3). Inflow locations consist of point-sources (001, 002, 003, 004,005, 006 and 011) and non-point sources (007, 008, 009, 010, and 012). Locations 003,004, 005 and 006 are only active during and straight after rainfall. Location 001 hasa contribution from rainfall and is the location through which the inflow from MarinaBay Reservoir enters Kingfisher Lake. Through location 002, catchment runoff comesinto Kingfisher Lake as well as half of the contribution supplied daily to the lakes bythe lake transfer system. The non-point sources are those associated to filter beds and


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bioswales. Inflow from these locations is by submersion and by overflow during satu-rated conditions. Below presents a list of the data that was collected for each event andlocation:

• water column temperature [oC];

• electronic conductivity [mS/cm];

• pH [-];

• dissolved oxygen concentration [mg/L and %];

• total dissolved solids [g/L];

• salinity [ppm];

• Turbidity [NTU];

• chlorophyll-a [µg/L];

• ortho-phosphate [mg/L];

• nitrite [mg/L];

• nitrate [mg/L];

• total phosphorus [mg/L]; and

• total nitrogen [mg/L].

The first six of the listed variables were measured using a handheld water qualityprobe. Turbidity was measured using a Secchi tube. Chlorophyll-a, ortho-phosphate,nitrite, nitrate, total phosphorus and total nitrogen concentrations were determined fromsample analysis in the laboratory.

2.2.3 Measurement and sampling practices

Measurement of water quality variables by handheld multiprobe Measure-ments were carried out using a handheld probe with sensors for the above mentionedvariables. The measurement frequency was set at 30 seconds and the instrument de-ployed for at least five minutes at each location to obtain a minimum of twelve mea-surement for averaging. The first recording for each variable and location was disre-garded in order for the sensors to stabilize. Data records were saved to the logger anduploaded into the computer for processing. The instrument was recalibrated frequentlythroughout the experiment to keep measurements as accurate as possible.


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Figure 4: Measurements and sampling were carried out at the locations identified in this figure.

Turbidity measurements by Secchi tube The turbidity was measured using a Sec-chi tube. The tube was first fully filled with water and then carefully poured out fromthe tube until the black and white shades at the bottom could be visually distinguished.At each location, the turbidity was measured trice for averaging purposes. At some lo-cations, the turbidity turned out to be so high that the first measurement would fall intoa wide range of values. In such cases, only one measurement was taken and the rangeof values administered as the turbidity of that location for that inflow event.

Collection, treatment and storage of physical samples Clean plastic samplebottles from the laboratory were brought to the field for sample collection. Before actualcollection of the sample, the bottles were rinsed with ambient water two times. Onelitre of water from the surface was collected in the bottles. The bottles were labelledto identify measurement event and location. The samples were kept dark and werecooled by ice packs straight after collection and during transport till the laboratory wasreached the same day. Immediately after the measurements were completed, the watersamples were brought to the laboratory to do filtration for chlorophyll-a extraction andfiltration for dissolved nutrients. The laboratory was reached within six hours aftersample collection commenced, to comply with the protocol for chlorophyll-a analysis(Arar and Collins, 1997). 80 mL of each sample was used for chlorophyll-a filtration,except for samples from location 004, 005 and 006 for event SW2 and location 001,


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010 and 012 for event SD2. For these samples, the filter paper got clogged due to thehigh turbidity of the water samples. The sample volume for filtration was reduced to 40mL. for these instances. The filtration was carried out using 47 mm. GF/F filter paper.Filters with extracted chlorophyll-a were wrapped in aluminium foil and stored in thefreezer at -20oC till the samples were analysed. The filtered samples for the analysis ofdissolved nutrients were also kept in the freezer at -20oC till the time the analysis byion chromatography was scheduled. The samples were stored in the refrigerator at 4oC.3.0 mL of each sample was used for digestion to transfer organic nitrogen into NO3-Nand to transfer organic phosphorus into PO4-P to prepare samples for total nitrogen andtotal phosphorus analysis. Digestion was carried out using an equal amount of K2S2O8·NaOH as oxidizing agent. The mixture was thoroughly stirred for approximately tenseconds. The glass tubes with the mixture of sample and oxidizing agent were coveredtightly with aluminium foil to prevent evaporation of the solution during digestion inthe autoclave. Digestion was carried out at 120oC for a period of 30 minutes. After theautoclave had cooled down till 96oC, samples were taken out of the autoclave to furthercool down till room temperature. The mixture was poured from the glass tubes intoplastic Falcon tubes for storage in the refrigerator at 4oC till the samples were analysed.

Determination of chlorophyll-a by colorimetric method Within one week afterfiltration the analysis for chlorophyll-a was carried out. The analysis was done usingvisible spectrophotometry according as described by (Arar and Collins, 1997). Filterswith chlorophyll-a were taken out from the freezer and kept at room temperature forfive minutes. The filters were unwrapped and put into a cup. Chlorophyll-a and otherparticles were gently wiped off from the surface of the filter using 4.0 mL. acetone 90%as extraction solvent and a mortar for wiping. The remaining fluid was poured into aFalcon tube. This step was followed by a rinsing and washing step using the 3.0 mL. ofthe same acetone standard. During rinsing, the filter was once more gently wiped to en-sure all chlorophyll-a was taken from the filter into the solvent. After that, the filter wasremoved from the cup and the cup and mortar both washed with the acetone standardfor the removal of any last remainders of chlorophyll-a. The total volume in the Falcontube was topped up till 10 mL. with acetone 90%. In order to prevent light penetration,the Falcon tubes with chlorophyll-a were wrapped in aluminium foil. The samples werekept refrigerated for about two hours to let the slurry steepen. After steepening, the tubeswere put into the centrifuge at 10,000 rotations per minute for thirty minutes of time inorder to clarify the solution. 3 mL. of the supernatant was brought into a glass cuvetteand fluorescence was measured at 664, 665 and 750 nm. 3 mL. of the same acetone90% as was used for sample preparation was used as a blank (no chlorophyll-a) duringthe analysis in the spectrophotometer. Next, the blank and the samples were acidified by90 µL of 0.1 N HCl. 90 seconds after acidification, the fluorescence was read again atthe same wave lengths as before acidification. Acidification transfers chlorophyll-a intopheophytin-a. As such, the fluorescence measurement after acidification measures thefluorescence as a result of pigments other than chlorophyll-a. The chlorophyll-a concen-tration then follows from the difference in the absorbance before and after acidificationaccording to the following formula:


18

Chl − a = 26.7 · (Abs664b − Abs750b)− (Abs665a)− Abs750a)) (1)

Subtracting the absorbance before and after acidification for fluorescence measuredat 750 nm corrects the measurements for turbidity. The peak adjustment from 664 nm.to 665 nm. is necessary to account for translation of the absorbance peak, caused byacidification.

Determination of total phosphorus by colorimetric method Determination oftotal phosphorus was done by colorimetry. The digested samples were prepared byadding a mixed reagents solution consisting of the following substances:

• 0.04 gmL−1 ammonium molybdate, (NH4)6Mo7O24·4H2O, solution;

• 2.5 M sulphuric acid solution (solution was at stock and had not to be prepared);

• 0.0176 gmL−1 ascorbic acid, C6H8O6, solution; and

• 0.028 gmL−1 potassium antimonyl tartrate, KSbC4H4O7, solution.

The solutions were added in the prescribed order according to the proportion 3:10:6:1.Distilled water was added last, in 20:75 proportionality with the total volume of themixed reagents mixture. The reagent mixture was freshly prepared every time the anal-ysis for total phosphorus was carried out and the quantity of the mixed reagents solutionwas hence changed according to the number of samples that would be analysed thatspecific day.

After preparation of the mixed reagent solution, 1.0 mL of each sample was addedto a glass tube together with 3.8 mL of the mixed reagents mixture. The solution wasthoroughly mixed and left at room temperature for one hour before analysis. After onehour, the samples were stirred again and poured into a plastic cuvette. The cuvetteswere put into the spectrophotometer and the fluorescence determined at 880 nm.

A calibration curve relating the fluorescence to the phosphorus concentration wasestablished every time the analysis for total phosphorus was carried out. Six digestedstandards with a concentration of 0, 1.0, 2.0, 3.0, 4.0 and 5.0 mgPO4L−1 were availablein the laboratory and used to obtain the calibration curve. The standards were preparedthe same way as the samples. The standard of 0 mgPO4/L was used as blank.

The least-square linear regression of the standards was used as the calibration curve.The total phosphorus concentration of the samples was then derived by reading the totalphosphorus concentration for the fluorescence value obtained from sample analysis. Incase the calibration curve did not meet the requirement R2 > 0.99, the calibration curvewas rejected and the analysis carried out again. This however did not occur for the seriesof measurements in this study.

Determination of total nitrogen, nitrate, nitrite and phosphate by ion chro-matography Total nitrogen, nitrate, nitrite and phosphate concentrations were de-termined by ion chromatography. Concentrations for nitrate, nitrite and phosphate con-centrations were determined from the filtered sample that was stored in the freezer at the


19

day of sample collection. Total nitrogen was determined from the digested sample. Theanion application of the ion chromatographer was used for the detection of the afore-mentioned components. The anion eluent was prepared by adding 9 mL 0.5 M sodiumcarbonate and 1.6 mL of 0.5 M sodium bicarbonate into a 1 L volumetric flask and bytopping it up till 1 L with deionized water.

Three standard solutions were prepared to obtain a calibration curve for the analysisfor each of the components in the water. A seven-anion standard from the supplier ofthe ion chromatographer, consisting of standard concentrations for bromide, chloride,fluoride, nitrate, nitrite, phosphate and sulphate was available in the laboratory. Thecalibration curve was established using the following three standards:

• S1: 40 µL. 7-anion standard and 760 µL deionized water;



The samples and standards were prepared by drawing 80 µL of the sample and byputting the solution into plastic vials. The plastic vials were put into the ion chromatog-rapher. The ion chromatographer was programmed using the software and instructionfrom the supplier.

2.2.4 Interpretation of the results by ANOVA

The results of the field and laboratory work were interpreted using analysis of variance(ANOVA). ANOVA is a powerful method to identify distinct sampling groups. HereANOVA was used to verify whether sampling groups could be distinguished in pointand non-point inflows. A significance level of 0.05 was chosen. Non-point sources aretypically from filter bed locations and bioswales and it was therefore expected that theselocations would have lower concentrations for nutrients.


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2.3 Results

This section shows the results of the measurements done in the field and sample analysiscarried out in the laboratory for chlorophyll-a, nitrogen and phosphorus. Except for thedescription of the results of the analysis of variance, most emphasis in the following twosection is put on nutrients. Nutrient concentrations and loading are the most importantwater quality variables that were examined in this study.

2.3.1 Nutrient loading from the catchment

Figure 5 till 7 present the results of samples analysis for nutrient concentrations. Nitriteand phosphate concentrations were also measured but were found to be below the detec-tion limit in most instances. Nitrogen and phosphorus levels are typically high for thepoint-sources that are driven only during rainfall events. PUB thresholds for nutrients,0.06 mg/L for TP and 1.0 mg/L for TN, are exceeded. Table 1 gives the average of themeasurements for each location. The ratio between the average total phosphorus con-centration and average total nitrogen concentration is smaller than 1:7.2, indicating thatphosphorus limitation is more likely than nitrogen limitation for almost all locations.

Location TP [mg/L] TN [mg/L] NO2 [mg/L] NO3 [mg/L] PO4 [mg/L]001 0.24 6.36 0.25 2.85 0.00002 0.18 5.33 0.00 2.16 0.00003 0.29 9.09 0.97 2.87 0.00004 0.96 7.74 0.10 2.16 0.81005 0.74 5.39 0.00 2.38 0.00006 1.05 7.83 0.12 2.98 0.00007 0.45 6.63 0.22 1.89 0.38008 0.24 3.86 0.00 1.25 0.00009 0.25 7.72 0.00 2.64 0.00010 0.71 3.28 0.00 1.03 0.00011 0.15 2.68 0.00 1.08 0.00012 1.29 13.00 0.00 0.21 0.00

Table 1: Average nutrient concentrations measured for surface water inflows. The lake transfer at011 was not taken into account in this analysis. The concentration at 001 lumps the contribution fromMarina Bay Reservoir and the catchment associated to this inflow location.

2.3.2 Characterisation of the inflows by analysis of variance

An analysis of variance (ANOVA) carried out for all data lumped into one set suggeststhe measurements exhibit differences for dissolved oxygen, chlorophyll-a concentra-tion, turbidity, nitrate, total nitrogen and total phosphorus. If the data is grouped intopoint-sources and non-point sources, there is remaining variance for dissolved oxygen,chlorophyll-a concentration and total phosphorus for the point-sources. If the samplesize of the ANOVA is reduced to include only inflow locations 003, 004, 005 and 006,


21

Figure 5: Total nitrogen [mg/L] measured at inflow locations throughout the period 26 November2013 till 6 January 2014.

Figure 6: Nitrate [mg/L] measured at inflow locations throughout the period 26 November 2013 till 6January 2014.


22

Figure 7: Total phosphorus [mg/L] measured at inflow locations throughout the period 26 November2013 till 6 January 2014.

there is no significant variability left in the data set. It suggests that the inflows drivenpurely by rainfall can be effectively grouped together and identified as one type of in-flow. In addition, total phosphorus and total nitrogen levels from these locations exhibita strong correlation of 0.87. In the correlation, measurement SW1-003 was omittedbecause it was regarded an outlier. For non-point sources, differences remain to per-sist for dissolved oxygen, electronic conductivity, total dissolved solids, chlorophyll-a,total phosphorus and total nitrogen. However, this is largely caused by location 012which has high values for chlorophyll-a, total phosphorus and total nitrogen. If onlylocation 007, 008, 009 and 010 are considered, the only significant difference that oc-curs is for dissolved oxygen concentration, electronic conductivity, salinity and totaldissolved solids. The latter three variables are correlated and therefore expected to varyconcurrently.

The dissolved oxygen concentration at location 003 to 006 is found near saturationmost of the times. There is flow in these channels only after rainfall. Turbulence likelycauses the reaeration of oxygen to be high and therefore higher oxygen levels at theselocations. The turbidity associated with these inflows is also significantly higher thanfor filter bed locations. Location 003-006 are more turbid as the flow comes from thecatchment through a soil ditch and discharges into the lake without interference of afiltering system. For locations 007-012 typically the filter bed will reduce the turbidityas ponding of water enhances settling of the material. Lower chlorophyll-a levels areexpected and also shown for locations 003-006. Chlorophyll-a is the end-product of pri-mary production processes in plants and algae. The pristine nature of the water in theseinflow locations makes the concentrations are generally low. Nutrient concentrationsare higher for location 003-006. The is either explained by the filtering of nutrients forfilter bed locations, but may also be attributed to higher fertilizer load in the catchments


23

that are connected to these inflows, or a combination of the two.


24

2.4 Discussion

2.4.1 Load calculations for surface water inflows

The contribution of each inflow location to the total external nutrient loading was deter-mined by multiplying the measured concentration with the estimated discharge. Table2 presents the results of the load calculations in percentage contribution in kg/yr andkg/ha/yr. Inflow location 011 was eliminated from this interpretative study as it comesfrom the lake transfer system and can therefore not be attributed to a catchment area.Furthermore, the lake transfer system contributes only minor loads to the system be-cause the flow volumes are small as compared to those from the Marina Bay Reservoirand the catchment. Therefore, their relevance for this analysis is limited. LocationsA001 to A004 are added as they are identified as inflow locations but were not includedin the sampling scheme. Locations A001 is assumed to have the same inflow character-istics as location 005; locations A002 and A003 were give the same characteristics asinflow location 010 and the characteristics of A004 were determined as the average ofcharacteristics measured at location 007 and 009. The similarity was based on land-usetype in the catchment. Most important are the inflows from the point-sources driven byrainfall (003-006 and A001). Catchment inflow contributes approximately 40% of theTN loading and 60% comes from the Marina Bay Reservoir. For total phosphorus this isexactly the opposite: 60% comes in from the catchment and around 40% is contributedby Marina Bay Reservoir. As was mentioned already, the lake transfer system has anegligible impact on the nutrient levels for both TN and TP.


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% contribution to load [kg/ha] % contribution to load [kg/ha/yr]

Location TP TN TP TN

001 5.1 13.6 3.2 7.8002 0.9 2.6 3.1 8.2003 1.0 3.2 4.7 13.6004 23.1 18.6 15.0 11.0005 12.7 9.2 12.9 8.5006 12.6 9.4 15.5 10.5

A001 7.2 5.2 4.9 3.2007 14.0 11.4 9.5 7.0008 0.9 1.5 2.1 3.1009 2.8 8.7 2.5 6.9010 4.3 2.0 4.7 2.0012 9.0 9.0 9.5 8.7

A002 3.1 1.4 6.0 2.5A003 1.7 0.8 3.5 1.5A004 1.7 3.5 3.0 5.5

Table 2: Relative contribution of catchments to the nutrient inflow in the lakes as a percentage of theload as kg/yr and in kg/ha/yr.


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3 Data correction

This chapter deals with the integrity of data collected by the monitoring stations in Gar-dens by the Bay. It discusses a number of data integrity issues that appear in the data inGardens by the Bay and discusses techniques for the correction of (environmental) data.After that, this chapter continues by describing an experimental approach by which theperformance of the measurements by the monitoring stations is verified. It also presentsa number of regression techniques whereby data is corrected. The results section givesan overview of the experimental study and the data regression methods, followed by aninterpretative discussion of the results in the last section.

3.1 Introduction

3.1.1 Water quality monitoring in Gardens by the Bay

Water quality monitoring in Gardens by the Bay was commenced in Dragonfly Lake inAugust 2012 and was followed by Kingfisher Lake in February 2013. Multiprobes wereinstalled measuring the following relevant water quality variables:

• electronic conductivity [mScm−1];

• temperature [oC];

• dissolved oxygen concentration [mgL−1]

• pH [-];

• chlorophyll-a [µgL−1];

• turbidity [NTU].

Recordings are taken every ten minutes and transferred to a server for real-time accessto the data. The location of the monitoring stations was indicated already in the previouschapter in figure 4.

3.1.2 Integrity of data from the monitoring stations in Gardens by the Bay

Raw data from the monitoring stations The raw data from the monitoring stationsin Gardens by the Bay show the following:

• data for electronic conductivity and temperature are largely consistent and freefrom drift and noise;

• dissolved oxygen data shows drifts till values below 1.0 mgL−1;

• pH data shows drifts till above 10 pH units;

• chlorophyll-a data shows jumps for readjustment of the sensor to the concentra-tion obtain from sampling; and


27

• turbidity values reach high values from which is does not recover to lower valueswithout cleaning of the sensor.

Values below 1.0 mgL−1 for dissolved oxygen and above 10 pH units are not ex-pected. In addition, the data pattern is such that the values are highly unlikely trueobservation as such sudden jumps are hardly observed in natural systems. Furthermore,the data remains consistently at these levels and does not return to a range of values thatare regarded acceptable. This is a strong indication of drift.

The jumps in the chlorophyll-a data seem to be the results of the adjustment of therecordings by the monitoring stations to the chlorophyll-a concentration that is obtainedfrom the sample collection on the day of calibration. Currently, the procedure thatis followed is that the value in the laboratory is trusted and that the field sensor getsadjusted accordingly.

Turbidity data predominantly suffered from sudden increases to values above 200 andeven 1000 NTU and would not recover to values in a range that is regarded acceptable.

Changing from site-mounted to submerged condition Initially the multiprobeswere installed as site-mounted systems. In this system setup, water is drawn from thecentre of the lake and pumped to the sight where the multiprobe is kept inside a cabinet.When the equipment was installed as site-mounted system, it was frequently observedthat the system got clogged internally. Membrane sensors such as the ones for dissolvedoxygen and pH, as well as optical sensors, such as those for chlorophyll-a and turbiditywere possibly affected by this. Therefore, in June 2013, the monitoring station in King-fisher Lake was replaced to submerged condition in Dragonfly Lake in order to comparethe measurements from a site-mounted system against a probe in submerged condi-tions. Large deviations between the measurements were observed for especially pH andthe response for dissolved oxygen, chlorophyll-a and turbidity were distinctly different.Therefore it was decided to change the system configuration from site-mounted intosubmerged. In the beginning of July, the probe from Kingfisher Lake was brought backin place and both systems continued to measure in submerged conditions.

3.1.3 Data cleaning and correction for environmental data

Data cleaning and correction is commonly performed for water quality data that hasbeen collected in the field. Environmental data sets often suffer from the followingproblems (Hipel and McLeod, 1994):

• missing and incomplete data;

• outliers;

• availability of short time-series;

Common techniques for correcting missing data are:

• mean or median of the non-missing values;

• simulate a distribution using the non-missing values;


28

• regression methods;

• use of historical data; and

• correlation with another variable.

Correlations and regression methods are closely related. Regression methods covera wider range of methods whereby a relation is established between one or more cor-related variables. Correlations between different variables in an environmental systemare an important indicator for the importance such variable will have on the variable forwhich a regression is to be established. Techniques such as artificial neural networksand genetic programming are regression methods that are able to make non-linear com-binations of a large number of input variables.

Replacing missing values by the mean or median value is a crude approximationof the true behaviour of environmental system. The system states that are indicatorsof water quality are determined by a complex interaction of physical, chemical andbiological relations. Replacing techniques such as the ones indicated here are onlyvalid if the duration of the data gap is shorter than the time scales of the fluctuationsof the system dynamics. For water quality systems, diurnal fluctuations are commonlyobserved for variables such as temperature, dissolved oxygen, pH and chlorophyll-a andtherefore the strategy of replacing using average values is not applicable. Combinationsof the above mentioned techniques are also viable solutions to correct for missing orspurious data. For example, regression methods could include a time-lag, thereby takingaccount of historical values in the data regression.

Purpose of data correction Correction of environmental data sets serves to recovertime-series data so that it can be used for interpretative studies that aim to address thewater quality or aim at monitoring important trends in the water quality of surface wa-ters. From a more practical perspective, data correction avoids early replacement ofmonitoring equipment and may reduce the number of time re-calibration of the mon-itoring equipment is required (Salit and Turk, 1998). It reduces the variability in thecollected data (Artursson et al., 2000) and gives relevant time-series data for calibrationof water quality models if developed.

Data correction for water quality data Simple one and two-point linear correctionmethods for temperature, pH, electronic conductivity and dissolved oxygen data areproposed by Richard J. Wagner and Smith (2006). One-point correction techniques as-sumes drift occurs linearly in time. The gap between post-cleaning and post-calibrationreading are a measure of the instrumental drift at the end of the period between two cal-ibration events. One-point linear correction assumes a linear development of drift fromzero straight after calibration to the drift, d, observed during re-calibration. Two-pointcorrection methods are preferred if the sensor range is large. The absolute drift mightbe larger for higher sensor readings.

Identification of drift in environmental data sets According to Carlo et al. (2010),sensor drift can be defined as ’the temporal shift in a sensor’s response under constant


29

environmental (physical and chemical) conditions.’ In Ziyatdinov et al. (2010) drift isfurther specified as ’gradual changes in a quantitative characteristic that is assumed tobe over time. Drift is associated with deterioration of the sensing material due to sensoraging and poisoning (Ziyatdinov et al., 2010). Aging describes the process wherebythe internal structure of the sensing surface changes over the course of time; poisoningis caused by binding of external contamination on the sensing surface (Vergara et al.,2011). Besides sources associated to instrumental issues, drift is influenced by environ-mental conditions specific to the site where sensing equipment is installed. For Gardensby the Bay, biofouling and clogging by suspended sediments are expected to contributeto additional drift. Membrane sensors such as those for pH and dissolved oxygen aswell as optical sensors such as the ones for chlorophyll-a and turbidity measurementscould be susceptible to material accumulating at the sensor head.

In the definition by Carlo et al. (2010), drift is a deviation in sensor output from whatit ought to be based on assumed steady conditions. Most of the research that involvesdrift correction was carried out for controlled conditions in which the state-variable(s)to be measured are kept constant, hence satisfy the steady-state assumption. Real-worldenvironmental conditions are hardly described by prolonged constant conditions, andthe sensor signal cannot be easily compared. A description of drift-free conditions thusis lacking.

Drift correction methods Carlo et al. (2010) and Sanchez et al. (2012) categorisesolutions to cope with instrumental drift in three groups:

1. periodic calibration;

2. attuning methods; and

3. adaptive models.

Periodic calibration is usually carried out for equipment because of environmentaland meteorological conditions that may alter the output of sensors. As was indicatedabove, the great advantage of an algorithm that corrects for drift is that it reduces thenumber of times re-calibration is required. Ultimately this reduces the frequency ofdisruptions of the observation system, reduces the number of field visits and therebytime and money spent on calibration.

Attuning methods aim to separate drift components from real response. Attuningmodels require a set of calibration data to identify the drift components (Sanchez et al.,2012). Identification of the components that result in sensor drift are used in componentanalysis techniques to express their relative importance. Components with most influ-ence on drift are then selected and used in a description for sensor drift which can beapplied to future data series. According to Ziyatdinov et al. (2010), drift has a domi-nant direction and therefore justifies the choice for component analysis to describe thephenomenon. Ziyatdinov et al. (2010) uses a multivariate technique based on commonprincipal component analysis for drift compensation.

The last category, adaptive models, describes methods that take into account patternchanges due to drift (Carlo et al., 2010). Adaptive models will not perform well if thedrift pattern is highly variable and training data series has been short. The classifier mayin such cases not be able to recognise the data pattern and cannot effectively determine


30

the drift component. Adaptive models rely on accurate description of the statistics of thesystem and therefore require either high resolution input data for drift-free conditionsor stationary conditions (known input signal) which are both hard to satisfy in uncon-trolled, real-world, systems. The great advantage of adaptive models is that they do notrequire an a-priori description of a drift model.

Detection of trends in time-series data The comparison of a signal from the mon-itoring stations with a verification measurement gives two time-series that have to becompared for their similarity. Measurement instrumentation can be biased expressedby an offset between the two data sets. Furthermore, drift could cause the deviationbetween two data sets to become larger over time. This should be visible in the residualvalues for the two data sets.

Data series with an unknown distribution, can be best tested for trend through non-parametric tests. Mann-Kendall’s test is a non-parametric test for trend detection (Hipeland McLeod, 1994) and has been applied in numerous environmental data studies, e.g.in Khaliq et al. (2009). Abaurrea et al. (2011) addresses a number of disadvantages ofnon-parametric tests. These include the disability of this group of methods to couple thetrend to explanatory variables and its disability to identify non-monotonic trends, or, inother words, its capacity to identify trends in seasonal data.


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3.2 Methodology

This study has explored two different ways to address the issue of drift in the waterquality monitoring data from the field. The first strategy compares the measurements bythe two monitoring stations with a verification measurement carried out with an inde-pendent, well-calibrated measurement device. Verification measurements were carriedout daily for a period of three weeks between two consecutive visits for servicing andre-calibration of the equipment. Secondly, spatial linear regression was used to correctdata that has been collected between 01 August 2013 and 31 December 2013. Thissection will describe in detail the methods of study for both correction approaches.

3.2.1 Experimental study for the collection drift-free verificationmeasurements

A drift-free time-series was collected in Dragonfly Lake and Kingfisher Lake to comparethe signal of the monitoring stations with a verification measurement for the three weekperiod between 26 November 2013 and 17 December 2013. On November 26th themultiprobes in Dragonfly and Kingfisher Lake were cleaned and calibrated such that anew series of data collection was started from a system that was initially free from anyinstrumental and/or environmental drift. Collection of the verification data started thesame day and continued daily for a period of two weeks. After two weeks, the frequencyof the measurements was reduced to once every three days. This was continued tillDecember 17th when the monitoring station got re-calibrated again. The verificationinstrument measured temperature, electronic conductivity, pH and dissolved oxygenfor comparison with the data from real-time monitoring and was lacking turbidity andchlorophyll-a. A Secchi tube with an indication for turbidity [NTU] was used insteadto obtain turbidity data. The Secchi tube was calibrated for another instrument andtherefore the comparison can only be used as a qualitative assessment. In order tocompare the chlorophyll-a data from the real-time monitoring stations, a water samplewas collected every day for chlorophyll-a analysis in the laboratory. The measurementinstrument for verification was the same as the one used for collection of catchmentinflow characteristics and the methods for turbidity measurements and chlorophyll-aanalysis the same as describe in chapter 1.

Verification measurements were carried out at three different locations along the mon-itoring station in Dragonfly Lake and at four different locations near the monitoring sta-tion in Kingfisher Lake to obtain a spatially averaged value. It was for practical reasonsthat one more site was selected for the verification measurements in Kingfisher Lake.Only one sample for chlorophyll-a analysis was collected for comparison with the mon-itoring station. Measurements and sampling were carried out around 9 AM. Duringmorning hours, the chlorophyll-a concentration is expected to be at its high. Having thehighest possible concentration for chlorophyll-a is favourable as it reduces the relativeerror in the laboratory measurement.

It took between 30 to 60 minutes to take the verification measurements for one mon-itoring station. In order to account for any spatial variation that may have taken placewhile the verification measurements were carried out, one hour worth of data was se-lected from the monitoring stations and averaged for comparison with the verification


32

measurement.

3.2.2 Statistical analysis for the comparison of verification measurementswith data from the monitoring stations

The existence of statistical significant differences between the data from the monitoringstation and the verification measurements is addressed by Student’s t-test and Mann-Kendall’s test. Student’s t-test is performed to verify whether the measurement by themonitoring station deviate significantly from the verification measurements. This testonly gives an indication of a difference in the mean value between the two data sets.Drift phenomena cannot be identified using such test. Therefore, Mann-Kendall test fortrend detection was performed on the residual values in addition to Student’s t-test. Theresiduals are defined as the difference between the measurement from the monitoringstation and the verification measurement. Mann-Kendall test verifies whether the nullhypothesis of absence of a trend (H0) holds against the alternative hypothesis of pres-ence of a trend (H1). The significance level for both the t-test and Mann-Kendall testwas chosen at 0.05. The test was carried out for all variables except turbidity as therecordings were reset in between the calibration servicing in November and December.Also for chlorophyll-a the test was not performed because the sensor value was only re-set five days after the day the experiment started. Mann-Kendall’s test has been widelyapplied for the analysis of trends in water quality data. Similarly the test can be usedto determine a trend for residual values as is described in Helsel and Hirsch (2002).Mann-Kendall’s test for trend detection is a non-parametric test and is therefore freefrom assumptions regarding normality of the data.

The Mann-Kendall test statistic is defined as:

S =∑

N−1i=1

∑nj=i+1sgn(Yj − Yi)

sgn(Yj − Yi) =

1, if Yj − Yi > 0

0, if Yj − Yi = 0

−1, if Yj − Yi < 0

A positive value of S indicates an upward trend and a negative value a downwardtrend for residual values.

3.2.3 Spatial linear regression for the recovery of temperature data

Other common data adjustments mentioned in Richard J. Wagner and Smith (2006)include correlations between point measurements and cross-section average measure-ments. The latter technique is not of use for Gardens by the Bay because there is onlypoint data available for Dragonfly and Kingfisher Lake. Correlations between pointmeasurements will be determined to identify correlations between data in Dragonflyand Kingfisher Lake. If correlations are apparent they can be used for regression meth-ods.


33

Temperature data from both monitoring stations possesses a high degree of correla-tion of 0.90. From this it was decided to establish a spatial linear regression for tem-perature in Dragonfly and Kingfisher Lake. In other words, temperature measured inKingfisher Lake would be used as an input value to predict the water column tempera-ture in Dragonfly Lake and the other way around.

Data for training and testing of a spatial linear regression was selected from the period01 August 2013. This day was chosen as starting point because:

1. it was one day after calibration of the monitoring stations. Before that the systemhad last been calibrated at 07 June 2013; and

2. both station were brought into submerged conditions from this day onwards.

The actual data to establish regression relations was chosen from 05 November 2013till 16 December 2013 with exclusion of 26 November 2013 when the equipment wascalibrated. Temperature data was available for both stations and there was no reason todoubt the quality of the measurements for this period. Data for training and testing ofthe regression models was divided such that both data sets would contain informationfrom before and after the calibration at November 26th. Data was split into a trainingdata set consisting of 70% of the data and a testing set of the remaining 30% accordingto the following division:

• Training data: 06 November 2013 - 19 November 2013; 27 November 2013 - 10December 2013; and

• Testing data: 20 November 2013 - 25 November 2013; 11 December 2013 - 16December 2013.

The performance of the linear regression for training and testing data is assessed forthe root mean squared error and Nash-Sutcliffe efficiency. In addition, the seasonality ofthe residuals is addressed by auto-correlation analysis. The presence of auto-correlationin the residual data is affirmed by Durbin-Watson test for auto-correlation. The test-statistic of this test is:

d =

∑(et − et−1)

2∑e2t

(2)


34

Dragonfly Lake Kingfisher Lake

Variable Accepted hypothesis p-value Accepted hypothesis p-value

EC H1 <0.0001 H0 0.0570T H0 0.9545 H0 0.4357

DO H1 0.0026 H1 <0.0001pH H1 0.0083 H1 <0.0001

Table 3: Student’s t-test confirms that the measurements from the monitoring stations and the veri-fication instrument are significantly different for a 5% confidence interval. Also the reading for elec-tronic conductivity in Dragonfly Lake is notably different according to the t-test.

3.3 Results

3.3.1 Visual comparison of verification measurements with data from themonitoring stations

Figures 8 till 19 present the data that was collected for the comparison of the monitoringstation with an independent verification measurements. The following observations aremade from visual inspection:

• the response for temperature is similar in terms of the trend and the magnitude;

• measurements for electronic conductivity, dissolved oxygen and pH clearly devi-ate;

• there is no clearly visible pattern for chlorophyll-a data; and

• measurements for turbidity show a similar pattern.

3.3.2 Comparison of the mean: results of Student’s t-test

The visually observed deviation between data is confirmed by Student’s t-test for elec-tronic conductivity, dissolved oxygen and pH for Dragonfly Lake and for dissolvedoxygen and pH for Kingfisher Lake. The results of the test are summarised in table 3.

3.3.3 Detection of sloping trend in residual values: results of Mann-Kendalltest

Mann-Kendall test shows there is absence of a significant sloping trend for the residualvalue of most of the variables. The alternative hypothesis of presence of a trend isaccepted only for electronic conductivity in Dragonfly Lake and dissolved oxygen inKingfisher Lake. Table 4 contains the relevant summary of the Mann-Kendall test.


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Figure 8: Comparison temperature Dragonfly Lake

Figure 9: Comparison temperature Kingfisher Lake


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Variable Accepted hypothesis p-value Accepted hypothesis p-value

EC H1 0.0060 H0 0.7480T H0 0.7665 H0 0.7665

DO H0 0.5526 H1 0.04878pH H0 0.4277 H0 0.3930

Table 4: Mann-Kendall test for trend detection indicates there is a significant slope for residual valuesfor electronic conductivity data from Dragonfly Lake and dissolved oxygen data from Kingfisher Lake.

Figure 10: Comparison electronic conductivity Dragonfly Lake

3.3.4 Comparison of the sensor response before and after calibration

On 17 December 2013, the water quality monitoring stations in Dragonfly Lake andKingfisher Lake were re-calibrated. The sensor readings for the calibration standardafter cleaning of the equipment, but before re-calibration of the equipment are presentedin table 5.

The difference of the sensor reading after cleaning, before calibration with the sensorreading after calibration for the calibration standard, can be interpreted as a measure ofthe instrument bias or drift. Because cleaning is carried out before a sensor reading iscarried out, the effects of environmental drift clogging cannot be inferred from thesemeasurements.

Table 5 indicates that the electronic conductivity sensor for Kingfisher Lake has func-tioned well. For Dragonfly Lake, the linearity of the sensor response between 147 and1413 µS/cm was tampered. This suggests the reading for electronic conductivity for


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Figure 11: Comparison electronic conductivity Kingfisher Lake

Figure 12: Comparison dissolved oxygen Dragonfly Lake


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Figure 13: Comparison dissolved oxygen Kingfisher Lake

Figure 14: Comparison pH Dragonfly Lake


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Figure 15: Comparison pH Kingfisher Lake

Figure 16: Comparison chlorophyll-a Dragonfly Lake


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Figure 17: Comparison chlorophyll-a Kingfisher Lake

Figure 18: Comparison turbidity Dragonfly Lake


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Figure 19: Comparison turbidity Kingfisher Lake

Variable Units Standard Dragonfly Lake Kingfisher Lake

EC [µS/cm]147 164 148

1413 1265 1416

pH [-]4 4.28 4.08

10 10.4 9.76

DO [%] 100 88.9 85.7

Table 5: Readings calibration standards after cleaning of the sensors, before calibration.

the range of values commonly observed in Gardens by the Bay have been overesti-mated. Overestimation of the measurements for electronic conductivity is however notconfirmed by the verification measurements. The verification measurements are consis-tently above the reading from the monitoring station.

The pH sensor in Kingfisher Lake did get affected in a similar fashion. Because therange of values for pH in the lake system is at the right end of the range covered by thecalibration curve, the pH in Kingfisher Lake has been underestimated. pH in DragonflyLake was overestimated according to the upward shift in the readings for calibrationstandards at pH 4 and pH 7. Verification measurements for pH are indeed higher inKingfisher Lake and confirm an underestimation by the sensor from the monitoringstation. However, for Dragonfly Lake the opposite is true. Although the monitoringstations overestimates the actual pH of the calibration standard, the verification mea-surement have been even higher throughout the three week period.

Calibration for dissolved oxygen consists of a one-point calibration only and indicates


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that for both stations, the concentration at saturation is underestimated. The underesti-mation was confirmed by reading of a site water sample at the same time after cleaningof the equipment and after calibration of the equipment:

• Dragonfly Lake: 4.84 mg/L before calibration (after cleaning) and 5.64 mg/L aftercalibration;

• Kingfisher Lake: 3.93 mg/L before calibration (after cleaning) and 4.74 mg/Lafter calibration.

This corresponds with an underestimation of 15% for Dragonfly Lake and 17% forKingfisher Lake which is comparable to the underestimation at saturation of 10% and15% respectively.

Throughout the period of the experiment, dissolved oxygen levels for the sensorsfrom the monitoring stations have been consistently lower than for the verification mea-surement. The underestimation amounted 17% on average. This very much agrees withwhat was found during calibration of the equipment at December 17th. For Dragon-fly Lake, the difference between the monitoring station and verification measurementamounts 32% on average. This difference is only covered for half by the reading bythe monitoring before and after calibration. In addition to instrumental drift there mighthave been an additional influence, possible related to the environmental conditions, thathas affected the sensor recording during the three week period.

The agreement between the percentage deviation between dissolved oxygen mea-surements by the monitoring station in Kingfisher Lake before and after calibration inDecember for a site water sample and for dissolved oxygen at saturation, in combina-tion with the residuals for the verification measurements, suggest that the differencebetween the recordings is due to differences in the calibration procedure or instrumentsettings. However, this is contrasted by the difference observed for data from the mon-itoring station in Kingfisher Lake before and after calibration on November 26th andDecember 17th. The observed differences amounts -2.4 mg/L in November and 2.2mg/L in December. These are gross underestimations of 45% and are much larger thanthose observed during calibration. It suggests that next to the 15% error which is at-tributed to instrumental issues, there has been another factor that has harmed the sensorperformance throughout this period. It is not sure what this factor has been but it seemssomehow associated with the sensor membrane as the readings recovered largely aftercleaning of the equipment during servicing in December.

The readings from after cleaning and before calibration of the monitoring stations, in-dicate that instrumental drift does not explain the disagreement between the verificationmeasurement and the readings from the monitoring station for electronic conductivityand pH. It suggests that the response for these sensors has been affected by environ-mental conditions. However, the disagreement between observations by the monitoringstation and the verification measurement is visible directly at the start of the experiment.If environmental conditions are the cause of the remaining discrepancy between the sen-sor readings, it implies that the sensor reading gets affected on shorter time scale thanthe interval of the verification measurements. Such short term response up or down ishowever not seen in any of the data from either of the monitoring stations.


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Figure 20: Dragonfly Lake - temperature before correction.

3.3.5 Results of spatial linear regression for temperature data

The results of spatial linear regression for temperature are presented in table 6. It showsthat a linear regression model can be successfully trained and verified for a relativelyshort data period and can be used for the recovery of temperature data in both Dragonflyand Kingfisher Lake, provided that data from the other station is available.


RMSE NSE RMSE NSE

Training data 0.41 0.79 0.49 0.79Testing data 0.38 0.09 0.42 0.59

Table 6: Performance spatial linear regression temperature for data from the period 01 August 2013- 16 December 2013.

The linear regression coefficients that were obtained from the regression model wereused to recover temperature data for Dragonfly Lake for the period 01 August 2013 -21 August 2013 and for Kingfisher Lake for the period 23 August 2013 - 16 September2013. Figure 20 till 23 show the temperature data for Dragonfly and Kingfisher Lakebefore and after the correction by spatial linear regression.

The auto-correlation of the residuals from training and testing is examined to iden-tify whether residual values possesses seasonality. Figure 24 and 27 show the auto-correlation plots for time lags till 100 hours. The horizontal lines indicate a significancebound of 99%. It shows that at every 24 hours there is an increase in auto-correlation


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Figure 21: Dragonfly Lake - temperature after correction. Data for 01 August 2013 to 23 August2013 is recovered by spatial linear regression.

Figure 22: Kingfisher Lake - temperature before correction.


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Figure 23: Kingfisher Lake - temperature after correction. Data for 23 August 2013 - 16 September2013 is recovered by spatial linear regression.


Accepted hypothesis p-value Accepted hypothesis p-value

Training H1 <0.0001 H1 <0.0001Testing H1 <0.0001 H1 <0.0001

Table 7: Durbin-Watson test identifies that the residual values for training and testing of spatial linearregression models for temperature data do contain significant auto-correlation.

and hence there seems to be a diurnal seasonality left in the regression model. TheDurbin-Watson test confirms the suggestion of strong auto-correlation. The test statis-tics are presented in table 7 and it shows that within a 99% confidence interval in can beconcluded that all residual series have significant auto-correlation.


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Figure 24: Autocorrelation for the training data for temperature regression Dragonfly Lake.

Figure 25: Autocorrelation for the test data for temperature regression Dragonfly Lake.


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Figure 26: Autocorrelation for the training data for temperature regression Kingfisher Lake.

Figure 27: Autocorrelation for the test data for temperature regression Kingfisher Lake.


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3.4 Discussion

The method whereby a drift-free time-series is collected for comparison with the mea-surements by real-time measurements in the field is successful to obtain quantitativeinformation about the sensor response under varying environmental conditions. Forwell-calibrated probes, it gives insight in how instrumental bias introduces uncertaintyin the observed data. Furthermore, changes in the permanently installed monitoringequipment can be attributed to changes in the environment that influence the sensorreading. Based on the analysis for dissolved oxygen, the drift components or causesfor data fallacies for the monitoring station in Kingfisher Lake for the period Novemberand December 2013, are not uniquely identified. The comparison suggests there is anenvironmental drift component but this is not convincingly shown in the data from themonitoring station. Spatial linear regression is a successful way to recover temperaturedata. The regression model trained and tested in this study can be used for this purposefor this project.


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3.5 Conclusions and recommendations

The comparison of drift-free data with sensors permanently deployed in the environmenthas resulted in three important conclusion for Gardens by the Bay:

1. the monitoring stations are able to represent the temporal dynamics for electronicconductivity, temperature, pH and dissolved oxygen reasonably;

2. the readjustment of the chlorophyll-a sensor readings according to the concentra-tion determined in the laboratory may introduce representation errors if the timebetween calibration servicing and laboratory analysis is substantial;

3. readings from the turbidity sensors seem to get affected by intense rainfall events.Additional cleaning of the turbidity sensors is recommended to ensure data qualityafter such events and before the next calibration and maintenance servicing isscheduled. Furthermore, an increase of the wiper frequency and possibilities todeploy a stronger wiper on the sensor head are options that could examined; and

4. the sensor response is affected, but not fully explained, by instrumental drift.

The first conclusion indicates that the fluctuations in the environmental conditions arerepresented by the sensors from the monitoring stations. The statement in the secondconclusion has to be verified more thoroughly. Solutions could be found in a changein the adjustment procedure or by agreeing chlorophyll-a analysis can be done at thesame day or one day after sample collection. Here it is furthermore important to notethat the chlorophyll-a analysis in the lab is probably insufficiently accurate to be usedas readjustment method. This was explained in more detail in the previous chapter.The third conclusion is a practical advice that Gardens by the Bay may want to followfor data quality assurance. Good quality data is important for the understanding of thesystem as well as the development of models. The fourth conclusion states that thesensor response is affected by causes that are associated to the instrument and systemcalibration. It will therefore be of importance to keep the current calibration protocolalive to ensure documentation of the sensor performance before cleaning, after cleaningand after calibration of the equipment. The last conclusion also gives an incentive tostudy environmental drift factors in more detail as there remains unexplained behaviourif the signal from the monitoring station are compared with an independent verificationinstrument.

For future purposes, it is advised to define for each state-variable what the acceptedabsolute errors are. The timing of calibration and maintenance of the sensors can then bealigned accordingly by assuming a linear drift pattern for the sensors with time. A lineardrift model can be derived by analysis of all past data from before and after calibration.This is especially necessary if data correction procedures do still not fully describe thedrift terms in the water quality data, because then, calibration remains the main meansby which to verify the sensor response.

Next to data comparison and good calibration practices, regression models can as-sist in recovering historic data that is missing due to sensor malfunctioning. This studysuccessfully shows how spatial linear regression helps to increase the data availability.Other relevant regression models that could be trained would predict dissolved oxygen


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levels. Dissolved oxygen is next to temperature an important variable for model cal-ibration purposes. It is recommended to look at pH and temperature as explanatoryvariables because these variable have a relative high degree of correlation. Non-linearregression methods such as artificial neural network may be required to obtain a goodfunctioning regression model for this variable.

More data will become available in the future and can be used to enhance and verifythe performance of existing regression models. Furthermore, it is suggested to lookat the integration of the above mentioned correction methods with regression methods.Adding in system information could increase the performance of the regression modeland could thereby give better results.


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4 Water quality modelling for Gardens by the Bay

This chapter shows the steps in the development of a hydrodynamic and water qualitymodel for Gardens by the Bay, Singapore. It uses the previous two chapters, the first asmodel input and the second for model verification.

4.1 Introduction

The water quality model developed in this study focuses on the following water qualityvariables:

• water column temperature [oC];

• dissolved oxygen [mgL−1];

• chlorophyll-a [µgL−1];

• total nitrogen [mgL−1]; and

• total phosphorus [mgL−1].

Temperature is the driving variable for many chemical and biological processes thatdetermine the water quality. It predominantly influences the rate by which those pro-cesses take place. Furthermore, water column temperature is influential for the occur-rence of thermal stratification. Thermal stratification is especially found in lakes intemperate climates (Chapra, 1997). In tropical regions, the effect of thermal stratifica-tion is more of an issue for deep lakes where the thermocline has a chance to developdespite the limited seasonal variation in temperature. In shallow water systems, a dailymixing pattern is expected due to the strong heating and cooling effect of the water.The thermal structure of Singapore’s Kranji reservoir is extensively examined in Xinget al. (2014). Dissolved oxygen is an important water quality variable as it gives anindication of the level of aquatic live that can be found in the system. High dissolvedoxygen levels enable species from high trophic level to reside in the water. A drop indissolved oxygen levels can result in fish kill. Chlorophyll-a is an indicator of the healthof aquatic systems in terms of the algae productivity. High chlorophyll-a levels indicatehigh level of phytoplankton. The negative consequences of high algae concentrationsinclude the release of toxins by algae, visual and odorous noise and deposition of algaeat the sediment layer. Turbidity is a key water quality variable for its strong influenceon light penetration. In terms of growth of algae, water systems can be either light ornutrient limited whereby in the latter case nitrogen, phosphorus, silica and carbon canbe further distinguished. Turbidity alters the light penetration and therefore enhance orinhibit algae scum. Furthermore, lake turbidity strongly has aesthetic value, especiallyfor lake systems that have a recreational function.

Eutrophication process Eutrophication refers to the natural process of maturingof a water body. Pristine water bodies get enriched in inorganic and organic nutrientsthat accumulate in the system by runoff from the catchment. Eutrophication becomes a


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threat if the process of enrichment is greatly accelerated, e.g. due to fertilizer applicationin the catchment runoff from highly urbanised areas. High concentrations of nitrogenand phosphorus promote the growth of aquatic vegetation ranging from lower to higherspecies. A sudden increase in the nutrient concentrations could in addition result inalgal bloom. Algal bloom has considerable negative consequences for the overall lakewater quality of which the following are relevant to Gardens by the Bay:

• production and release of toxins in the water column by algae;

• visually obtrusive scums and odorous nuisance; and

• decomposition of algae at the sediment layer after algal bloom causing anoxicconditions in the water column;

The release of toxins and possibility of anoxic conditions are both unfavourable foraquatic life. Higher species of fish are the first to suffer from a decrease in the dissolvedoxygen concentration in the water column.

Thomann and Mueller (1987) list a number of variables that determine the trophicstate of natural water bodies:

• solar radiation at the water column;

• geometry of the water body including the surface area and surface area, waterdepth and volume;

• water flow, velocity and dispersion;

• water column temperature;

• nutrient loading and concentration, including nitrogen, phosphorus and silicate;and

• phytoplankton concentration.

Important indicators for the risk of algal bloom are the nutrient concentration, radi-ation and temperature, water column turbidity and flow velocity. Low turbidity levelsenhance the penetration of ultraviolet light deep into the water column, thereby stimu-lating algae growth over greater depth. Flow velocity is an important indicator for thedeposition rate of nutrients on the sediment layer. For low flow velocity the depositionrate will be high and the sediment layer becomes a sink for nutrients. Phosphorus couldget release into the water column during anoxic conditions at the sediment. As such,low flow velocities are a potential threat as far as the water quality on the longer termis considered. Climatological conditions in Gardens by the Bay are favourable for eu-trophication. All year round high temperature accelerate the rates by which chemicaland biological processes take place. Located near the equator, solar radiation levels arehigh. Water column turbidity has been considerable, partly due to the ongoing con-struction in the lake’s catchment. However, the water body is shallow which makes thatsunlight penetration is possibly still able to reach till the bottom of the lake. The riskassociated with low flow velocity is recognised as well for Dragonfly and Kingfisher


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Lake. The flow velocities are small though and wind induced motion is also expectedto be of a minor contribution. The vegetation around the lakes, reduces the fetch lengthand is expected to create a rather stagnant wind regime above the water surface.

Nitrogen and phosphorus are present in the water column in several forms. Phos-phorus is composed of a particulate and a dissolved component that both contain anorganic and inorganic part. Dissolved inorganic phosphorus contains ortho-phosphate,(PO4), which is the required form for phytoplankton growth. Total particulate, organicphosphorus is made up by phosphorus contained in living and death biomass. The firstcomponent is referred to as the phytoplankton-P and the second component is calleddetritus-P. Nitrogen is subdivided into an organic and inorganic component. Particu-late organic nitrogen is composed of phytoplankton biomass and detritus-N. Inorganicnitrogen appears as ammonium, nitrite and nitrate.


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4.2 Methodology

Modelling was carried out in Delft3D modelling software by Deltares (Delft, Nether-lands). Hydrodynamic modelling was done in Delft3D-FLOW and resulted in waterlevel and flow velocity computations. Furthermore, the water column temperature wasobtained in Delft3D-FLOW by accounting for the heat exchange between the atmo-sphere and the water column and the influence of the temperature of inflow dischargesand water offtake from the Dragonfly Lake. Water quality modelling was carried out inDelft3D DELWAQ, the water quality modelling suite of Delft3D software. Here the rel-evant state-variables (substances in DELWAQ) and governing physical, biological andchemical processes were selected that influence lake water quality. The results of thehydrodynamic model were taken into consideration in the water quality model throughthe coupling procedure.

4.2.1 Hydrodynamic modelling

The hydrodynamic modelling was continued from the grid and bathymetry as it wasdeveloped in the earlier phase of Gardens by the Bay project. Other than that, the modelconfiguration was completely changed:

• the model time period was changed from 01 December 2012 to 31 March 2013into 01 March 2013 to 31 November 2013;

• the model inflows locations were changed and additional inflow locations wereadded to adjust the model to what had been observed in the field;

• the discharge per inflow location was changed from the same magnitude for allinflow locations to an inflow amount specific to the size and characteristics of thesubcatchment associated to each individual inflow location;

• the heat flux model was changed to include net solar radiation data;

• the model boundary condition was changed from an overflow into a dischargetime-series;

• additional observation locations and cross-sections, features to assess and verifythe model performance at specific parts in the domain, were added;

• appropriate modelling of the contributions by rainfall and evaporation; and

• expansion of the model to 3D using sigma layers.

Hydrodynamic grid and bathymetry The hydrodynamic grid represents the King-fisher and Dragonfly Lake and connects the two by representing the Saraca stream asa 2DHV channel in Delft3D. Saraca stream is merely five metres wide and the sizeof the weirs that connect the Frog pond and Rainforest Lily Pond to Kingfisher Lakeare approximately five metres wide. In combination with the relatively steep bottomslope in both lakes, the horizontal resolution of the grid was very refined to representthese features accurately. Both the grid and bathymetry were taken from the precedingmodelling study.


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Vertical discretisation The three dimensional hydrodynamic model required addi-tional configuration settings for the vertical discretisation and vertical mixing and comeswith additional constant for the heat flux model. This study has looked into the follow-ing aspects of three dimensional modelling:

• z-layer model with ten layers;

• σ-layer model with ten layers;

• σ-layer model with five layers; and

• σ-layer model with three layers;

The thickness of computational layers in the z-model is fixed and was set at 0.80metres. Layers in the σ model cover a user-defined percentage of the water depth. Herea ten layer σ-model was used as a reference case for the z-model as far as the thermalstructure in Dragonfly Lake was concerned. At the deepest part of the lake, the layerthickness will be similar for both model configurations. The five layer and three layermodel were introduced to reduce the number of grid cells which has the advantage ofsaving computational time.

z-Layers are preferred for systems that have steep bottom gradients and where thermalstratification may play a role. This type model configuration was successfully appliedin studies for Singapore’s Punggol-Serangoon Reservoir and Upper Peirce Reservoir.Stratification was not an expected phenomenon for Gardens by the Bay because of theshallow nature of the lakes. Since measurements are lacking, the vertical temperaturedistribution cannot be verified and the model outcome is rather used to have an indica-tion of whether such effect is present or not and what is extent would be.

Because of the fixed thickness of layers in the z-model, z-layer discretisation yields toa large number of inactive cells if deeper parts are rare. This reduces the computationaltime to a large extent. σ layers do not have the same advantage. Only reducing thenumber of layers can reduce the computational time in this case. In the end, σ layershad to be used because no working configuration for the water quality model was foundif it was combined with a hydrodynamic model using z-layers.

Modelling time frame Hydrodynamic modelling was carried out for the period 01March 2013 till 01 December 2013. The time step for the computations was set at 10minutes to ensure the numerical stability of the computations.

Initial conditions For the hydrodynamic model the following set of initial conditionswere used:

• water level: 102.0 m; and

• water column temperature: 29oC;

The initial conditions were taken constant throughout the modelled domain. For wa-ter column temperature, the initial condition was taken in between the recording bythe monitoring station in Dragonfly Lake and Kingfisher Lake at March 1st 2013. The


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choice for the initial water level had to be balanced with the total inflow and outflowduring the simulation period to prevent the lake would fall dry during specific time pe-riods. The choice for 102.0 m. is within the water level range as envisaged for Gardensby the Bay (101.6 - 102.2 m.).During the period from June till August, a comparativelydry period, the water level drops only slightly below 101.6 m. for a short period of time.This is also what Thompson (2008) found. At the end of the simulation time, 31st ofNovember 2013, the water level reached 102.85 m. which is also still within the rangeanticipated by Thompson (2008). The choice for 102.0 m. as initial condition is therebyjustified.

Heat flux model Important for accurate modelling of lake water column temperatureis the choice of the heat flux model in Delft3D. The software comes with five separatechoices that are different in the data from which the individual heat fluxes at the air-water interface are derived. Heat flux Ocean (model 5) was used for water columntemperature modelling and requires the following meteorological forcing data:

• air temperature [oC];

• relative humidity [%]; and

• cloud coverage [%].

Net solar radiation [Jm−2s−1] data was manually added to this to explicitly define thenet heat fluxes. Cloud cover data was only available for the period 01 December 2013to 31 March 2013. During this period, there is hardly any variation in the percentagecloud cover. Therefore this study has taken the average cloud cover from this period,72%, as a constant for the entire time period covered by the simulations.

Other meteorological forcing data required for the hydrodynamic model is windspeed [ms−1] and wind direction [o] data and precipitation [mm/hr] and evaporation[mm/hr]. Except for evaporation, all meteorological data for the period 01 March 2013to 31 November 2013 was obtained from the Singapore National Environmental Agency(NEA). Evaporation was calculated internally by the heat flux model.

Precipitation and evaporation The effect of precipitation and evaporation on thewater balance was explicitly included in the water balance. Precipitation and evapora-tion are typically high fluxes in tropical regions and become a relevant component ofthe water balance for shallow water systems. Precipitation was modelled by eight inflowdischarges located at distinct locations from Kingfisher to Dragonfly Lake. The inflowswere taken as a daily flow with constant temperature of 29oC.

Boundary condition The only boundary condition is located at the Dragonfly Lakewhere water is spilled into the Marina Bay Reservoir. A discharge time-series wasderived based on the volume of water coming in from the Marina Bay Reservoir, catch-ment inflows and precipitation and leaving the system through the irrigation offtake andevaporation. The remainder of these components was assumed to leave the system atthe outflow location. Evaporation fluxes were calculated by the model internally. Since


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evaporation fluxes would be determined internally in the model, a constant daily evapo-ration rate was assumed to obtain the discharge time-series at the boundary. As such thewater balance was artificially closed. The outflow to Marina Bay Reservoir is modelledas a constant value for every day, proportional to the amount of inflow into the system.

Discharges The following configurations for inflow discharges were tested:

• runoff modelled as a discharge with 24 hour duration;



In the first scenario, the volume of water associated with the rainfall event was cal-culated and the runoff spread equally over a one day period. For the second scenario,the volume of rainfall water was calculated from the rainfall intensity and duration dataprovided by NEA. In the last scenario a few more cases were distinguished to accountfor different lag-times as presented in table 8. This approach was applied only to point-sources. Non-point sources have a longer retention time and water only seeps into thewater at moderate rates.

Time [h] after rainfall

Scenario 0 1 2 3 4

1 20% 20% 20% 20% 20%2 40% 20% 20% 10% 10%3 60% 20% 10% 8% 2%

Table 8: Different scenarios for the distribution of the runoff for a total duration of the runoff of fivehours.

Hydrodynamic scenarios Three different hydrodynamic scenarios were modelledas part of this study. The scenarios are different in the amount of inflow from the catch-ment. Scenario WQ01 assumes a simplified rainfall-runoff method using the rationalmethod. For each subcatchment the volume of water for a rainfall event is determinedby multiplication of the rainfall intensity and the area of the subcatchment. Five differ-ent land-use types, garden, paved, roof, road and water were distinguished. A runoffcoefficient of 0.7 for garden area, 1.0 for roof area; 0.9 for paved areas and roads and0.0 for water was assigned to different land-use types. The runoff coefficient of 0.0 forwater, was to avoid double counting as precipitation on the water surface has alreadybeen accounted for. Scenario WQ02 does not consider losses due to rainfall at all. Thelast scenario assumes that there is no inflow from the filter bed locations at all. Forthe runoff for other locations it assumes the same rainfall-runoff relation as for scenarioWQ01.


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Verification of the results The results of the hydrodynamic model were verified forthe following:

• closure of the water balance;

• water level within the limits according to Thompson (2008);

• representation of the water column temperature in Dragonfly and Kingfisher Lake.

Water balance closure is important for the correct modelling of heat and nutrientbudgets. Water column temperature was the only state variable for which the resultsof the hydrodynamic model could be truly verified. The performance of the differenthydrodynamic scenarios is given in terms of the mean absolute error (MAE), meansquared error (MSE) and the Nash-Sutcliffe efficiency (NSE). This study did not do anextensive model calibration but focussed on the sensitivity various changes in the modelconfiguration would have on the model outcome. Such changes included variation ininflow discharges. As these aspects have more to do with the model input, a sensitivityanalysis comes in useful to address what additional research is required to reduce theuncertainty in the model outcome. As long as the inputs are not fixed, calibration couldresult in overfitting of the model on the wrong configuration.

4.2.2 Water quality modelling

The water quality model uses the results from the hydrodynamic model as an input. Inaddition it requires specification of a substance-files that gives a complete overview ofstate-variables that are modelled in DELWAQ as well as the processes that are takeninto account. DELWAQ furthermore requires the load or concentration to be specifiedfor each of the inflow locations.

Coupling of the hydrodynamic model to the water quality model The origi-nal hydrodynamic grid was coupled to the water quality model without carrying outaggregation and without removal of inactive cells.

Numerical scheme for water quality modelling This study uses numerical scheme15 as described in Deltares (2011a). It is an unconditionally stable scheme. The numer-ical stability of the 2D and 3D water quality model was assessed using the continuitytracer in DELWAQ. All inflow concentrations were set to 1 g/L as well as the initialconcentration in the entire lake system. Using this scheme and time-step of ten minutes,the performance for Dragonfly Lake was within 7% accuracy and for Kingfisher Lakewithin 3% accuracy. This is acceptable for water quality modelling.

Processes and formulations The following processes were included in the waterquality model for Gardens by the Bay:

• decomposition and mineralisation of detritus phosphorus and detritus nitrogen;

• primary production, mortality and respiration of algae;


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Figure 28: Proposed expansion of the existing water quality model.

• nitrification and denitrification;

• reaeration of oxygen;

• settling of organic matter, suspended solids and algae; and

• extinction of light.

DELWAQ comes with two model descriptions for the modelling of algae, BLOOMand DYNAMO. BLOOM is an extensive phytoplankton model that distinguishes be-tween different algae species. DYNAMO lumps algae species into two groups; diatomsand greens. The first group is composed of algae that need silicate whereas the secondgroup of algae does not. This study uses DYNAMO for it has not data on individualalgae species available. Phytoplankton grazing by zoo-plankton was not taken into ac-count. DYNAMO requires the user to correct the net radiation data for reflection andfor the UV-visible spectrum. It was assumed that 10% of the radiation gets reflectedon the water surface and that 45% of the radiation that penetrates the water is in theUV-visible spectrum. The choices were in accordance with the corrections applied inBLOOM (Deltares, 2011b).


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Suspended solids were taken into account to assess the influence it has on light ex-tinction and thereby on algae growth. There is however no data in Gardens by the Bayon suspended matter.

This modelling study has focused on those processes that take place in the watercolumn. There was no data available that could be used to model the interaction betweenthe water column and sediment layer. Processes that involve such interactions havetherefore been omitted to the extent by which that was possible. Carbon and silicatewere defined as process parameter. Their influence could not be disregarded as they haveinfluence on other processes. DELWAQ describes the sedimentation of detritus nitrogenand detritus phosphorus as proportional to the detritus carbon concentration (Deltares,2011b). Therefore, if sedimentation is to be included, either the carbon cycle has to beadded to the model or detritus carbon has to be specified as a model process parameter.The same holds for silicate. Silicate is required for the primary production of diatomsand was therefore also included in the model as a process parameter. In this stage ofthe water quality modelling it was preferred to keep the model parsimonious so ratherthan including the cycles for carbon and silicate concentrations for both substances weredefined as process parameter. The model assumes a constant value for detritus carbonand detritus silicate of 2.5 and 1.0 g/m3 respectively. In most systems, carbon andsilicate are not the limiting nutrients for algae growth. The levels as chosen in this studyprevents that either of these substances limits the growth of diatoms and green in thewater column.

Other relevant process parameters are shown in table 9 and 10. The numbers in table 9come from Deltares (2011b) except for settling velocities for diatoms and greens whichwere chosen within the range 0.1 to 1 md−1. This range was given on personal noteby Jingjie Zhang (2014). The Secchi extinction was chosen as in Smits (2007) and thesedimentation velocity for IM1 again came on personal note from Jingjie Zhang (2014).

Algal type N:C P:C Si:C Chl-a:C Pmax M R Vsed

[g:g] [g:g] [g:g] [mg:g] [d−1] [d−1] [d−1] [md−1]

Diatoms 0.16 0.02 NA 50 1.2 0.25 0.11 0.2Greens 0.16 0.02 0.49 50 1.2 0.35 0.15 0.2

Table 9: Process parameters for modelling of algae in DYNAMO in DELWAQ.

Discharges The field measurements were used to model the inflow discharges inDELWAQ. For all constituents the average value was used. Except for location 001 and002, the algae contribution was set to zero. For location 001 and 002, the incomingconcentration of greens and diatoms was determined from the measured chlorophyll-aconcentration. The measured chlorophyll-a concentration was converted into biomass[gC] using a carbon to chlorophyll-a ratio of 50 [gC : mgChla] which is the default valueused in DELWAQ (Deltares, 2011b). The biomass was equally divided over diatoms andgreens. DELWAQ separates the contribution by total nitrogen into ammonium, nitrate,


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Name Process / Definition of parameter Value Units

KLRear Reaeration transfer coefficient 1 [md−1]SecchiExt1 Secchi extinction 1.7 [-]V0SedIM1 Sedimentation velocity IM1 0.1 [md−1]

Table 10: Other specified process parameters for modelling in DELWAQ.

detritus nitrogen and other organic nitrogen. The ammonium concentration was calcu-lated as 0.047 times the total nitrogen concentration. Organic nitrogen was calculatedas total nitrogen minus the concentrations for inorganic nitrogen. Adsorbed phosphatemakes up 5% of the total phosphorus concentration. The organic part was then calcu-lated as total phosphorus minus the adsorbed phosphate and ortho-phosphate. For bothnitrogen and phosphorus, the organic part was split into 90% for detritus and 10% forother organic nitrogen and phosphorus. This part was not modelled as other organicnitrogen and phosphorus were not taken into account in the current modelling study.

Model verification and calibration Verification of the water quality model wasdone for dissolved oxygen, chlorophyll-a, total phosphorus and total nitrogen. For allthese variables, the aim is the model is able to capture the long term dynamics correctlyand for dissolved oxygen and chlorophyll-a whether it represents the daily fluctuationsas these are observed in the data from the field. The performance of the current modelis not judged in terms of performance indicators as the deviation between the model andthe observations currently is still too large to make such comparison a useful exercise.

Sensitivity analysis for different loading scenarios A model base run was car-ried out using the averages of all water quality data as it was collected in the field. Inaccordance with Thompson (2008), a reduction of 45% was applied for nutrient loadingwas applied at filter bed location. The sensitivity of the model was addressed by thefollowing additional simulations:

1. inflows as determined in hydrodynamic scenario WQ02-01;

2. inflows as determined in hydrodynamic scenario WQ03-01;

3. reduction of the nutrient load by one time standard deviation for locations 003,004, 005, 006and A001;

4. reduction of the nutrient load by one time standard deviation for inflow fromMarina Bay Reservoir; and

5. increase of the nutrient load by one time standard deviation for inflow from Ma-rina Bay Reservoir.

Table 11 lists the different scenarios addressed in this study and mentions how thedifferent scenarios will be more conveniently referred to in the remained of this report.


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Load

Scenario Hydrodynamics Point Non-point MBR

WQ01-01 2D-01 Measured 45% reduction MeasuredWQ02-01 2D-02 Measured 45% reduction MeasuredWQ03-01 2D-03 Measured 45% reduction MeasuredWQ01-02 2D-01 Reduced 45% reduction MeasuredWQ01-03 2D-01 Measured 45% reduction ReducedWQ01-04 2D-01 Measured 45% reduction Increased

Table 11: Different loading scenarios simulated in this study.


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Model configuration Number of layers MAE [oC] MSE [oC] NSE [-]

σ-layer 10 0.74 0.91 -0.41σ-layer 5 0.34 0.19 0.70σ-layer 3 0.80 1.08 -0.68

Table 12: Comparison of the 3D model configuration using σ layers for the temperature differencebetween the upper and lower model layer for the z-layer model.

4.3 Results

4.3.1 Results from the model test configurations

The hydrodynamic model was extensively tested for the different configurations andscenarios as mentioned in the methodology section. Not all result from the testing willbe presented in this thesis.

Choice for the modelling of discharges It turned out that modelling the inflowdischarges at time scales shorter than one day lead to inconsistencies in the model’swater balance. Spreading the inflow over a larger number of grid cells did not solvethe problem and therefore this study stayed with the first scenario for inflow discharges,modelling the runoff from the catchment as an inflow of 24 hours duration.

Choice for vertical discretisation Although the performance of the z-layer modelwas good in terms of the water column temperature at both locations, the configurationcould not be used as it caused problems in the water quality simulations. The σ-layerwith best approximation of the water column temperature was chosen as an alternative.In order to find the right replacing σ-layer model, a configuration using three, five andten computational layers were modelled for a two month period. Table 12 shows theperformance of these models as compared to the z-layer model. The σ-model usingthree and ten layers are not able to simulate the same vertical temperature differenceas the z-layer model. Only a σ-layer with five layers does give more similar results.In table 12 this becomes clear from smaller values for the mean average error as wellas the smaller root mean squared error. Especially for a number of events whereby thedifference in water column temperature would become large, the σ-layer models withthree and ten layers did not follow the same distribution as the z-layer model. This ismost clearly expressed by the Nash-Sutcliffe efficiency.

4.3.2 Results hydrodynamic modelling

The performance of three different scenarios for catchment runoff on the water columntemperature in Dragonfly Lake and Kingfisher Lake is presented in table 13. Graphsare only shown for the first hydrodynamic scenario, for Dragonfly Lake and KingfisherLake in figure 29 and 30 for the cleaned observation data and in figure 31 and 32 forcorrected temperature data.


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Figure 29: Water column temperature in Dragonfly Lake. Observation data before correction.

Figure 30: Water column temperature in Kingfisher Lake. Observation data before correction.


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Figure 31: Water column temperature in Dragonfly Lake. Observation data after correction.

Figure 32: Water column temperature in Kingfisher Lake. Observation data after correction.


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Simulation MAE [oC] MSE [oC] NSE [-] MAE [oC] MSE [oC] NSE [-]WQ01 0.52 0.38 0.73 0.64 0.63 0.72WQ02 0.49 0.34 0.76 0.65 0.65 0.71WQ03 0.61 0.51 0.64 0.64 0.63 0.72

Table 13: Performance for temperature representation for three different scenarios of inflow dis-charges.

The influence in Kingfisher Lake is especially small as most of the inflow locationsare located downstream from the monitoring station. Also for Dragonfly Lake, theinfluence is minor.

At this stage a definite choice for the catchment inflow was not made yet. For thisthe effect of the different loading scenarios has to be assessed first in the water qualitymodel. This however is not done as part of the current study.

4.3.3 Results water quality modelling

Figure 33 to figure 40 present the modelled water quality for dissolved oxygen [mg/L],chlorophyll-a [µg/L], total nitrogen [mg/L] and total phosphorus [mg/L] for Dragonflyand Kingfisher Lake against the observed values. For dissolved oxygen the observedvalues come from the monitoring station; for chlorophyll-a both the measurements fromthe monitoring station and laboratory analysis are presented and for total phosphorusand total nitrogen the comparison is only through the monthly samples. Observationsand model results are visualised as daily averages.

The model underestimates the dissolved oxygen levels observed in Dragonfly Lake.Those in Kingfisher Lake are neither captured well with overestimation during Septem-ber and underestimation in November. The last part of November the model againshows an overestimation as compared to the measurement. However, as was shown inthe chapter on data correction, the dissolved oxygen concentration has most likely notbeen measured well during this period and an uplift of 2.3 mg/L may apply. That wouldbring model and measurement much closer to one another.

Diurnal fluctuations in dissolved oxygen concentrations are not clearly visible fromthe graphs presented in this study due to averaging. Although not shown here, theextent too which the model represents diurnal fluctuations is less than is observed bythe monitoring stations.

Chlorophyll-a levels are underestimated for both lakes most of the time. Only the pe-riod from half August to the beginning of September shows acceptable results for King-fisher Lake. The laboratory measurements show an upward trend of the chlorophyll-alevels for these four months. This is nowhere captured by the model.

The modelling results for total nitrogen and total phosphorus do hardly show anyvariation on longer and shorter time scales. On the shorter time scale there is some effect


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of catchment inflows but this certainly does not dominate the nutrient concentrations inthe water column.

Spatial differences in concentrations for water quality variables The spatialdifferences for a ten day period from 01 September 2013 to 10 September 2013 arepresented in figure 41 to 44. This period was chosen because of numerous rainfall daysduring this period. It shows clearly how the water quality gets affected by the inflowfrom the catchment. Furthermore it shows the gradual change of the water quality fromKingfisher Lake through the Saraca stream to Dragonfly Lake.

Three-dimensional water quality modelling The results from three-dimensionalwater quality modelling show hardly any difference for surface and bottom water qual-ity at the deepest point of the lake system. In table 14 the differences between themodel results for dissolved oxygen, chlorophyll-a, total nitrogen and total phosphorusin the surface layer and bottom layer in Dragonfly Lake are summarised. The aver-age difference, maximum and minimum difference are given in the table. It indicatesthat the differences in water quality are small, even at the deepest location of the lakesystem. Denitrification in the water column is not taking place due to permanent oxicconditions. The three dimensional model confirms that even at the deepest location theoxygen concentrations never reach lower than 4.28 mgL−1 in Dragonfly Lake.

4.3.4 Sensitivity analysis for the water quality model

The model’s sensitivity for inflow quantity was assessed in scenario WQ02-01 wherebythe catchment runoff was taken equal to the rainfall; and scenario WQ03-01 whichassumed there would be no outflow at all from filter bed locations. The results of the firstsensitivity analysis are shown in figure 45 and 48 for total nitrogen and total phosphorusin Dragonfly and Kingfisher Lake. Results for chlorophyll-a and dissolved oxygen arenot shown because they did not deviate much for the different scenarios.

Figure 49 to 52 show the result for the load reduction scenario. The influence inKingfisher Lake for both total nitrogen and total phosphorus is smaller as compared tothe observed difference in Dragonfly Lake.

Figure 53 to 56 show the result of the influence changes in the total phosphorus andtotal nitrogen load have on the concentrations for these variables in Kingfisher and Drag-onfly Lake. Dragonfly Lake shows the expected results with a lower total nitrogen andtotal phosphorus concentration for this scenario. For the reduction scenario, the totalphosphorus concentration is almost 0.10 mgL−1 lower and for total nitrogen the dropamounts approximately 0.40 mgL−1. For increase in load from the Frog Pond, Drag-onfly Lake responds with an on average increase of the total nitrogen concentrationby 0.40 mgL−1 and 0.04 mgL−1 increase in the total phosphorus concentration. ForKingfisher Lake, the results for scenario WQ01-04 are as expected with higher concen-trations for both total nitrogen and total phosphorus. For scenario WQ01-03 however,there is almost no difference with the concentration for nutrients as compared to sce-nario WQ01-01.


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Figure 33: Dissolved oxygen concentration [mg/L] Dragonfly Lake.

TN TP DO Chl-a[mgL−1] [mgL−1] [mgL−1] [µgL−1]

Average 0.00 0.00 -0.05 0.07Maximum 0.01 0.00 0.00 0.37Minimum -0.01 0.00 -0.21 -0.15

Table 14: Hardly any difference in water quality is observed for the surface layer and the bottomlayer for any of the modelled water quality variables.


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Figure 34: Dissolved oxygen concentration [mg/L] Kingfisher Lake.

Figure 35: Chlorophyll-a concentration [µg/L] Dragonfly Lake.


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Figure 36: Chlorophyll-a concentration [µg/L] Kingfisher Lake.

Figure 37: Total nitrogen concentration [mg/L] Dragonfly Lake.


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Figure 38: Total nitrogen concentration [mg/L] Kingfisher Lake.

Figure 39: Total phosphorus concentration [mg/L] Dragonfly Lake.


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Figure 40: Total phosphorus concentration [mg/L] Kingfisher Lake.

Figure 41: Dissolved oxygen concentration at the water surface for the period 01 September 2013to 10 September 2013. Due to high catchment runoff during this period there is a gradual change inthe dissolved oxygen concentration visible.


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Figure 42: Chlorophyll-a concentration at the water surface for the period 01 September 2013 to 10September 2013. Due to high catchment runoff during this period there is a gradual change in thechlorophyll concentration visible.


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Figure 43: Total nitrogen concentration at the water surface for the period 01 September 2013 to 10September 2013. Due to high catchment runoff during this period there is a gradual change in thetotal nitrogen concentration visible.


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Figure 44: Total phosphorus concentration at the water surface for the period 01 September 2013to 10 September 2013. Due to high catchment runoff during this period there is a gradual change inthe total phosphorus concentration visible.


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Figure 45: Scenario WQ01-01 and WQ02-01 have similar total nitrogen concentration; ScenarioWQ03-01 leads to lower concentrations for total nitrogen in Dragonfly Lake.

Figure 46: The results for Kingfisher Lake are highly similar since the discharges for which thecharacteristics were changed are mostly located downstream the observation point in KingfisherLake.


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Figure 47: Scenario WQ01-01 and WQ02-01 have similar total phosphorus concentration; ScenarioWQ03-01 leads to higher concentrations for total phosphorus in Dragonfly Lake.

Figure 48: The results for Kingfisher Lake are similar to the observation for total phosphorus in thislake. The location of the discharges makes that there is hardly any effect here.


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Figure 49: Comparison of total nitrogen concentration in Dragonfly Lake for scenario WQ01-01 andWQ01-02.

Figure 50: Comparison of total nitrogen concentration in Kingfisher Lake for scenario WQ01-01 andWQ01-02.


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Figure 51: Comparison of total phosphorus concentration in Dragonfly Lake for scenario WQ01-01and WQ01-02.

Figure 52: Comparison of total phosphorus concentration in Kingfisher Lake for scenario WQ01-01and WQ01-02.


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Figure 53: Comparison of the total nitrogen concentration in Dragonfly Lake for the base scenario(WQ01-01) with higher and lower loads from Marina Bay Reservoir.

Figure 54: Comparison of the total nitrogen concentration in Kingfisher Lake for the base scenario(WQ01-01) with higher and lower loads from Marina Bay Reservoir.


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Figure 55: Comparison of the total phosphorus concentration in Dragonfly Lake for the base scenario(WQ01-01) with higher and lower loads from Marina Bay Reservoir.

Figure 56: Comparison of the total phosphorus concentration in Kingfisher Lake for the base sce-nario (WQ01-01) with higher and lower loads from Marina Bay Reservoir.


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4.4 Discussion

4.4.1 Model predictive capacity

Water column temperature at both Dragonfly Lake and Kingfisher Lake have been mod-elled within 0.5oC accuracy on average. As the inflows are modelled as daily averages,the temperature of those discharges is also constant. This study uses the daily averageair temperature as an estimation of the temperature of inflow discharges. By doing so,the discharges impose more information from the meteorological data onto the watercolumn temperature in addition to the heat flux model. Therefore, the model will notbe able to represent anomalies that may exist between the modelled and observed watercolumn temperature that are associated with cold water discharges. Furthermore, thedaily fluctuation in the modelled water column temperature could be tampered at dayswith high runoff. The model is not able to capture the fluctuations seen in the obser-vations for dissolved oxygen, chlorophyll-a, total nitrogen and total phosphorus. Thisis likely due to the use of average values for all water quality variables over the entiresimulation period. As a result, variation in the characteristics for individual inflows isneglected. As a result, the concentrations for these variables now have a tendency tobe constant and do show minor fluctuations during rainfall events. Diurnal fluctuationsfor dissolved oxygen in the model are smaller as compared to field observations. Pos-sibly, this is due to lower concentrations for chlorophyll-a in the model as compared tothe measurements. Lower chlorophyll-a concentrations imply lower primary productionand respiration rates. This will impede the fluctuation in dissolved oxygen.

4.4.2 Discussion of the results from sensitivity analysis

Non-point sources contribute 37.4% of the total phosphorus load and 38.2% of the to-tal nitrogen concentration as a percentage of the total load from the catchment (thatis excluding the inflow from Marina Bay Reservoir). The relative contribution of in-flow location 001 to the nutrient loading is 13.4% for total nitrogen and 5.1% for totalphosphorus, indicating a more dominant effect of this location on the total nitrogen con-centration in the lake system as compared to the total phosphorus concentration. Thissampling location however does not only represent the inflow from the catchment at thissite, it includes the inflow from the Marina Bay Reservoir as well. As was seen in chap-ter 1, the total phosphorus concentration from Marina Bay Reservoir is low as comparedto the other point-sources whereas the total nitrogen concentration is high compared tothe other point-sources. For scenario WQ01-01, if Marina Bay Reservoir is includedin the calculation of the relative contribution to the external load, it signifies its impor-tance to the nitrogen loading (48.9%) as compared to total phosphorus (26.5%). Thisexplain why scenario WQ03-01 gives the higher concentrations for total nitrogen andlower concentration for total phosphorus. In scenario WQ03-01, the contribution by theMarina Bay Reservoir to total nitrogen increases to (60.8%) and to (36.6%) for totalphosphorus. That the effect in Kingfisher Lake is less pronounced makes sense since itis dominated by the inflow from Marina Bay Reservoir in both scenarios.

In scenario WQ03-01, the filter bed locations and bioswales are assumed not toodischarge at all into the lake system. As compared to scenario WQ01-01, the totalnitrogen concentration in the lake goes up whereas the total phosphorus concentration


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goes down. As the volume of water in the lake does not change for the two scenarios,the effect of eliminating discharge from these locations is that the concentrations inthe lake get attuned to the ensemble concentration from inflow from the Marina BayReservoir and the other catchment inflows. For total nitrogen, these concentrationsare typically higher whereas for total phosphorus they are slightly lower. Therefore, thechange in concentration for total nitrogen and total phosphorus shows an opposite effectfor scenario WQ03-01.

Results of scenario WQ01-02 are as expected. The influence of load reduction forsurface water inflows is negligible in Kingfisher Lake due to the location of the inflowdischarges. In Dragonfly Lake the effect for both total nitrogen and total phosphorus isclearly visible.

Marina Bay Reservoir has impact on the nutrient concentration in both Dragonflyand Kingfisher Lake. For the latter, the effect is more pertinent because it is the largestinflow to the lake. Due to its short retention time its characteristics seem to have atendency to follow those of this inflow.

4.4.3 Mass balances for total nitrogen and total phosphorus

The field measurements and model results were combined to obtain a first insight inthe nutrient budgets for Gardens by the Bay. The load for each inflow location wasdetermined by multiplying the average concentration measured for total phosphorusand total nitrogen at each inflow location with the discharge according to the rainfall-runoff relation for the associated subcatchment. The contributions from the Marina BayReservoir and lake transfer system were known with more certainty because the inflowquantity was provided by Gardens by the Bay. Outflow of nutrients was determined bymultiplying the outflow from Dragonfly Lake and offtake for irrigation by the measuredconcentration in Dragonfly Lake. The sampling location in Dragonfly Lake is locatedboth near to the irrigation offtake and the overflow to Marina Bay Reservoir and wastherefore used as the approximate concentration of the outgoing water for both thesedischarges. The difference between inflow and outflow for nutrients is presented as thenet increase of total phosphorus and total nitrogen in figures 57 and 60.


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Figure 57: Total nitrogen balance for scenario WQ01-01.

Figure 58: Total phosphorus balance for scenario WQ01-01.


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Figure 59: Accumulation of total nitrogen in biomass and sediment takes place in Gardens by theBay.


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Figure 60: Accumulation of total phosphorus in biomass and sediment takes place in Gardens bythe Bay.


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4.5 Conclusions and recommendations

The following conclusions are drawn from the hydrodynamic model:

• the thermal regime of the lake is predominantly determined by the meteorologicalconditions rather than the inflows; and

• Dragonfly Lake may be diurnally stratified due to the combination of a sharptemperature difference and the small flow velocities.

The different hydrodynamic scenarios do not change the temperature profile in Drag-onfly and Kingfisher Lake significantly. The small variation suggests that the temper-ature from inflow locations is not affecting the lake water column temperature signifi-cantly and that the water column temperature is governed by the meteorological condi-tions. It is however noted that to stabilise the model, the discharges were modelled asa constant flow with duration of 24 hours and that for the temperature the daily averagetemperature was assumed. Therefore, important variation might have been taken outfrom the inflow discharges and the temperature representation might have been mademore homogeneous in an artificial manner. Although the lake may be diurnally strati-fied, it does not result in any significant differences for the vertical distribution of dis-solved oxygen, chlorophyll-a, total phosphorus or total nitrogen. The modelled algaepopulation could be higher based on stoichiometric relations and the nutrient to carbonratios assumed in this modelling study. This suggests that the algae production is notlimited by nutrients. It is possible that instead the lake is limited by radiation. Thelakes have been rather turbid throughout time, as an effect of ongoing constructionsnear Kingfisher Lake.

This study has presented the nutrient budgets for total phosphorus and total nitrogen.The measurements do not show an increase of concentration in the water column. Witha net inflow of total nitrogen and total phosphorus, it is expected that deposition istaking place in biomass and in the sediment layer. Denitrification has not been takeninto account as a loss term. This is in accordance with the model results that showconstant oxic conditions and therefore no losses of nitrogen through denitrification.

At last, the budgets for nitrogen and phosphorus do not account for atmospheric de-position which could be another important source of nutrients. Lake shallowness makesthat atmospheric deposition per volume water will be more significant than for lakes inthe same region but which are deeper.

Considering the limitations of the current study, the following paths are indicated forthe improvement of the understanding of the system and for further improvements ofthe model:

• monthly sampling in the Frog Pond to have time-varying input data from thelocation that contributes 50% of the volume of water to the lake system;

• sampling of the sediment layer to study the nutrient composition of the sedimentlayer and study the potential accumulation of at the bed; and

• additional monthly measurement of Secchi disk depth for direct comparison ofthe modelled lake turbidity with field observations.


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5 Recommendations and directions for future work

The last chapter of this thesis will discuss numerous recommendations and directionsfor future work. The water quality monitoring and modelling studies for Gardens bythe Bay will continue for two more years and should provide Gardens by the Bay witha water quality management program. A well-calibrated water quality model is onecomponent of such water quality program as it can be used as a tool for the predictionof water quality and scenario analysis. This study is the first step in developing thewater quality model. Its results are used to give direction to the project and to refine theproject scope. This chapter is structured into recommendations that concern uncertaintyreduction, data correction and water quality modelling.

5.1 Recommendations for further reduction of modellinguncertainties

Additional experiments could reduce the uncertainty in the water quality model for Gar-dens by the Bay much further. A number of recommendations will follow under therecommendation that involve water quality modelling. This section will focus on rec-ommendations that involve improvements of the measurement and sampling practicesso to improve the accuracy of the measured data. Furthermore it addresses which ad-ditional information Gardens by the Bay could be asked for so to reduce uncertainty inthe model.

5.1.1 Handling of samples

The procedure of handling samples is to be improved to increase the reliability of theperformed laboratory tests. Preferably, digestion is carried out directly after collectionof the samples, and all samples stored in the freezer. This will ensure the quality of thesample, and also of the digested samples. Furthermore, collection of samples in darkbottles would be preferred over the plastic bottles that were used in the current study.

5.1.2 Chlorophyll-a analysis

Improvements into the analysis for chlorophyll-a are required. The instrument mea-sures fluorescence with an accuracy of three decimals. Hence, considering two timessubtraction of fluorescence values, the accuracy of the measurement for fluorescence isx +- 0.002. For the same measurement the chlorophyll-a concentration could thus devi-ate 6.7 µgL−1 up and down. The chlorophyll-a concentrations measured in DragonflyLake and Kingfisher Lake are about 10 to 15 µgL−1 This implies a minimum percent-age error 30%. The accuracy can be improved by increasing the filtered sample volume.Increasing the volume for filtration to 200 mL would decrease the uncertainty boundto 2.7 µgL−1 up and down. This results in an approximate maximum error of 30% formeasurements in Dragonfly Lake and Kingfisher Lake. Increasing the volume for fil-tration is only possible if the turbidity of the water is not too high. Furthermore, it isadvised to timely adjust the chlorophyll-a readings by the monitoring stations to avoidlarge deviations due to delay in sample collection and sample analysis.


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5.1.3 Total phosphorus analysis

The analysis for total phosphorus using colorimetric method has a 0.02 mg/L sensitivityif the current practices are followed. The highest concentrations detected in Gardensby the Bay are 0.42 mg/L for Kingfisher Lake and 0.28 mg/L for Dragonfly Lake. Forinflow measurements 1.64 mg/L was the highest detected level. On average, the concen-tration of total phosphorus in the surface runoff is such that the uncertainty is 10%. Forsurface water inflow the accuracy of the method may thus be sufficient. For the monthlysamples drawn from Dragonfly and Kingfisher Lake, the accuracy of the measurementhas to be improved.

5.1.4 Nitrite, nitrate and total nitrogen analysis

Also for the analysis of nitrite, nitrate and total nitrogen, improvements to the currentprocedure are suggested to decrease the uncertainty in the data collection. This studyused 5 ppm as lowest calibration standard for nitrite and nitrate. In Kingfisher Lake,nitrate concentrations have never exceeded 3.9 mg/L and in Dragonfly Lake, the highestconcentration detected is 1.3 mg/L. Total nitrogen levels were 6.5 and 5.3 mg/L at most,meaning that ion chromatographer detected 3.3 and 2.7 mg/L because of dilution dur-ing autoclave digestion. All values for nitrite, nitrate and total nitrogen therefore do notexceed the concentration of the first calibration standard. For surface water inflows, thehighest total nitrogen level was 18.8 mg/L and thus corresponds with 9.4 mg/L as mea-sured per ion chromatographer. Although this falls within the range of the calibrationstandards, adjustment of the standards concentration would be preferred as 36 out of42 samples have TN concentrations below 10 mg/L hence, below 5 mg/L in the dilutedsamples used in the ion chromatographer.

5.1.5 Phosphate analysis

Phosphate values are determined in the anion application in ion chromatographer. Thelowest calibration standard is 7.5 mg/L, which is much higher than what is commonlydetected in surface waters and approximately three times higher than the highest con-centration observed in Gardens by the Bay. To improve the reliability of the samplesanalysis for phosphate, lower standards are required for higher accuracy of the measure-ments. The 7-anion standard contains fractions of nitrite, nitrate and ortho-phosphate in2:2:3. In order to analyse all samples in one run, the highest concentration observed sofar will determine the concentration for the calibration standard with the highest concen-tration. In that respect, only the highest concentration can be dropped as total nitrogenlevels in surface water inflows can be as high as 10 mg/L. Therefore, it is better to in-crease the number of calibration standards in the lower range from 0.01 mg/L nitriteand nitrate and 0.015 mg/L ortho-phosphate to 5 mg/L nitrite and nitrate and 7.5 mg/Lortho-phosphate. Samples from the lake require only a standard till 5 mg/L nitrite andnitrate and additional calibration standards in the lower range to detect concentrationsof samples more accurately.


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5.1.6 Additional data collection

Time varying input data The current study only has sampling data from a six weekperiod for twelve locations. In some instances the number of measurements is less dueto the nature of the inflow. This is too little information to expected significant time-varying behaviour in the model that is driven other than by the catchment inflow quantityand meteorological conditions. Additional data collection could involve addressing theinter-event time-variability and outer-event time-variability. The first, inter-event time-variability, could be assessed by using an autosampler at locations 001 to 006. In thisway, the distribution of the load from the catchment can be determined with more accu-racy. The outer-event variability is the variability from event to event and would resultin more time-variant behaviour in the model.

Verification of the catchment loading with land-use and fertilizer applicationThis study has determined the total nitrogen and total phosphorus load per sub-catchment.The relative contribution for each catchment can be tested for correlation with the land-use type to identify whether loads are attributed to land-use in the catchment. Further-more, the loading per subcatchment has to be verified against the fertilizer application inthe gardens. The timing of application and amount of fertilizer used would be relevantinformation to address relations between inflow concentrations and fertilizer use in thecatchment.

5.2 Recommendations experimental and statistical datacorrection

5.2.1 Data correction by verification measurements

The comparison of water quality monitoring data with verification measurements is arobust method to identify potential drifts in measurement data. However, it is a labourintensive and time-consuming tasks and therefore not a preferred method for a long pe-riod of time. Collection of such series for a larger number of calibration events can assistto assess whether similar patterns recur. A time-series that covers only one calibrationperiod does not point this out.

The significance levels for Student’s t-test and Mann-Kendall test for trend detectionhave been chosen without consideration of the physics of the system. The choice forthe significance levels for both statistical tests implicitly states what error bounds areregarded acceptable. A sloping trend in a water quality variable can be significant, yetif calibration is carried out frequently, may not have a detrimental effect on the absoluteerror of the measurement. For Student’s t-test it implies a level of bias that is acceptedfor every variable for which the test is carried out. Such effects have not been taken intoaccount in this study and it is strongly recommended that for new comparative studies,the significance levels are chosen in accordance to the accepted error limits.

5.2.2 Data correction by linear regression

Data recovery for historical time-series using a regression method has been successfullyapplied for temperature data. The spatial correlation for temperature data can be used


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during the remainder of the project to fill data gaps or replace untrusted data for watercolumn temperature in Dragonfly or Kingfisher Lake. The performance of the currentregression should be validated on new data that will be coming in from the monitoringstations in May 2014. If the performance for this period is not good, additional datamight be required to retrain the regression. It is suggested to look into other, non-linear,regression methods to correct data for dissolved oxygen.

5.3 Recommendations for further water quality modelling

According to the results from this study, the concentrations for dissolved oxygen, chlorophyll-a, total phosphorus and total nitrogen do hardly change in Kingfisher Lake. The char-acteristics of the lakes in terms of these water quality variables in strongly influencedby the inflow from the Frog Pond of which the Marina Bay Reservoir forms by far thelargest inflow. The large inflow quantity as compared to the small volume of the lakemakes that the retention time of water in Kingfisher Lake is short and the character-istics dominated by the characteristics of the main inflow. Time-series data were notavailable for this study and hence the average of six measurements taken during the pe-riod 26 November 2013 to 6 January 2014 were taken as the inflow concentrations fora four month period stretching from 01 August 2013 to 31 November 2013. From theobservation in this study, the following concrete actions for future work are suggested:

• collection of time-series data in the Frog Pond for comparison of the water qualitywith Kingfisher Lake, possible followed by monthly water quality monitoring atthis location;

• additional monthly measurement of Secchi disk depth for direct comparison ofthe modelled lake turbidity with field observations;

• sampling of the sediment layer to study the nutrient composition of the sedimentlayer and study the potential accumulation of at the bed;

• model the water quality for the period 26 November 2013 to 6 January 2014using field data as it was collected during the six measurement and sampling eventduring this period to assess the model’s sensitivity, especially the response inKingfisher Lake, to time-varying data input; and

• assess the necessity of three dimensional hydrodynamic and water quality mod-elling.

5.3.1 Collection of time-series data in the Frog Pond

Collection of time-series data in the Frog Pond would have the purpose of comparingthe measurements in Kingfisher Lake with those in the Frog Pond. If the recordingsdo agree well, it confirms the conclusion drawn in this study that the water quality inKingfisher Lake is determined by the water quality of its major inflow. This woulddisseminate the necessity of extensive hydrodynamic and water quality modelling forKingfisher Lake. It is suggested to make such comparison ones during a dry periodand ones during a wet period in order to see the separate effect of inflow from the


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Marina Bay Reservoir compared to the effect of the combined inflow from Marina BayReservoir and catchment runoff from the Frog Pond. It is suggested to measure at adaily frequency for a period of one month to obtain a representative time-series forcomparison. If there is a distinct difference between the water quality coming in fromthe Frog Pond and the water quality observed in Kingfisher Lake, it is suggested tocontinue water quality measurements in the Frog Pond ones every month and draw awater quality sample on a monthly basis as well for the analysis of chlorophyll-a, totalphosphorus and total nitrogen. The additional measurements can be easily aligned withmonthly visits for servicing of the equipment and sample collection in Dragonfly andKingfisher Lake.

5.3.2 Monthly Secchi disk measurement

There is a mismatch between the units for turbidity data collected in the field and turbid-ity values in the water quality model. The sensors from the monitoring stations measureturbidity in NTU whereas the water quality model gives suspended matter concentra-tions and Secchi disk as measured of the turbidity in the water column. It is suggestedto bring a Secchi disk during monthly sampling to determine the Secchi depth in Drag-onfly Lake and Kingfisher Lake using a measure that directly compares with the outputvariable in the water quality model. Furthermore, a relation between Secchi depth [m]and turbidity [NTU] for the sensor could possibly be inferred from the calibration stan-dards. This option should be taken into account.

5.3.3 Modelling water quality with time-varying input data

This recommendation is another way to compare the response for dissolved oxygen,chlorophyll-a, total phosphorus and total nitrogen in Kingfisher Lake to the inflow fromthe Frog Pond. By using the little time-variant data that was collected, it can be veri-fied whether the water quality in Kingfisher is indeed sensitive to the water quality inthe Frog Pond and how well it agrees with the observations. It should be noted thatthe model configuration yet is incomplete and could attribute to disagreement betweenobserved and modelled data even if time-series data for the inflow from the Frog Pondis considered. Furthermore, the monitoring station has not been calibration after 17December 2013 and the observed data should therefore be interpreted first for its ap-plicability for such comparison. In addition to this, the relevant meteorological forcingdata is currently not available from 01 December 2013 onwards. Either this data is to bepurchased or data from another period should be used and taken as an uncertainty in themodel outcome. Rainfall data would be most relevant in order to quantify the catchmentinflow and determine the catchment loads.

5.3.4 Assess the necessity of three-dimensional hydrodynamic and waterquality modelling

As was shown in the chapter on water quality modelling, there is hardly any differenceobserved for dissolved oxygen, chlorophyll-a, total phosphorus and total nitrogen atthe deepest location of the Gardens by the Bay lake system. This implies that three


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dimensional modelling might not be necessary in order to represent the water quality inGardens by the Bay well. The model could be expanded to include the modelling of theinteraction between the water column and sediment layer in more detail. Benthic algaeand sediment oxygen demand by mineralisation of organic matter at the bottom couldlead to a larger difference for dissolved oxygen and a different distribution for algae inthe system.

Possibly it is not even necessary to have a hydrodynamic calculation advancing thewater quality simulations. There are no distinct flow patterns observed in the currentmodel simulations that would result in a spatially distributed water quality. There is agradual change from Kingfisher Lake to Dragonfly Lake for the water quality variablesthat were considered in this study. Changes are observed, especially in the Saraca streamand Dragonfly Lake for rainfall events. Local geometric features, such as the islandslocated in Dragonfly Lake could have had an influence on the flow patterns. That is whyinitially the hydrodynamic behaviour of the model was determined first.


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Water quality modelling for Gardens by the Bay, Singapore€¦ · a change in the concentration from Marina Bay Reservoir is less ampliﬁed and is around 0.05 mgL 1for total phosphorus