Applied Science, Faculty of
Civil Engineering, Department of
DSpace
UBCV
Jamali, Sara
Master of Applied Science - MASc
University of British Columbia
In recent decades temperate lakes have experienced notable changes in their thermal regimes, including extended durations of summer stratification and shorter ice-covered periods. In addition, recent studies have shown that the presence of low levels of salinity can have dramatic effects on the circulation of ice-covered lakes. For example, the exclusion of salt from ice as it forms can result in a relatively fresh surface layer immediately after ice melt. The density contrast between the ice-melt and the more saline water beneath it can be suﬀicient to hinder, or even prevent, turnover. These changes have far-reaching ecological consequences, influencing oxygen availability, nutrient cycling, and biological productivity. Base Mine Lake Demonstration (BML) in Alberta, Canada (57°1'N, 111°37'W) is the first full-scale demonstration of an end pit lake in the oil sands industry. BML was formed by the backfilling of a previously mined-out pit with fluid fine tailings, followed by the addition of fresh water from a nearby reservoir. The evolution of the resulting brackish lake has been actively monitored since its commissioning in 2012. In the present study we investigate the inhibition and dynamics of spring turnover, and the establishment of summer stratification, in BML. Field observations from spring and summer of 2016 and 2017 are analyzed using a conceptual model, and compared with simulations from the one-dimensional General Lake Model (GLM), to assess changes in the temperature stratification during notable wind events. Results indicate that although significant mixing occurs after ice-off, complete turnover is not achieved; instead, the lake experiences long periods of upwelling/downwelling and short periods (hours) of partial spring turnover. Additionally, the dynamics at BML are influenced by the expression of saline pore water from the settling of the underlying tailings. These findings highlight both the complex mixing dynamics within BML and the limitations of using a one-dimensional model to represent this system.
Stratification During Spring andSummer in a Brackish Lake Subject toIce CoverbySara JamaliB.Sc., Sharif University of Technology, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)September 2025© Sara Jamali 2025The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the thesis entitled:Stratification During Spring and Summer in a Brackish Lake Subject to Ice Coversubmitted by Sara Jamali in partial fulfillment of the requirements for the degree ofMaster of Applied Science in Civil Engineering.Examining Committee:Gregory A. Lawrence, Professor, Department of Civil Engineering, UBCSupervisorBernard Laval, Professor, Department of Civil Engineering, UBCCo-SupervisoriiAbstractIn recent decades temperate lakes have experienced notable changes in their thermal regimes,including extended durations of summer stratification and shorter ice-covered periods. In ad-dition, recent studies have shown that the presence of low levels of salinity can have dramaticeffects on the circulation of ice-covered lakes. For example, the exclusion of salt from ice asit forms can result in a relatively fresh surface layer immediately after ice melt. The densitycontrast between the ice-melt and the more saline water beneath it can be sufficient to hinder,or even prevent, turnover. These changes have far-reaching ecological consequences, influencingoxygen availability, nutrient cycling, and biological productivity.Base Mine Lake Demonstration (BML) in Alberta, Canada (57°1'N, 111°37'W) is the firstfull-scale demonstration of an end pit lake in the oil sands industry. BML was formed by thebackfilling of a previously mined-out pit with fluid fine tailings, followed by the addition of freshwater from a nearby reservoir. The evolution of the resulting brackish lake has been activelymonitored since its commissioning in 2012.In the present study we investigate the inhibition and dynamics of spring turnover, and theestablishment of summer stratification, in BML. Field observations from spring and summerof 2016 and 2017 are analyzed using a conceptual model, and compared with simulations fromthe one-dimensional General Lake Model (GLM), to assess changes in the temperature strati-fication during notable wind events. Results indicate that although significant mixing occursafter ice-off, complete turnover is not achieved; instead, the lake experiences long periods ofupwelling/downwelling and short periods (hours) of partial spring turnover. Additionally, thedynamics at BML are influenced by the expression of saline pore water from the settling of theunderlying tailings. These findings highlight both the complex mixing dynamics within BMLand the limitations of using a one-dimensional model to represent this system.iiiLay SummarySpring turnover is an important period in a lake’s seasonal cycle when strong winds mix thelake, bringing oxygen-rich surface water to the bottom and nutrient-rich bottom water to thesurface. This process supports aquatic life by replenishing oxygen and redistributing nutrients.However, factors like salinity can prevent this mixing.This study investigates the mixing dynamics of Base Mine Lake Demonstration (BML), aman-made lake in northern Alberta, Canada’s oil sands region, during the spring and summerof 2016 and 2017, using temperature measurements and numerical modelling. Results showthat BML does not fully mix in spring. Instead, mixing only occurs in parts of the lake andlasts for just a few hours. This limited mixing is influenced by BML’s salt content, surface oillayers, and unique conditions created by mining materials at the lake bottom. Understandingthese dynamics is crucial for predicting future water quality in BML and other similar reclaimedlakes.ivPrefaceThis thesis presents original research conducted by the author under the supervision of Dr. Gre-gory Lawrence, Dr. Edmund (Ted) Tedford, Dr. Bernard Laval, and Dr. Roger Pieters. Fielddata used in this research were collected by several collaborators: mooring and CTD measure-ments were collected by Dr. Tedford; radiation measurements along with inflow and outflowdata were provided by Dr. Gordon Drewitt and Dr. Sean Carey from McMaster University;meteorological and water quality data were collected by Hatfield Consultants; and bathymetrydata were provided by Syncrude Canada Ltd.Chapter 2 describes the calibration of a numerical model, which was carried out by theauthor with guidance from Dr. Lawrence, Dr. Tedford, Dr. Laval, and Dr. Pieters. Chapter 3presents an analysis of field measurements and model results, conducted by the author withguidance from Dr. Lawrence and Dr. Tedford. Chapter 4 outlines a conceptual model devel-oped by the author with guidance from Dr. Lawrence. All numerical model simulations wereperformed independently by the author. Generative Artificial Intelligence (ChatGPT, OpenAI)was used to assist with proofreading of thesis drafts.The work presented in this thesis reflects my own contributions, with valuable guidance andfeedback provided by my supervisory committee throughout the research process.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Wind Effects on Lakes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Base Mine Lake Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.3 Study Motivations and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 121.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.1 Site Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2 Field Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3 General Lake Model (GLM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.1 Energy Balance and Mixing . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.2 Solar Heating and Light Penetration . . . . . . . . . . . . . . . . . . . . 222.3.3 Sediment Heating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23viTable of Contents2.4 Model Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.5 Preliminary Processing of Field Data . . . . . . . . . . . . . . . . . . . . . . . . 302.5.1 Temporal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.5.2 Horizontal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.6 Validation Parameters and Evaluation Metrics . . . . . . . . . . . . . . . . . . . 322.6.1 Heat Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.6.2 Schmidt Stability Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.6.3 Seiching Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342.6.4 Root Mean Square Error . . . . . . . . . . . . . . . . . . . . . . . . . . . 342.7 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.1 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.1.1 Meteorological Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.1.2 Mooring, CTD and DO Data . . . . . . . . . . . . . . . . . . . . . . . . . 403.1.3 Internal Seiche Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.2 Summer Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.2.1 Summer 2017 (Day of Year 171.5 - 224.0) . . . . . . . . . . . . . . . . . . 493.2.2 Summer 2016 (Day of Year 187.5 - 225.0) . . . . . . . . . . . . . . . . . . 553.3 Spring Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.3.1 Spring 2017 (Day of Year 131.5 - 172.0) . . . . . . . . . . . . . . . . . . . 613.3.2 Spring 2016 (Day of Year 124.625 - 188.0) . . . . . . . . . . . . . . . . . 684 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.1 Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.2 Spring Turnover in Base Mine Lake Demonstration . . . . . . . . . . . . . . . . 804.2.1 Spring Turnover in 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . 804.2.2 Spring Turnover in 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . 834.2.3 Implications for Dissolved Oxygen . . . . . . . . . . . . . . . . . . . . . . 864.3 Model Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87viiTable of Contents5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92AppendicesA Example of GLM Namelist File . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99B Heat Content and Thermal Stability . . . . . . . . . . . . . . . . . . . . . . . . . 113C Mooring Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116viiiList of Tables2.1 CTD cast dates (2016-2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.2 GLM general model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.3 GLM mixing parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.4 BML morphometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.5 GLM simulation periods (2016-2017) . . . . . . . . . . . . . . . . . . . . . . . . . 262.6 GLM meteorology coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.1 Classification of wind events (2016-2017) . . . . . . . . . . . . . . . . . . . . . . . 784.2 RMSE of heat content and thermal stability (2016-2017) . . . . . . . . . . . . . . 88ixList of Figures1.1 Wind-driven internal seiche in a two-layer lake . . . . . . . . . . . . . . . . . . . 41.2 Classification of lake responses to wind forcing . . . . . . . . . . . . . . . . . . . 71.3 Examples and schematic of wind-driven upwelling . . . . . . . . . . . . . . . . . . 91.4 BML bathymetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.1 Timeline of data examined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2 BML hypsometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.3 GLM initial temperature and salinity profiles . . . . . . . . . . . . . . . . . . . . 272.4 BML inflows and outflows (2016-2017) . . . . . . . . . . . . . . . . . . . . . . . . 292.5 BML sediment temperature (2016-2017) . . . . . . . . . . . . . . . . . . . . . . . 303.1 2016 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.2 2017 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.3 2016 Meteorological and moored measurements data . . . . . . . . . . . . . . . . 433.4 2017 Meteorological and moored measurements data . . . . . . . . . . . . . . . . 443.5 2016 Temperature and salinity CTD profiles . . . . . . . . . . . . . . . . . . . . . 463.6 2017 Temperature and salinity CTD profiles . . . . . . . . . . . . . . . . . . . . . 473.7 Internal seiche period (2016-2017) . . . . . . . . . . . . . . . . . . . . . . . . . . 483.8 Summer 2017 air temperature, Wedderburn Number, and temperature contours . 493.9 Summer 2017 model and field Wedderburn Number, HC, and SSI . . . . . . . . . 523.10 Summer 2017 model and CTD profiles . . . . . . . . . . . . . . . . . . . . . . . . 543.11 Summer 2016 air temperature, Wedderburn Number, and temperature contours . 553.12 Summer 2016 model and field Wedderburn Number, HC, and SSI . . . . . . . . . 573.13 Summer 2016 model and CTD profiles . . . . . . . . . . . . . . . . . . . . . . . . 59xList of Figures3.14 Spring 2017 air temperature, Wedderburn Number, and temperature contours . . 613.15 Spring 2017 model and field Wedderburn Number, HC, and SSI . . . . . . . . . . 643.16 Spring 2017 model and CTD profiles . . . . . . . . . . . . . . . . . . . . . . . . . 663.17 Spring 2016 air temperature, Wedderburn Number, and temperature contours . . 683.18 Spring 2016 model and field Wedderburn Number, HC, and SSI . . . . . . . . . . 723.19 Spring 2016 model and CTD profiles . . . . . . . . . . . . . . . . . . . . . . . . . 744.1 Conceptual model of wind-driven thermal responses . . . . . . . . . . . . . . . . 794.2 Spring 2016 field temperature measurements during wind event 3 . . . . . . . . . 814.3 Spring 2017 field temperature measurements during wind event 3 . . . . . . . . . 844.4 RMSE between field and model temperatures (2016-2017) . . . . . . . . . . . . . 88B.1 Heat content at P2 and P3 (2016–2017) . . . . . . . . . . . . . . . . . . . . . . . 114B.2 Thermal stability at P2 and P3 (2016–2017) . . . . . . . . . . . . . . . . . . . . . 115C.1 Time series of mooring temperatures during spring of 2016 . . . . . . . . . . . . . 117C.2 Time series of mooring temperatures during spring of 2017 . . . . . . . . . . . . . 118C.3 Time series of mooring temperatures during summer of 2016 . . . . . . . . . . . . 119C.4 Time series of mooring temperatures during summer of 2017 . . . . . . . . . . . . 120xiAcknowledgmentsThis research would not have been possible without the many individuals who supported meover the course of my degree.I am deeply grateful to my supervisor, Gregory Lawrence, for his patience and exceptionalguidance. His encouragement and trust in my abilities motivated me to persevere throughchallenges in my research. I am equally indebted to my co-supervisor, Bernard Laval, whosewisdom and care extended far beyond academia, and who consistently pushed me to reachhigher standards than I thought possible. Their mentorship has left a lasting impression on meand has shaped me not only as a researcher, but also as a person.I am sincerely thankful to Ted Tedford and Roger Pieters for their generosity with theirtime, thoughtful insights, and steady encouragement of my academic growth. Their patienceand well-timed scientific puns made this journey both rewarding and memorable. I am especiallygrateful for the countless hours they devoted to supporting my work and for the enthusiasmwith which they celebrated my progress.I gratefully acknowledge the financial support provided by the Natural Sciences and Engi-neering Research Council of Canada (NSERC) and Syncrude Canada Ltd., without which thisresearch would not have been possible.To my parents, Mirmosadegh and Zahra, and to my sister, Ava, I owe everything. Yourunconditional love and unwavering support have given me the foundation to grow and pursuethis path, and the many sacrifices you have made are the reason I have come this far. I amforever blessed to be surrounded by your care and strength.To my close friends in Vancouver, I am truly thankful for standing by me throughout thisjourney. Your presence and encouragement not only made the hard days lighter and the gooddays even brighter, but also fostered much of my inner growth. I carry a home in each of yourhearts, and I am deeply thankful for the love and belief you showed me, even when I struggledxiiAcknowledgmentsto believe in myself.Finally, I thank the UBC EFM team for their support, humour, insights, and, most im-portantly, their teamwork, which made this experience all the more enjoyable. I will alwayscherish our extravagant coffee runs to Loafe, despite having a perfectly good espresso machinecollecting dust in the office!xiiiChapter 1IntroductionClimate change affects all ecosystems around the world by disrupting the surface heat balanceand increasing the frequency and intensity of extreme events. Lakes are particularly sensitiveto climatic variations, leading to their recognition as sentinels of climate change (Adrian et al.,2009). A large proportion of lakes in temperate latitudes are dimictic, undergoing verticalmixing (turnover) twice annually - in spring and fall. During summer, they warm above thetemperature of maximum density, imd ≈ 4 ◦C, and during winter, they cool below imd, oftenbecoming ice-covered. As a result, dimictic lakes experience temperature stratification in bothseasons, which inhibits the vertical transport of heat, nutrients, and dissolved oxygen (Wetzel,2023).In recent decades temperate lakes have experienced notable changes in their thermal regimes,including shorter ice-covered periods and extended durations of summer stratification (Stainsbyet al., 2011). These shifts have far-reaching ecological consequences, influencing oxygen avail-ability, nutrient cycling, and biological productivity (Wells and Troy, 2022; Flood et al., 2021).Understanding and predicting the dynamics of vertical mixing in the presence of thermal,and sometimes salinity, stratification is a key challenge in limnology, particularly during thetransitional seasons of spring and fall when mixing is most vigorous. In spring, mixing in lakes ismost commonly caused by wind. In fall, the cooling of surface waters enhances vertical mixing,and turnover typically continues until the lake cools to imd. However, during spring, warmingsurface waters stabilize the water column, reducing the impact of wind forcing. As a result,spring windstorms may trigger mixing events, but these are often followed by re-stratificationonce the winds subside.An added complexity in spring is the formation of a stable surface layer due to salt exclu-sion during ice growth. When the ice melts, this relatively fresh layer sits atop denser, more11.1. Wind Effects on Lakessaline water, further inhibiting vertical mixing and, in some cases, suppressing spring turnoveraltogether (Pieters and Lawrence, 2009).Rising temperatures and changing wind patterns due to climate change, along with increasedsalinity inputs from anthropogenic sources (e.g., urbanization, agriculture, and industrial dis-charges), all threaten to alter the occurrence and extent of spring turnover (Herbert et al., 2015;Denys, 2009; Ladwig et al., 2023). The ecological consequences include reduced oxygen replen-ishment, altered nutrient availability, and potential habitat degradation for aquatic organisms(Jane et al., 2021; Wells and Troy, 2022; Schindler, 2017).The present study was motivated by the desire to better understand the complexities ofmixing during spring and to advance our ability to predict and mitigate the impacts of risingtemperatures, wind intensity and salinity on water quality in lakes. As a first step, we beginby reviewing our current knowledge of the effects of wind on lakes.1.1 Wind Effects on LakesLakes are dynamic natural systems in which physical processes such as thermal stratification,vertical mixing, and internal wave motion interact to shape their ecological conditions. Inmost lakes, the formation and oscillation of the thermocline (metalimnion), a zone of rapidtemperature change separating warmer surface waters (epilimnion) from colder deep waters(hypolimnion), plays a crucial role. Strong wind events can disrupt this stratification by tiltingthe thermocline and causing upwelling, a process where deeper, nutrient-rich and often oxygen-depleted waters rise towards the surface. The earliest formal descriptions of such internalmotions and their associated mechanisms were pioneered in the early 20th century, laying thefoundation for modern physical limnology.The earliest interpretation of thermocline oscillations in stratified lakes was offered by Wat-son, 1904, who described them as standing internal waves at the density interface between twofluid layers in Loch Ness. This idea was further developed by Wedderburn and Williams, 1912,who conducted more detailed observations and formalized the phenomenon under the term”temperature seiche”, integrating field observations with theoretical analysis.The first detailed field observations confirming wind-induced internal seiches in lakes were21.1. Wind Effects on Lakesmade by Mortimer, 1952, who aimed to understand how internal waves influence lake biologyand sedimentation. Using high-resolution temperature measurements across Lake Windermereduring summer stratification, Mortimer observed both hypolimnetic upwelling and thermoclineoscillations in response to wind forcing. He validated Wedderburn’s earlier theory, confirmingthat wind-induced surface stress causes tilting of isotherms, followed by the development ofinternal seiches once the wind subsides (Figure 1.1).31.1. Wind Effects on LakesFigure 1.1: Movement caused by (i) wind stress and (ii) a subsequent internal seiche in ahypothetical two-layered lake, neglecting friction. Direction and velocity of flow are roughlyindicated by arrows. Reproduced from Mortimer, 1952.Extending this work, Mortimer, 1961 demonstrated that while internal seiche motion doesnot significantly alter the average temperature structure, it does displace the thermocline todeeper levels. He also identified return currents and localized upwelling at the upwind end,attributed to increased shear instability in the thermocline, and noted that this process steepensthe density gradient at the downwind end of the lake.41.1. Wind Effects on LakesWhile the early 20th-century studies by Watson and Wedderburn provided foundationalinsights into thermocline motion and internal seiche behavior in lakes, a parallel line of researchin atmospheric science introduced important theoretical tools for understanding stratified shearflows. One such contribution was made by Richardson, 1925, who investigated the stability ofturbulence in stratified shear flows. His work led to the development of the gradient Richardsonnumber (gi), a dimensionless parameter that quantifies the balance between the stabilizingeffect of stratification and the destabilizing effect of shear. Although originally derived in ameteorological context, the Richardson number has since become widely used in limnologyto characterize the potential for mixing and internal instability within stratified lakes. Thegradient Richardson number is defined as:gi = −g/U/RUz(UuRUz)2(1.1)where g is the gravitational acceleration, / is the reference density of the fluid, U/RUz is thevertical gradient of density, and UuRUz is the vertical gradient of the horizontal component offluid velocity, u. A small Richardson number (gi < 1R4) indicates that shear is strong rela-tive to stratification, promoting instability and mixing (Goldstein and Taylor, 1931), whereaslarger values indicate that the stabilizing effect of density stratification dominates, suppressingturbulence and vertical mixing (Richardson, 1925).While the gradient Richardson number is useful for identifying local shear instabilities atfine vertical scales, it requires high-resolution velocity and density profiles, which are not alwaysavailable in lake studies. To address this, a bulk Richardson number (giS) is often used as alarge-scale, integrated alternative.giS =g′hu2∗(1.2)where u∗ is the wind-induced shear velocity, g′ is the reduced gravity representing stratification,and h is the epilimnion thickness.The reduced gravity g′ quantifies the buoyant force arising from the density difference be-tween the upper and lower layers of the lake, and is defined as:51.1. Wind Effects on Lakesg′ =/2 − /1/2g (1.3)where /1 and /2 denote the densities of the epilimnion and hypolimnion respectively.The shear velocity u∗ represents the wind’s ability to generate momentum transfer at thelake surface and is given by:u∗ =√XD/R/1UR ≈ 1O3× 10−3UR (1.4)where XD is the wind drag coefficient (≈ 1.3 ×10−3), /R is the air density (1O225 kgm−3 at15 ◦C), and UR is the wind speed measured at 10m above water surface.While the bulk Richardson number provides a vertically integrated estimate of mixing po-tential, it does not account for the horizontal length scale of the basin, which plays a criticalrole in determining how a lake responds to wind forcing. To incorporate both vertical andhorizontal scales, the Wedderburn number (W ) was introduced by Thompson and Imberger,1980:W =hagiS (1.5)where a is the length of the lake. Simply put, the Wedderburn number is the seiche or ther-mocline set-up amplitude for a two-layer lake system non-dimensionalized by the mixed-layerdepth. This forms the basis of a classification scheme for the response of two-layer lakes towind stress.Building on these foundational studies, Spigel and Imberger, 1980 developed a theoreticalframework to classify the response of a two-layer stratified lake with a total depth of H towind forcing, neglecting Coriolis effects and surface heating (Figure 1.2). They defined fourdistinct dynamic regimes based on the interplay among wind stress, stratification strength, andlake geometry. Spigel and Imberger, 1980 derived this classification scheme based on the bulkRichardson number, the lake’s aspect ratio hL , and the quantity√YY−h . Here, a simplifiedscheme is presented using the Wedderburn number and assuming√YY−h = d(1).61.1. Wind Effects on LakesFigure 1.2: Overall features of the four regimes for a two-layer basin. Adapted from Spigel andImberger, 1980Regime 1In this regime, the lake basin quickly becomes vertically homogeneous under wind forcing.Although baroclinic shear initially exists, vertical mixing occurs so rapidly that instabilities(billows) do not have time to develop, eliminating significant longitudinal density gradients.As a result, the water column becomes nearly uniform horizontally and vertically. This regimeoccurs when:W < d(ha) (1.6)Regime 2In this regime, wind stress is strong enough to lift the thermocline or density interface closeto the surface at the upwind end, causing longitudinal (horizontal) stratification. The interfacebecomes unstable, losing its sharpness and leading to two-dimensional effects. This instabil-ity can result in upwelling of deeper, hypolimnetic waters toward the surface. Consequently,complete vertical mixing may occur during a single wind event. This regime occurs when:71.1. Wind Effects on Lakesd(ha) < W < d(1) (1.7)Regime 3In this regime, buoyancy forces become stronger, limiting vertical mixing processes such asentrainment and billow formation. However, wind stress remains sufficient to induce large-scaleinternal wave motions (internal seiches) and sizable vertical displacement of the thermocline.Unlike regime 2, the density interface remains sharp, two-dimensional effects are minimal, andcomplete vertical mixing rarely occurs within a single wind event. Upwelling in this regimetypically involves displacement of intermediate-depth waters (metalimnetic upwelling) ratherthan deeper hypolimnetic waters, as the thermocline maintains its integrity. This regime occurswhen:d(1) < W < d(ah) (1.8)Regime 4In this regime, buoyancy forces overwhelmingly dominate lake dynamics. Internal wavesthat form under wind forcing have relatively small amplitudes, with minimal influence fromentrainment at the interface. Turbulent instabilities (billows) are negligible, and the thermoclineremains sharp and stable. Vertical mixing is highly restricted, and complete mixing does notoccur during individual wind events. This regime is characterized by:W S d(ah) (1.9)Collectively, these four regimes illustrate how lake geometry, stratification strength, andwind forcing jointly control internal mixing behavior and the vertical structure of lakes. Thisconceptual framework introduced by Spigel and Imberger, 1980 has provided a foundationalunderstanding of lake dynamics and has been incorporated into the widely used numericalmodels of lake hydrodynamics, e.g. the General Lake Model (GLM) (Hipsey et al., 2019).It is important to note that the theoretical framework developed by Spigel and Imberger,81.1. Wind Effects on Lakes1980 assumes a simplified two-layer stratification. However, many lakes exhibit a more complexstructure that includes an intermediate layer of finite thickness, known as the metalimnion,positioned between the epilimnion and hypolimnion. This layer has long been recognized asan important factor in lake dynamics (see Mortimer, 1961). When wind blows across thelake surface, the isopycnals (surfaces of constant density) often remain nearly horizontal atthe base of the metalimnion but begin to slope upward toward the surface near the top ofthe metalimnion, especially at the upwind end of the lake, where they may surface (see Figure1.3(a)). This phenomenon, known as metalimnetic upwelling, is a defining feature when d(1) <W < d(Lh ). Metalimnetic upwelling has been observed in laboratory experiments (Monismith,1986) and is evident in the field data presented by Mortimer, 1954 and Robb et al., 2025,see Figure 1.3. To better account for this behaviour, Monismith, 1986 proposed an enhancedmixed-layer deepening model that explicitly incorporates the effects of metalimnetic upwelling.Figure 1.3: Wind-driven upwelling. (a) Schematic adapted from Monismith, 1986. Plot of fielddata from (b) Windermere Lake’s northern basin on Oct 26th, 1949, reproduced from Mortimer,1954 and (c) Carpenter Reservoir, Canada on Jun 18th, 2015 when W = 1O1, reproduced fromRobb et al., 2025.The above observational and theoretical studies of thermocline motion and wind-inducedinternal waves laid the groundwork for our present understanding of lake hydrodynamics. By91.1. Wind Effects on Lakesintroducing fundamental concepts such as internal seiches, shear-driven mixing, and stabilityparameters like the Richardson and Wedderburn numbers, these foundational works establisheda framework that remains central to limnology today. As the climate crisis intensifies, therelevance of these early insights becomes even more apparent. Modern day efforts to understandand predict how lakes will respond to climate change, particularly in the face of shifting windregimes, altered stratification patterns, and prolonged stratified periods, continue to rely on theprinciples established by these pioneering studies.These dynamics are particularly critical during spring, when mixing events have a greaterimpact on nutrient and oxygen redistribution than in fall (Ghane and Boegman, 2021). Up-welling, in particular, connects deep, cooler waters with surface layers, allowing oxygen to reachtypically depleted regions. While this can benefit lake ecosystems by oxygenating bottom wa-ters and enhancing habitat quality, it can also transport hypoxic water into nearshore zones,triggering sudden declines in oxygen levels that may cause fish mortality (Wells and Troy, 2022)and alter the spatial distribution of planktonic organisms (Cyr, 2017). A notable example ofthese effects is observed in Hamilton Harbour, where upwelling of hypoxic waters has disruptedfish populations and degraded habitat quality (Flood et al., 2021).Despite the clarity provided by Spigel and Imberger, 1980 and Monismith, 1986 regardingthe two-dimensional process of wind-driven mixing, vertically one-dimensional (1-D) hydro-dynamic models are commonly used to simulate lake thermal structure over multi-decadaltimescales, often in the context of climate change projections. While 1-D models are gener-ally appropriate when the Wedderburn number is either much greater than or much less thanunity (i.e., W SS 1 or W << 1), transitional periods such as spring and fall are charac-terized by intermediate W values on the order of unity (W ∼ d(1)), where two-dimensionalprocesses dominate and the assumptions underlying 1-D models break down. Nevertheless,several long-term modelling studies continue to span these transitional periods. Examples in-clude simulations of thermal structure in Lake Geneva (Perroud et al., 2009), Lake Ammersee(Weinberger and Vetter, 2012), Lake Sevan (Shikhani et al., 2024), Lake Erken (Ayala et al.,2020), and simulation of the 37-year record of anoxia dynamics in Lake Mendota (Ladwig et al.,2021). While these studies provide valuable insights into long-term lake behaviour, they alsohighlight an important gap in current modelling practices. The reliance on 1-D assumptions101.2. Base Mine Lake Demonstrationduring prolonged simulation periods, particularly across dynamically complex periods, violatesfundamental model assumptions and may limit predictive accuracy.1.2 Base Mine Lake DemonstrationAlberta’s oil sands industry is the third largest oil reserve in the world, holding approximately170 billion barrels of proven oil reserves, 168 billion of which is in the form of bitumen and therest is conventional crude oil (Kabwe et al., 2019). Currently, the oil sands industry produce1.9 million barrels of oil each day which is 55% of all the oil produced in Canada. Only 3% ofthe oil sands area can be accessed by surface mining, but this small area contains 20% of thetotal oil (Kabwe et al., 2019).According to the Cumulative Environmental Management Association (CEMA, 2012), anend pit lake (EPL) is a man-made waterbody constructed below the natural ground level withina former oil sands mining pit. These lakes may contain residual oil sands by-products and aredesigned to receive both surface runoff and groundwater from nearby reclaimed and undisturbedareas. EPLs are intended to serve as permanent features in the final reclaimed landscape,eventually releasing water into downstream environments (Kabwe et al., 2019).Alberta’s Environmental Protection and Enhancement Act (2000) requires that disturbedlands be reclaimed to a state of equivalent land capability. EPLs have been proposed as a recla-mation strategy that allows for the permanent containment of fine tailings, including residualhydrocarbons, under a freshwater cap (Kabwe et al., 2019, Dompierre, 2017).Base Mine Lake Demonstration (BML), situated at 57°1'N, 111°37'W north of Fort McMur-ray, Alberta, Canada, commissioned on Dec 31st, 2012, is the pioneering full-scale demonstra-tion of an EPL in the oil sands industry (Figure 1.4). BML was created by Syncrude CanadaLtd. through the strategic backfilling of fluid fine tailings (FFT) into a previously mined-outpit, followed by capping with a water layer.The initial depth of the water cap was designed to exceed 5m to prevent resuspension of theFFT by fluid motions driven by wind generated surface waves (Lawrence et al., 1991, 2016).Since the commissioning of BML, the FFT have continued to settle, and as of 2017, the averagedepth of the water cap had increased to approximately 9m, see Figure 1.4.111.3. Study Motivations and ObjectivesIn the present study, we examine an extensive set of field data collected during the springand early summer of 2016 and 2017 at BML.Figure 1.4: Bathymetry of Base Mine Lake Demonstration in 2017 with 2 m depth contoursranging from 1 m to 11 m. Locations of pumped inflow and outflow are marked by red andblue arrows, respectively. Data courtesy of Syncrude Canada Ltd.1.3 Study Motivations and ObjectivesBML represents a significant potential reclamation strategy for the oil sands industry. Itssuccess, or failure, carries far-reaching economic, social, and environmental implications. Asa result, BML has been monitored since its commissioning to assess the evolution of its phys-ical, chemical, and biological limnology. Beyond its applied relevance, BML also presents acompelling case study of scientific interest.Due to its origin from mining operations, the lake’s water cap is brackish, with a typical121.4. Thesis Outlinesalinity of approximately 2 ppt, and it remains ice-covered from roughly November to May.Recent studies have shown that in brackish lakes subject to ice cover, spring turnover can beinhibited or even entirely suppressed (Pieters and Lawrence, 2009; Ladwig et al., 2023). Asthe FFT consolidates, it releases porewater with a salinity of approximately 4 ppt, raising thelong-term possibility of meromixis.The potential suppression of spring turnover, and the possibility of meromixis, raise seriousconcerns about water quality and the long-term ecological viability of EPLs, particularly asthey are intended to be permanent features in the reclaimed landscape. Understanding thecirculation and mixing of BML is critical, as it directly affects lake ventilation, oxygen transport,and overall water quality.The objective of this study is to investigate the inhibition and dynamics of spring turnoverand the progression of summer stratification in BML through a combination of field observationsand 1-D hydrodynamic modelling. The years 2016 and 2017 were selected due to their relativelycomplete data records, making them suitable for calibrating the General Lake Model (GLM).The calibrated model was then used to analyze the thermal dynamics of BML and identify thekey drivers influencing stratification and mixing during the open-water season.1.4 Thesis OutlineChapter 1 provides an overview of the physical processes governing lake dynamics, with a focuson thermal stratification, wind-driven mixing, and upwelling. It traces the historical develop-ment of our understanding of these processes and highlights the limitations of 1-D modellingapproaches during dynamically complex periods. Additionally, it outlines the background andmotivations for this study, emphasizing the importance of spring turnover and its influence onwater quality.Chapter 2 provides a description of the study site and outlines the preliminary data pro-cessing procedures for measurements obtained from moored instrumentation and CTD surveys.It also introduces the 1-D hydrodynamic model used in this study, GLM, and details the modelsetup and calibration process. Additionally, key parameters used to evaluate model performancesuch as lake heat content and Schmidt stability index are presented.131.4. Thesis OutlineChapter 3 presents both field observations and model results for the spring and summer of2016 and 2017. Notable wind events during each period using field observations are identifiedand compared with the model results.Chapter 4 introduces a simple conceptual model and applies it to analyze spring turnoverdynamics in 2016 and 2017. Additionally, the accuracy of the model in simulating the dynamicsof BML are summarized and discussed.Chapter 5 presents the conclusions drawn from this study, and explores potential areas forfuture research.14Chapter 2MethodologyIn this chapter, we begin by presenting background information on the characteristics of thefield site, BML, and the data collection methods. We then provide a summary of the model usedto simulate the evolution of BML thermal stratification. This summary includes the governingequations used in GLM, followed by a description of the model calibration parameters, theprocessing of field data for comparison with model results, and the metrics used to evaluate themodel’s performance.2.1 Site DescriptionBML is a dimictic lake with a surface area of approximately 7O8 km2 (Zhao, 2023). It is charac-terized as an average 10m brackish water cap with a salinity of approximately 2 ppt, overlyingan approximately 45m mud-like deposit of FFT with a pore water salinity of approximately4 ppt. The gradual consolidation of the FFT results in the upward expression of saline porewa-ter into the overlying water column and contributes to the gradual deepening of the lake basin(Dompierre and Barbour, 2016).The average temperature of the FFT in BML is approximately 12O9 ◦C, significantly higherthan both the regional average annual air temperature (1 ◦C) and the average shallow ground-water temperature (3 ◦C to 4 ◦C) (Dompierre and Barbour, 2016). Furthermore, Dompierre,2017 determined that the upper 1m of the FFT is influenced by seasonal variations in laketemperature, with FFT temperatures fluctuating between 5 ◦C to 15 ◦C over the course of theyear.BML experiences methane (CH4) ebullition from the FFT to the overlying water cap, whichtransports benthic material and hydrocarbons to the surface. This process significantly impactslake water quality by increasing turbidity and is hypothesized to enhance vertical mixing within152.1. Site Descriptionthe water column. Additionally, methane ebullition is a notable contributor to global green-house gas emissions (Ing, 2024). Turbidity in BML varies seasonally, with wind-driven mixingredistributing suspended particles throughout the water column. Tedford et al., 2019 reportedthat turbidity levels are lowest during the summer, as particulate matter settles steadily fromthe epilimnion through the thermocline to the bottom of the lake. In contrast, convective cool-ing and wind events in the fall lead to a sharp increase in turbidity, peaking just before ice-on.This seasonal increase in turbidity reduces light penetration and disrupts the lake’s thermalstratification.In BML, there are several mechanisms governing the vertical mixing of suspended solidsincluding convection due to saline water exclusion during ice formation, penetrative convectiondue to surface cooling, ebullition from FFT, and internal wave activity (Lawrence et al., 2016).To reduce turbidity, alum was added to BML from September 1st to 28th, 2016, resulting ina distinct turbidity decline compared to previous years. Prior to this, the turbidity of thelake increased steadily throughout the fall turnover period, reaching up to 300NTU in 2015.However, in 2016 during the alum dosing period, turbidity which had ranged between 15NTUto 20NTU in late August, began to decline and reached as low as 5NTU by September 12th -just 12 days after treatment began (Tedford et al., 2016).The surface of BML is often covered by a hydrocarbon sheen originating from the under-lying tailings deposit. Hurley, 2017 demonstrated that this sheen suppresses high-frequencywind waves, leading to a slower-developing wave field dominated by longer wavelengths. Thishydrocarbon sheen also affects the heat exchange between the lake and its surroundings, as itreduces the transfer coefficients of momentum, heat, and moisture across the air–water interface(Chang, 2020).BML operates as a flow-through system, receiving inflow from the nearby Beaver CreekReservoir, a freshwater source situated on the southwest side of the lake (Figure 1.4). Sincethe lake’s commissioning in 2012, freshwater has been continuously pumped from the BeaverCreek Reservoir during ice-free periods to raise BML’s surface elevation to 308.7 ± 0O5m abovesea level. Currently, water from BML is pumped into the mine’s recycled water system fromthe northeast side of the lake for use in bitumen extraction, and the lake’s surface elevation hasremained relatively constant (Dompierre, 2017). However, the long-term plan is to discharge162.2. Field Datathis water into the Athabasca River once it meets the required water quality standards (Syn-crude, 2021). A summary of the pump-in and pump-out flows during 2016 and 2017 in BMLhas been provided in Figure 2.4.2.2 Field DataThree platforms serve as the primary locations for collecting meteorological and water qualitydata at BML: Platform 1 (P1) situated in the center of the lake; Platform 2 (P2) located atthe northeast end; and Platform 3 (P3) positioned at the southwest end, see Figure 1.4. Inthis study, meteorological and dissolved oxygen measurements were recorded at P1. Mooringdata (continuous temperature) and CTD (Conductivity, Temperature, and Depth) profiles werecollected at P2 and P3. These measurements were used to initialize and force the 1-D model,GLM.There are two weather stations at P1. One of these stations is managed by the McMasterUniversity Watershed Hydrology Research Group and measured the following variables: windspeed, wind direction, incoming and outgoing shortwave radiation, incoming and outgoing long-wave radiation. Details regarding the instrumentation and data collection methodology can befound in Clark et al., 2021. The second weather station is managed by Hatfield ConsultantsLtd. (Fort McMurray, Alberta, CA) and recorded the following variables: Wind speed, winddirection, incoming and outgoing shortwave radiation, incoming and outgoing longwave radi-ation, relative humidity, and air temperature. In this study, Hatfield Consultants Ltd. datawere primarily used, with gaps in the dataset filled using corresponding data from Clark et al.,2021.Wind speed was recorded at height 3O3m above the water surface and subsequently adjustedby the following equation to represent wind speed at 10m above the water (Manwell et al., 2009).U10 = U(10z)1/7 (2.1)Where U10 is the wind speed at height 10m above the water surface water and U is the windspeed measured at height z above the water surface.The CTD was a Seabird SBE19plus with a pumped conductivity-temperature cell. The172.2. Field Datainstrument sampled every 0O25 s and was lowered at approximately 40 cm s−1. The mooringconsisted of 11 to 12 RBR SoloTs sampled every 10 s and were spaced at vertical spacing of1m.Water is pumped out from the northeast of the lake. Water is pumped into the southwest ofthe lake from Beaver Creek reservoir. The flow rates were provided by Syncrude Canada Ltd.A summary of the timeline for when each moored and CTD measurement was made at P2and P3, along with periods of data gaps for 2016 and 2017, is provided in Figure 2.1. As shownin this figure, in 2016, ice-off occurred on April 27th corresponding to Day of Year (DOY) 118,with fall turnover on August 27th (DOY 240). In 2017, ice-off occurred on May 5th (DOY 125),with fall turnover on September 3rd (DOY 246). Each year, after ice-off, three CTD surveyswere performed at BML. Table 2.1 provides a summary of the time and date at which eachCTD survey was carried out during 2016 and 2017.Figure 2.1: Timeline of data examined for 2016 and 2017. ( ) and ( ) show P2 and P3 mooringdata, respectively. CTD cast dates at P2 and P3 are marked by (T) and (T). The x-axis showsday of year, with corresponding dates above. Data gaps indicate periods of instrument service.182.3. General Lake Model (GLM)Table 2.1: CTD cast dates for platforms P2 and P3 in 2016 and 2017Platform 2016 2017CTD 1 P2 DOY 124 2016-05-03 14:17 DOY 131 2017-05-11 13:36P3 2016-05-03 13:51 2017-05-11 10:24CTD 2 P2 DOY 187 2016-07-05 11:37 DOY 171 2017-06-20 11:43P3 2016-07-05 12:05 2017-06-20 12:13CTD 3 P2 DOY 224 2016-08-11 10:40 DOY 223 2017-08-11 09:10P3 2016-08-11 09:19 2017-08-11 10:002.3 General Lake Model (GLM)Understanding the effects of wind stress on lakes, requires careful modelling and physical vali-dation of the results. In BML, competing physical processes - such as the presence of a surfacehydrocarbon sheen (Chang, 2020; Hurley, 2017), the release of brackish pore water as thetailings settle, which results in the deepening of the water column (Dompierre, 2017), andthe entrainment of turbidity into the water column due to methane ebullition from the tail-ings (Zhao et al., 2022a; Zhao et al., 2021, 2022b) - make this lake unique and challenging tounderstand and model.In this preliminary effort to model the complexities of BML, we chose the General LakeModel (GLM). GLM is a 1-D lake model designed to simulate the seasonal evolution of densitystratification of lakes, reservoirs, and wetlands and can be coupled with biogeochemical andecological modelling libraries for integrated simulations of water quality and ecosystem health.GLM incorporates a flexible Lagrangian layer structure in which the vertical distribution oftemperature, salinity, and density in a lake is calculated by integrating a series of vertical layersthat capture the variation of water column properties.The ability of GLM to account for the effects of inflows and outflows on the water balance,surface heating and cooling, and vertical mixing, while computing the vertical profiles of tem-perature, salinity, and density, makes it a suitable model for simulating BML. To achieve this,we present a range of parameters for assessing the lake’s thermal stability, along with addi-tional parameters to evaluate the model’s accuracy in estimating the lake’s thermal conditionsat specific times.Initially featured in the DYnamic REservoir Simulation Model (DYRESM), developed by192.3. General Lake Model (GLM)the Centre for Water Research (Imberger and Patterson, 1981, Hamilton and Schladow, 1997),the Lagrangian technique enables the dynamic adjustment of layer thicknesses in response toinflows, outflows, mixing, and surface mass fluxes. This allows layers to expand or contract asconditions change. When sufficient energy becomes available to overcome stable density gra-dients, adjacent layers merge, representing vertical mixing processes. Originally, DYRESM’smixed-layer subroutine incorporated only a stirring mechanism driven by both wind and con-vection; however, following the theoretical framework developed by Spigel and Imberger, 1980for classifying a lake’s response to wind forcing (see Section 1.1), the subroutine was modi-fied to include the contribution of shear production to mixed-layer deepening. In GLM, layerthicknesses are similarly adjusted to accurately resolve vertical density gradients. Unlike fixed-grid systems, which rely on mixing algorithms based on vertical diffusivity, The Lagrangianapproach of GLM significantly reduces numerical diffusion across the thermocline, making itparticularly well-suited for long-term lake investigations.The following summary of the GLM equations is based on the detailed description of themodel presented in (Hipsey et al., 2019).2.3.1 Energy Balance and MixingThe energy balance approach of GLM to lake mixing dynamics involves estimating the amountof turbulent energy available for both the surface mixed layer (surface mixing) and mixingbelow the thermocline (deep mixing).For surface mixing, GLM calculates the available energy, EeKE , and the energy requiredfor mixing, EPE , with the balance between these two resulting in the deepening of the surfacemixed layer (SML) with the rate of dzsmldt , where zsml is the distance from the surface tothe bottom of surface mixed layer. In this approach, the calculated kinetic energy includescontributions from wind stirring, convective overturn, shear production between layers, andKelvin–Helmholtz (K–H) billowing. Defining k as the index of the layer at the bottom of SML(epilimnion), these factors can be combined and summarized as the following:EeKE = 0O5XKw3∗∆t+ 0O5XKXhu3∗∆t+ 0O5Xd[u2s +u2s6dξdzsml+usξ3dusdzsml]∆zk−1 (2.2)202.3. General Lake Model (GLM)In this equation, for a time step of ∆t and a layer below the epilimnion with a thickness of∆zk−1; ξ is the K-H billow length scale, us is the shear velocity at the base of the mixed layer,u∗ is the wind shear velocity, and w∗ is the turbulent velocity scale within the surface mixedlayer due to convective cooling. Moreover, XK , Xh , and Xd are the convective overturn, windmixing, and shear production mixing coefficients, respectively.The potential energy required for mixing encompasses the energy needed to lift water fromthe layer below the bottom of the mixed layer, accelerate it to the mixed layer’s velocity (us), andenergy consumption associated with K–H billowing. This can be summarized in the followingequation:EPE =[0O5Xe (w3∗ + Xhu3∗)2/3 +∆//0gzsml +gξ224/0d(∆/)dzsml+gξ∆/12/0dξdzsml]∆zk−1 (2.3)where Xe is the unsteady turbulence mixing efficiency, /o is a reference water density, and ∆/is the density difference between the layers.Deep mixing is modelled by incorporating both vertical molecular, Dm, and turbulent dif-fusivity, Dϵ, represented by Dz = Dϵ +Dm. In GLM, there are three possible approaches forhypolimnetic mixing: (1) no diffusivity Dz = 0, (2) a constant vertical diffusivity Dz through-out the depth below the SML, or (3) a method from Weinstock, 1981, used in DYRESM andapplied in this study, which adjusts the diffusivity according to the levels of dissipation andstratification, increasing with dissipation and decreasing with stronger stratification. This ver-tical diffusivity (Dz) is given by:Dz =ϵeKEXYY PN2 + 0O6k2eKEu2∗(2.4)where XYY P is the mixing efficiency of hypolimnetic turbulent kinetic energy (TKE), keKE isthe turbulence wavenumber, ϵeKE is the TKE dissipation rate, and N2 is the Brunt–Väisäläfrequency.GLM begins by calculating the diffusivity through a simplified approximation of the involvedparameters. Once the diffusivity is determined, the diffusion of scalars, such as temperature,212.3. General Lake Model (GLM)salinity, and other water quality attributes, between two layers is numerically estimated usinga mass transfer expression.In the following sections, we will discuss how each of the parameters covered so far wasadjusted during our calibration process and the implications of these changes.2.3.2 Solar Heating and Light PenetrationSince solar radiation is the key driver of lake thermodynamics, in the case where solar radiationdata is unavailable, GLM provides the option of computing the solar heating through an internaltheoretical approximation based upon the Bird Clear Sky Model (BCSM) (Bird, 1984). Ashortwave albedo is applied to the approximated or given solar radiation. GLM provides threeoptions for calculating the shortwave albedo, αdh : (1) a daily approximation by Hamilton andSchladow, 1997, (2) a sub-daily approximation from Briegleb et al., 1986, and (3) a sub-dailyapproximation from Yajima and Yamamoto, 2015. The three mentioned albedo modes havebeen summarized as follows:Albedo mode 1: (Hamilton and Schladow, 1997)adh =0O08 + 0O02 sin(2π3+5d− π2): northern hemisphere0O08 : equator0O08− 0O02 sin ( 2π3+5d− π2 ) : southern hemisphereAlbedo mode 2: (Briegleb et al., 1986)adh =1100(2O6(1O1 cos(Φzen))1K7 + 0O065+ 15 [cos(Φzen)− 0O1] [cos(Φzen)− 0O5] [cos(Φzen)− 1])Albedo mode 3: (Yajima and Yamamoto, 2015)adh = 0O001RH [cos(Φzen)]0K33 − 0O001U10 [cos(Φzen)]−0K57 − 0O001 ζ [cos(Φzen)]0K829where d is the day of year, Φzen is the solar zenith angle in radians, gH is the relative humidity,ζ is the percentage of atmospheric diffuse radiation which is approximately 6%, and U10 is the222.3. General Lake Model (GLM)wind speed corrected at 10m.After calculating the shortwave albedo from either one of the mentioned options, GLMwill adjust the incoming shortwave radiation flux (ϕˆdh ) supplied by the user to calculate thepenetrative solar radiation flux entering the lake (ϕdh ). This is done according to Beer-LambertLaw:ϕdh (z) = (1− αdh )fdh fPRRϕˆdh exp[−Kh z] (10)Here, z is the depth from the surface layer, fdh is the solar radiation scaling factor, fPRR is thephotosynthetically active fraction (PAR) of the global incoming radiation flux, which is 45% ofthe incident light, and Kh is the light extinction coefficient (m−1) . By using this method, themodel applies Beer’s Law to the penetrative PAR fraction of the incident light. The remainingnon-penetrative 1 − fPRR fraction accounts for the higher attenuation of near infra-red andultraviolet bandwidths of the incident shortwave radiation.2.3.3 Sediment HeatingGLM also accounts for the effects of sediment heating and cooling on the water column’s thermalbudget. To do this, the model incorporates Fourier’s law of heat conduction to a user-definednumber of sediment zones that extend across the lake’s depth. For each water layer, the rateof temperature change is determined by the temperature gradient and the relative area of thelayer’s volume in contact with the bottom sediment and the soil-sediment thermal conductivity,Ksozl.Furthermore, GLM considers seasonal variations in the temperature of the bottom sediment,which also depend on its depth below the water surface. For a given soil-sediment zone, thetemperature of this sediment zone, iz, varies as such:iz = izmean + δiz cos[2.365(d− dez)](2.5)where z is the soil-sediment zone, izmean is the annual mean sediment temperature, δiz is theseasonal amplitude of the sediment temperature variation, and finally, d and dez are the day ofyear and the day of year when sediment temperature peaks respectively.232.4. Model Setup2.4 Model SetupGLM can be configured through the main ’namelist’ text file (nml), called glm3.nml, see Ap-pendix A for an example from this study. This file defines all the key model setup configurations,including ’GLM setup’, ’mixing’, ’water quality setup’, ’morphometry’, ’time’, ’outputs’, ’initialcondition profiles’, ’meteorology’, ’bird model’, ’inflows’, ’outflows’, ’sediment’, and ’snow andice’. The following section provides details on some of these components and the values used inthe simulations performed on BML.GLM SetupThrough the density model, GLM provides the user with the ability to select which densityequation to use for calculating the layer densities. The available options include TEOS-10(density_model = 1), UNESCO 1981 (density_model = 2), and a user-defined density equation(density_model = 3+) which was not explored in this study. In this study density_model = 2was used. A summary of the general model set up is provided in Table 2.2.Table 2.2: General model parameters and assigned values for spring and summer of 2016 and2017Variable Name Description Valuemax_layers Maximum number of layers 100min_layer_vol Minimum layer volume (m3) 0.025min_layer_thick Minimum thickness of a layer (m) 0.15max_layer_thick Maximum thickness of a layer (m) 1.50density_model Switch to set the density equation 2Mixing CoefficientsIn Section 2.3.1, the equations governing the mixing coefficients were presented. Althoughthe calibration of these coefficients was briefly addressed, it was deemed beyond the scope ofthis study. Consequently, the values listed in Table 2.3 reflect the default recommendationsprovided in the model manual (Hipsey et al., 2014) and were adopted throughout this work.Similar to the density_model, GLM offers multiple options for both surface and deep mix-ing configurations. For surface mixing, the model allows for either no surface mixing (sur-face_mixing = 0) or active surface mixing (surface_mixing = 1) the later of which was used242.4. Model Setupin this study. The deep mixing configuration options were outlined in Section 2.3.1 of whichoption 2 was used here.Table 2.3: Mixing parameters and their assigned values for spring and summer of 2016 and2017Variable Name Symbol Description Valuesurface_mixing - Surface mixing mode 1coef_mix_conv XK Mixing efficiency - convective overturn 0.2coef_wind_stir Xh Mixing efficiency - wind stirring 0.23coef_mix_shear Xd Mixing efficiency - shear production 0.3coef_mix_turb Xe Mixing efficiency - unsteady turbulence effects 0.51coef_mix_KH XKY Mixing efficiency - Kelvin-Helmholtz billowing 0.3deep_mixing - Deep mixing mode 2coef_mix_hyp XYY P Mixing efficiency - hypolimnetic turbulence 0.5Water Quality SetupThis section covers the selection of a water quality model to couple with GLM (FABM orAED2) and the method for solving the ODEs in the water quality module. This feature wasnot utilized in this study.MorphometryIn this section lake latitude, longitude, basin length and width (Table 2.4) and hypsometry(Figure 2.2) are specified.Figure 2.2: Hyposmetry of BML used in GLM and the water elevation for 2016 ( ) and 2017( )252.4. Model SetupTable 2.4: BML morphometry used to initiate GLMVariable Name Description Valuelatitude - 57longitude - 111.6bsn_len Basin length at crest (m) 3500bsn_wid Basin width at crest (m) 2200TimeThis section allows for the definition of simulation start and stop times, time steps, andtime zones. For this study, the time step for all simulations was set to one hour. The followingtable presents the start and stop times for all four scenarios run for BML.Table 2.5: GLM simulation periodsSeason Year Start Date End DateSpring 2016 2016-05-03 15:00:00 (DOY 124.625) 2016-07-06 00:00:00 (DOY 188.0)2017 2017-05-11 12:00:00 (DOY 131.5) 2017-06-21 00:00:00 (DOY 172.0)Summer 2016 2016-07-05 12:00:00 (DOY 187.5) 2016-08-12 00:00:00 (DOY 225.0)2017 2017-06-20 12:00:00 (DOY 171.5) 2017-08-12 00:00:00 (DOY 224.0)The simulation start and end dates presented in Table 2.5, align with the dates when theCTD casts were conducted in both years (Table 2.1), providing the initial temperature andsalinity profiles used to initiate the model. For the spring simulations, CTD survey 1 wasused to initialize GLM, and the model results were then evaluated using the profiles fromCTD survey 2. For the summer simulations, the model was initialized with CTD survey 2 andevaluated using the profiles from CTD survey 3. A comparison of the model simulations withfield observations is provided in Sections 3.2 and 3.3.OutputsGLM produces two output files: one with detailed, depth-specific model results in NetCDFformat, and a bulk output in CSV format, which includes daily heat and mass fluxes, as wellas a water balance summary at a specified depth. This section specifies the directory structure,naming conventions for the output files, and the frequency at which the results are generated(output time steps).262.4. Model SetupInitial Condition ProfilesThe model was initialized using the average temperature profile from the combined P2 andP3 CTD casts and moorings. The initial salinity profile was based on the average of the salinityprofiles from the P2 and P3 CTD casts, see Figure 2.3.Figure 2.3: Temperature ( ) and salinity ( ) profiles of (a) May 3rd 2016, (b) July 5th 2016(c) May 11th 2017, (d) June 20th 2017 used to initialize the model.MeteorologyThis section provides details on surface forcing and meteorological data, as well as theoptions for calculating shortwave albedo and emissivity, as outlined in Section 2.3.2. It alsospecifies the bulk transfer coefficients and extinction coefficients. Additionally, the model incor-porates scaling factors for shortwave radiation, longwave radiation, wind speed, air temperature,relative humidity, and rainfall, which adjust these parameters relative to the values providedin the user’s meteorological data. These factors can be used to calibrate the model, and in thisstudy, the wind speed factor, shortwave factor, and relative humidity factor were employed forcalibration.To reduce the turbidity in BML, alum was added to the lake in the fall of 2016, resulting in a272.4. Model Setupsignificant decrease in turbidity and, consequently, the light extinction coefficient. This changewas incorporated into the model by applying one-tenth of the 2016 light extinction coefficientfor 2017 (Table 2.6).Table 2.6: GLM meteorology coefficientsSeason Spring SummerYear 2016 2017 2016 2017Wind Factor Wind speed scaling factor 0.6 0.5 0.8 0.65SW Factor Shortwave radiation scaling factor 1RH Factor Relative humidity scaling factor 1Albedo mode Shortwave albedo mode 2XE Transfer coefficient for latent heat flux 0.001XY Transfer coefficient for sensible heat flux 0.0013XD Transfer coefficient for momentum 0.0013Kw Light extinction coefficient (m−1) 4 0.4 4 0.4Inflows and OutflowsIn the ’Inflow’ section of the model configuration file, details on the inflow input file directoryand inflow morphometry, including streambed half-angle, slope, and drag are specified. Inflowscan be specified as surface runoff from the surrounding lake domain, rivers entering at thesurface, or submerged inflows. Similarly, in the ’Outflow’ section, the outlet elevation, basinlength, and width at the outlet are specified.GLM considers three types of outflows: seepage, withdrawal, and overflow. For this study,to accurately represent the conditions at BML, GLM was configured with two inflows and oneoutflow: a horizontal submerged inflow at the surface to represent the pumped water into BML,a vertical submerged inflow at a depth of 9m to represent the pore water inflow, and finally asurface withdrawal outflow to represent the pumped-out water from BML. The flow rates forthe pumped water entering and exiting the lake were set to match those measured from fielddata, see Figure 2.4.282.4. Model SetupFigure 2.4: Flow rate of pumped in water ( ) and pumped out water ( ) of (a) 2016 and (b)2017Porewater flow rate was initially estimated based on previous studies to be approximately0O5mmd−1 to 1mmd−1 (Dunmola et al., 2023); however, in the present study a flow rate of3mmd−1 was found to more accurately represent the salt budget of BML. Since the waterpumped from Beaver Creek Reservoir is located near the P3 station (Figure 1.4), the tempera-ture of the pumped-in water was set to match the surface water temperatures recorded at theP3 mooring. The porewater temperature was set to the estimated sediment temperature of thelake, as shown in Figure 2.5.SedimentAs discussed in Section 2.3.3, the ’Sediment’ section of the configuration file provides detailson sediment heating. Specifically, the amplitude and mean temperature of the tailings inBML were estimated based on previous studies (Zhao, 2023, Dompierre, 2017). Moreover,the soil-sediment thermal conductivity of the fluid fine tailings of BML was assumed to be0O6 Jm−1 s−1 ◦C−1 following estimates from (Dompierre and Barbour, 2016). These studieshelped approximate the temperature characteristics of the tailings, which were then used to292.5. Preliminary Processing of Field Dataconfigure the sediment heating model in GLM for 2016 and 2017 (Figure 2.5).Figure 2.5: Variations in sediment temperature of 2016 ( ) and 2017 ( ) with time, used toinitialize the model based on estimates from Dompierre and Barbour, 20162.5 Preliminary Processing of Field DataField measurements were processed to facilitate comparison with the model results. This pro-cessing involved temporal averaging of the moored measurements at P2 and P3, as well asspatial averaging, specifically horizontal averaging, of both moored and CTD measurementscollected across P2 and P3. The details of these data processing steps are discussed in thissection.2.5.1 Temporal AveragingThe mooring data collected from P2 and P3 were recorded at approximately 10 s intervals. Toimprove data manageability and facilitate analysis, raw data were smoothed and averaged toan hourly resolution. Two different averaging methods were applied: hourly bin-averaging andhourly moving-averaging. The equations used for these averaging methods are presented in thissection. It is important to note that all mooring data are results from the hourly moving-averageapproach.302.5. Preliminary Processing of Field DataBoth hourly bin-averaging and hourly moving-average were computed using a similar integral-based averaging approach, mathematically expressed as:iavg(t, z) =1∆t∫ t+∆t2t−∆t2i (τ, z) dτ (2.6)Where iavg(t, z) is the averaged temperature at depth z around time t, i (τ, z) is the measuredtemperature at time τ . ∆t is the averaging interval and is set to be one hour.Hourly Bin-AveragingFor hourly bin-averaging, this integral was evaluated over fixed, discrete, and non-overlappinghourly intervals. Each hourly interval was centered exactly on each hour, extending ± 30min,resulting in uniformly spaced hourly averaged temperature values across all depths at P2 andP3.Hourly Moving-AverageIn contrast, the hourly moving-average applied the integral to continuously sliding intervals,each centered on individual timestamps. This procedure generated smoother, continuouslyvarying temperature data, as each averaging window significantly overlapped adjacent intervals.2.5.2 Horizontal AveragingThe average of the temperatures measured at P2 (i2) and P3 (i3) at any given depth and timei¯ (t, z) =i2(t, z) + i3(t, z)2(2.7)was used as an approximation to the spatially averaged temperature in the lake at that depthand time. We compare this average value with the output of the 1-D GLM model. Additionally,horizontal averaging of CTD casts at P2 and P3 was performed to generate initial conditionsfor the model simulations.To ensure consistent timestamps when averaging temperatures across both platforms, thehourly bin-averaged and hourly moving-averaged datasets were used. Both datasets provideduniformly spaced timestamps, enabling direct comparison and combined analysis at identical312.6. Validation Parameters and Evaluation Metricshourly intervals. Additionally, because each mooring covered a slightly different depth range -although with the same vertical resolution of 1m - spatial averaging was performed only overthe depth intervals common to both platforms. Similarly, for the CTD casts, differences indepth ranges and vertical resolutions were addressed by interpolating all raw CTD profiles ontoa uniform 0O1m depth grid. After interpolation, spatial averaging between platforms P2 andP3 was carried out for CTD profiles that shared overlapping depth ranges.2.6 Validation Parameters and Evaluation MetricsIn the spring, following ice-off, the lake’s thermal structure is weak, and strong winds during thisperiod can lead to significant mixing. The presence of saline water in BML further complicatesthe physical processes occurring during this time. In contrast, during the summer, high airtemperatures combined with weak winds enhance the thermal stratification within the lake.To assess the accuracy of GLM in simulating seasonal stratification and mixing in BML, theHeat Content (HC) and Schmidt Stability Index (SSI) were calculated for the model resultsand compared with the corresponding values from field measurements.The HC of the lake provides insight into the amount of heat stored in the lake at anygiven time, while the SSI indicates the stability of the lake’s thermal structure and is the en-ergy required to fully mix it at a given time. These two parameters were selected to validatethe model’s ability to estimate the effects of seasonal variations between spring and summer.Additionally, the Wedderburn Number and internal seiche period, were used to identify andcharacterize wind events that were strong enough to trigger upwelling of the hypolimnion atvarious stations within the lake. Finally, to compare the model’s temporal and salinity struc-ture with field data, the Root Mean Square Error (RMSE), was used to evaluate the model’sperformance.2.6.1 Heat ContentTo track the heat predicted by the model for BML, the model’s HC was calculated and comparedwith the HC derived from mooring data. The following equation, adapted fromWeinstock, 1981,was used to calculate HC:322.6. Validation Parameters and Evaluation MetricsHX =∫Xpi (z)/(z)A(z) dz (2.8)Where Xp is the specific heat capacity of water (4182 JRkgOX), i , /, and A represent thetemperature, density, and cross-sectional area of the water column at depth z, respectively.Note that for comparisons with the 1-D model, the horizontal average of the temperatureat P2 and P3 moorings, i¯ , was used to calculate the HC, see Section 2.5.2. Additionally, dueto the absence of salinity measurements in the mooring data, salinity was assumed to be zeroin all cases when calculating water density, which was then used to compute heat content. Thissimplification was applied consistently across both field observations (CTD casts and moorings)and model output to enable a meaningful and fair comparison.2.6.2 Schmidt Stability IndexTo calculate the energy required to mix the entire lake at a given time, the SSI was computedfor both the model results and the mooring data using the equation provided in Pieters andLawrence, 2009. This calculation helps assess the model’s ability to accurately simulate thelake’s density structure.hhI =gA(0)∫A(z) (/(z)− /¯) z dz (2.9)where /¯ is the volume-averaged density of the water column and A(0) is the lake surface area.The SSI for the model was computed by assuming zero salinity when calculating water den-sity - referred to here as thermal stability. This simplification was necessary due to the absenceof salinity measurements in the mooring data. To ensure a consistent and fair comparisonbetween model output and field observations (CTD casts and moorings), the effects of salinityon water column density, and consequently on stability, were excluded throughout this study.For comparisons with the 1-D model, the horizontally averaged temperature profile fromthe P2 and P3 moorings, i¯ , was used to compute SSI (see Section 2.5.2). It is important tonote that the SSI derived using this approach becomes less reliable during periods of upwelling- a limitation that is further addressed in Chapter 4.332.6. Validation Parameters and Evaluation Metrics2.6.3 Seiching ParametersTo investigate the thermal dynamics of BML, the internal seiche period was calculated for theCTD casts from both 2016 and 2017. The internal seiche period represents the time requiredfor a complete oscillation of a stratified water column and is influenced by factors such as thedegree of stratification, lake length, and the depth of the thermocline. The phase speed of theinternal seiche, X, and the period, i , adapted from Stevens and Lawrence, 1997 is given by:X =√g′h1h2h1 + h2(2.10)i =2aX(2.11)where a is the length of the lake, h1 and h2 are the initial thickness of the top and bottom layerrespectively. The value of h1 was determined by identifying the depth at which the densitydifference between that depth and the average density of the entire water column was at aminimum.2.6.4 Root Mean Square ErrorTo evaluate the performance of the model against field observations, the root mean square error(RMSE) was calculated. RMSE provides a quantitative measure of the differences between val-ues predicted by the model and those observed in the field. This was computed for temperature,heat content, and thermal stability using the following equation:RMSE =√√√√ 1nn∑z21(XmodelLz −XobsLz)2 (2.12)where XmodelLz and XossLz represent the modelled and observed values at time step i, and n isthe total number of time steps in the analysis period.To ensure a consistent temporal comparison between the model and field data, observedtemperatures were temporally averaged using a ± 30min window centered on each model timestep. This approach accounts for the higher temporal resolution and variability of the mooringobservations and aligns them with the model’s 1-hour output intervals.342.7. CalibrationFor spatial alignment, model temperatures were vertically averaged over a ± 0O15m rangearound the field data depths to address minor mismatches in depth resolution between themodel layers and sensor positions. These time and depth aligned datasets were then used tocalculate the RMSE between i¯ and the corresponding model predictions for each time step.2.7 CalibrationA series of simulations with different initial conditions were conducted to calibrate the modelfor the specific characteristics of the study site. Prior to calibration for the target periods, asensitivity analysis was performed for spring 2016 to evaluate the influence of key parameters- such as mixing coefficients, meteorological coefficients, initial conditions, and meteorologicalinputs - on model performance.This analysis involved a set of model simulations in which extreme values were assigned toindividual parameters. For example, meteorological variables were set to zero or held constantone at a time to assess its effect. Additional test cases included initializing the model with zerosalinity, using a simple two-layer stratification, and applying constant temperature and salinityprofiles. Other simulations explored the effects of applying an 8-hour smoothed wind speed andreplacing the local wind speed data with that from Mildred Lake Reservoir, a lake 3 km awayfrom BML.Later, upon comparing BML’s 2016 meteorological data with that from Mildred Lake Reser-voir it was discovered that the data for BML was shifted by approximately 22 hours. Afteridentifying this discrepancy, a second set of simulations was conducted using the meteorologicaldata corrected for this time shift. The correction had a significant impact on model perfor-mance. In the initial set of simulations, despite beginning with a clearly stratified water columnon May 3rd, 2016, the model mixed this stratification within just a few hours. However, oncethe timing error was addressed in the second set of runs, the model was able to preserve theinitial stratification more realistically. This correction highlighted the importance of the timingof heating/cooling and wind forcing on the lake’s mixing dynamics during spring.After validating the model’s ability to produce meaningful results, additional componentsof BML’s hydrology - such as rainfall, inflows, and outflows - were incorporated into the model.352.7. CalibrationSeveral scenarios were tested during the implementation of these new forcings. These includedvarious configurations of inflow, outflow, and porewater expression, followed by the addition ofrainfall.Inflow was modelled using both constant and dynamic flow rates, initially assuming a salinityof zero. In later scenarios, the inflowing water was assigned a salinity of 0O2 ppt. Outflowconfiguration was also introduced, simulating a surface withdrawal at the very top layer of thewater column. Finally, saline porewater expression was incorporated as a submerged inflowlocated at the bottom of the lake. This bottom inflow was tested under several thermal andsalinity scenarios, including constant temperature and salinity, linearly increasing temperatureprofiles, and seasonally variable temperature patterns (Figure 2.5).Following the implementation of these additional forcings, the model was calibrated byadjusting a range of key parameters, including the wind factor, shortwave radiation factor,relative humidity factor, albedo mode, sediment temperature, sediment thermal conductivity,porewater temperature and flow rate, as well as various initial temperature and salinity profiles.A summary of the calibration process and the adjustments made for each of these parametersis presented below.Wind FactorGLM was calibrated by testing wind factors ranging from 0.5 to 1.0. For each simulation,the area under the thermal stability curve was calculated and compared to that of the field-based thermal stability curve derived from i¯ . The wind factor that resulted in the closestmatch to the observed thermal stability was selected as the optimal value. This calibration wasperformed separately for each simulation period, specifically the spring and summer seasons of2016 and 2017, see Table 2.6.AlbedoAlbedo calibration was performed by comparing the observed albedo at BML with theparameterized albedo values used in GLM. The observed albedo, αSML, was calculated directlyfrom the meteorological data using the following equation:362.7. CalibrationαSML = 1 + (1− gdhD +gdhRgdhD) (2.13)where gdhD and gdhR are the incoming and reflected shortwave radiations respectively.The calculated αSML was then compared against the albedo values used in GLM, denotedas αGLM which are derived from various empirical parameterizations, see Section 2.3.2. Af-ter evaluating the differences between αSML and each αGLM formulation over the simulationperiod, the most representative albedo scheme for BML was found to be mode 2, see (Table2.6).Shortwave FactorShortwave factors ranging from 0.9 to 1.1 were tested in GLM simulations. For each run,the area under the heat content curve was calculated and compared to the corresponding areaunder the heat content curve of i¯ . The shortwave factor that produced the closest match tothe observed heat content was found to be 1 for all four simulation periods (Table 2.6).Relative Humidity FactorBecause the observed hydrocarbon sheen on the surface of BML is known to suppress evap-oration Chang, 2020, the model was calibrated using a range of relative humidity factors from0.5 to 1.0 to account for this effect.The rationale behind this approach is that higher relative humidity in the atmosphereindicates that the air is already holding more moisture, thereby reducing the potential forevaporation from the lake surface. In the context of modelling, adjusting the relative humidityfactor could allow for better representation of the reduced evaporative flux caused by the sheen.For each simulation, the area under the heat content curve was calculated and comparedto that of the field-based heat content curve derived from i¯ . The relative humidity factor thatproduced the closest agreement with observations was found to be 1 for all four simulationperiods (Table 2.6).Sediment HeatingThe temperature of the tailings is most dynamic during the summer, increasing by approx-imately 8 ◦C over the course of the season (Dompierre and Barbour, 2016). To capture this372.7. Calibrationvariability, GLM was calibrated over a range of values for the annual mean sediment tem-perature (izmean), the day of year when sediment temperature peaks (dez), and the seasonalamplitude of the sediment temperature variation (δiz), see Section 2.3.3. Specifically, izmeanvalues of 6 ◦C and 10 ◦C were tested, with dez ranging from DOY 240 to 260, and δiz set to4 ◦C and 5 ◦C. The simulation that produced bottom water temperatures most closely match-ing those of the deepest mooring measurements was selected as the calibrated result. Thiscalibration was performed for both the spring of 2016 and 2017, see Figure 2.5.38Chapter 3ResultsIn the previous chapter, the methodology behind field data collection and model setup andevaluation metrics were discussed. In this chapter, we begin by presenting field measurementsthat provide essential background information for analyzing each of the four modelling periods(Tables 2.5 and 2.6). Following this, we present the four simulation periods arranged in orderof increasing complexity: summer 2017, summer 2016, spring 2017, and spring 2016. For eachperiod, notable wind events are identified based on field observations, followed by a comparisonof model results with the corresponding measurements.To ensure a meaningful comparison between the model results and field observations, theaverage temperature of the P2 and P3 moorings is calculated (i¯ ), see Section 2.5.2. Thisapproach helps eliminate the effects of 2-D processes that occur between the two platforms,providing a simpler representation of the lake’s overall behaviour.The following results include the hourly moving average temperature measurements (Section2.5.1) of the P2 mooring ( ), the P3 mooring ( ), i¯ ( ), and the GLM results ( ).3.1 Observations3.1.1 Meteorological DataA summary of key meteorological data for 2016 and 2017 is presented in Figures 3.1 and 3.2,respectively. These measurements were used as external the forcing for the GLM simulations.In Figure 3.1, panel (a) shows the diurnal trends of incoming shortwave and net longwaveradiation, with incoming shortwave radiation reaching up to 897Wm−2 and net longwaveradiation dropping to −120Wm−2 midday. Panel (b) presents air temperatures ranging from2O9 ◦C to 29O5 ◦C, with an average of 17 ◦C. Panel (c) displays relative humidity values ranging393.1. Observationsfrom 15.4% to 100%, with and average of 61.7%. Panel (d) shows wind speeds peaking up to13O5m s−1 in 2016.Similarly, in Figure 3.2, panel (a) shows incoming shortwave radiation reaching up to896Wm−2 and net longwave radiation dropping to −117Wm−2 midday. In panel (b), airtemperatures range from 3 ◦C to 30 ◦C, with an average of 17O3 ◦C. Panel (c) shows relativehumidity ranging from 18.8% to 100%, with an average of 60.9%. Finally, panel (d) presentswind speeds peaking up to 13O9m s−1 in 2017.In these figures, notable wind events for each of the four periods are highlighted in gray.These events are defined as periods when the Wedderburn number drops below 1 for at leastthe duration of the internal seiche period (see Sections 2.6.3 and 3.1.3).3.1.2 Mooring, CTD and DO DataFigures 3.3 and 3.4 present a summary of key meteorological data and dissolved oxygen (DO)measurements at P1 along with moored temperature profiles at P2 and P3 in 2016 and 2017respectively. These measurements offer valuable background information that will help contex-tualize the impact of the highlighted wind events on field observations, which will be discussedlater in this chapter as well as in Chapter 4.403.1.ObservationsFigure 3.1: Time series of meteorological data from 2016. (a) Incoming shortwave radiation ( ) and net longwave radiation( ), (b) airtemperature, (c) relative humidity, and (d) wind speed at 10m above the lake surface with selected wind events highlighted in gray413.1.ObservationsFigure 3.2: Time series of meteorological data from 2017. (a) Incoming shortwave radiation ( ) and net longwave radiation( ), (b) airtemperature, (c) relative humidity, and (d) wind speed at 10m above the lake surface with selected wind events highlighted in gray423.1.ObservationsFigure 3.3: Time series of meteorological and of mooring temperatures at BML from 2016. (a) wind speed at 10m above the lake surface,(b) wind direction, (c) temperature from 1m to 10m at P2, (d) temperatures from 0O5m to 9m at P3, with dashed lines indicating theimd (≈ 3O54 ◦C); and (e) dissolved oxygen 2m (•) and 6m (•) at P1 with selected wind events highlighted in gray433.1.ObservationsFigure 3.4: Time series of meteorological and of mooring temperatures at BML from 2017. (a) wind speed at 10m above the lake surface,(b) wind direction, (c) temperature from 0O5m to 11O5m at P2, (d) temperatures from 1m to 9m at P3, with dashed lines indicating theimd (≈ 3O54 ◦C); and (e) dissolved oxygen 2m (•) and 6m (•) at P1 with selected wind events highlighted in gray443.1. ObservationsFigures 3.5 and 3.6 present the temperature and salinity profiles from the three CTD surveysin 2016 and 2017 with the moored measurements corresponding to the exact time of the CTDsurveys also shown in these figures. Note, in 2016 (Figure 3.5), a sharp increase in salinity isobserved in the deeper waters (>9m) at P3 and P2 on both DOY 187 and DOY 224. Thisincrease is due to the instrument approaching the bottom tailings during the CTD casts, whichhas a salinity of 4 ppt.In 2016 the first open water CTD profiles were collected on May 3rd (DOY 124), six daysafter ice-off (Figure 2.1). At this time the water column was stratified in temperature andsalinity at both P2 and P3 with a warmer, fresher upper layer on top of a cooler, more salinelower layer (Figure 3.5). Note, much of the deep water is below imd, and this, along with thefresh water cap, indicates that spring turnover did not occur between ice-off and the first CTDcast. On May 3rd, the CTD surveys show slight differences between P2 and P3 with the watercolumn at P2 slightly cooler and more saline than at P3 through most of the depth. By the nextCTD survey on July 5th (DOY 187), temperatures at both stations increased by roughly 11 ◦C.The epilimnion at both P2 and P3 was more saline than on May 3rd, while the hypolimnionbecame less saline, suggesting that mixing has occurred during this period. A clear two-layerstratification was still present with the top 4m both warmer and fresher than at depth. Slightdifferences between P2 and P3 in both temperature and salinity likely result in from internalmotions discussed in subsequent sections. On the final CTD survey of August 11th (DOY 224),the surface layer at both stations warmed by an additional 2 ◦C and deepened to approximately5O5m, seen most clearly in the salinity.In 2017 the first open water CTD profiles were collected on May 11th (DOY 131), six daysafter ice-off (Figure 2.1). The water column was again temperature and salinity stratified atboth P2 and P3 (Figure 3.6). The presence of a fresh water cap suggests that turnover hadnot occurred between ice-off and this first CTD cast. By the next CTD cast on June 20th(DOY 171), water temperature at both stations increased by an average of 6O5 ◦C. From May11th to June 20th, the salinity increased in the epilimnion and decreased in the hypolimnion atboth P2 and P3, again suggesting that mixing occurred during this period. On the third CTDcast, conducted on August 11th (DOY 223), water temperatures at both stations increased byanother 3 ◦C.453.1.ObservationsFigure 3.5: Temperature and salinity profiles taken from the CTD casts on May 3rd (DOY 124), July 5th (DOY 187), and August 11th(DOY 224) of 2016 at P2 ( ) and P3 ( ); with dashed lines indicating the imd (≈ 3O54 ◦C). Mooring data at corresponding times areshown as dots.463.1.ObservationsFigure 3.6: Temperature and salinity profiles taken from the CTD casts on May 11th (DOY 131), June 20th (DOY 171), and August11th (DOY 223) of 2017 at P2 ( ) and P3 ( ); with dashed lines indicating the imd (≈ 3O54 ◦C). Mooring data at corresponding timesare shown as dots.473.1. Observations3.1.3 Internal Seiche PeriodUnderstanding how the internal seiche period evolves both seasonally and annually helps identifywind events that may disrupt the lake’s thermal structure (Section 3.1.3). Based on the resultspresented in Figure 3.7, it was determined that an internal seiche period of approximately24 hours was appropriate for both the spring of 2016 and 2017. For the summer of bothyears, a period of 12 hours was chosen. These values are approximate, as the internal seicheperiod is inherently variable and dependent on the lake’s thermal structure, which can fluctuatethroughout the year due to changing wind patterns.Figure 3.7: Internal seiche period for CTD casts at P2 (T) and P3 (T) during 2016 and 2017.483.2. Summer Simulations3.2 Summer Simulations3.2.1 Summer 2017 (Day of Year 171.5 - 224.0)GLM was used to model BML for the summer of 2017, from midday on June 20th (DOY 171.5)until midnight on August 11th (DOY 224.0), see Table 2.5. Our analysis begins by annotatingthe notable wind events highlighted where W < 1, see Figure 3.8(b). These wind events have aduration of at least the internal seiche period, which is approximately 12 hours during summer.The timing of these events is:• Event 1: June 21st - June 22nd (DOY 172.1 - DOY 173.8)• Event 2: July 4th - July 5th (DOY 185 - DOY 186.7)• Event 3: July 16th - July 17th (DOY 197.7 - DOY 198.5)• Event 4: July 24th (DOY 205 - DOY 205.9)Figure 3.8: Time series for June 20th - August 11th, 2017 of (a) air temperature; and (b)Wedderburn number of the average of P2 and P3 moorings, with major wind events highlightedin grey. Temperature contours for the (c) P2 mooring, (d) P3 mooring, (e) the average of theP2 and P3 moorings and (f) the model-simulated temperature contours.493.2. Summer SimulationsEvent 1 (DOY 172.1 - DOY 173.8)At the beginning of summer, the lake exhibits a relatively thick and cold hypolimnion at bothplatforms, with the epilimnion at P2 being slightly warmer than at P3, see Figure 3.8(c,d). Atthe onset of the first wind event, southwesterly winds disrupt this thermal stratification, pushingwarm water down at P2 while pulling cooler waters up at P3. However, when the wind directionsuddenly shifts to northerly on DOY 173 (Figure 3.4(b)), warmer waters are downwelled at P3,while cooler waters are upwelled at P2. As a result, temperatures at P3 becomes uniformwith depth for approximately 12 hours, while a weak cold stratification persists at P2 (seeAppendix C). This event decreases the lake’s heat content and thermal stability by 2.2% and64% respectively, see Figure 3.9(b,c).Between Events 1 and 2 (DOY 173.8 - DOY 185)As the winds calm and air temperatures rise, the lake begins to restratify: the epilimnionwarms up, while a cold, saline hypolimnion is restored at both platforms. Weak winds on DOY177.5 and 179.5 lead to oscillations in the thermocline depth at both platforms; however, thesewinds are strong enough to induce upwelling. Although these two weak wind events cause atemporary decrease in the lake’s thermal stability, the overall heat content and thermal stabilityof the lake continue to rise, see Figure 3.9(b,c). After the winds on DOY 179.5 subside, theepilimnion at both platforms, particularly at P2, warms further, and the epilimnion at P2begins to deepen.Event 2 (DOY 185 - DOY 186.7)On DOY 185, southwesterly winds cause downwelling of warm waters at P2 and upwellingof cool water at P3. On DOY 186, as the wind shifts to northwesterly (Figure 3.4(b)). Thischange in the wind coincides with the shoaling of the hypolimnion at P2, with cooler watersbeing pulled up to the surface, while the epilimnion at P3 deepens and warmer waters arepushed down deeper into the water column, see Figure 3.8(c,d). This event results in a 22%decrease in the lake’s thermal stability (Figure 3.9(c)).503.2. Summer SimulationsBetween Events 2 and 3 (DOY 186.7 - DOY 197.7)As the winds from the second major wind event subside, the lake’s surface waters begin towarm up at both platforms, such that the heat content continues rising (Figure 3.9(b)). Duringthis period, the thermal stability of the lake increases again at the same rate as before thesecond wind event. On DOY 195.1, northerly winds lack the energy to disrupt the lake’s strongthermal stratification, resulting only in weak upwelling of cool waters at P2 and downwelling ofwarm waters at P3. This event causes a brief decrease in the lake’s thermal stability. However,the lake quickly recovers, and the thermal stratification returns to its pre-wind event state.Afterwards, air temperature rises and peaks at 30 ◦C on DOY 196.6, coinciding with the theheat content and thermal stability of the lake reaching their maximum at this time, see Figure3.9(b,c).Event 3 (DOY 197.7 - DOY 198.5)Winds blowing from the southwest (Figure 3.4(b)) during this event cause upwelling of coolwaters at P3 and downwelling of warm waters at P2, leading to a 31% decrease in the lake’sthermal stability.Between Events 3 and 4 (DOY 198.5 - DOY 205)After the winds from the third wind event subside, weaker winds on DOY 198.6 and 200cause oscillation of the thermocline at depth across both platforms. These winds lead to a briefdecrease in the thermal stability and heat content of the lake. However, overall, the thermalstability continues to rise, although at a slower rate compared to before the third wind event, seeFigure 3.9(b,c). Additionally, the epilimnion at both platforms cool down slightly, see Figure3.8(c,d).Event 4 (DOY 205 - DOY 205.9)Strong northerly winds on DOY 205 (Figure 3.4(b)) cause a complete upwelling of coolwaters at P2 and downwelling of warm waters at P3. Although a dramatic 46% drop inthermal stability is observed and the water columns at both platforms appear homogeneous, acold saline layer (>9m) is maintained at both platforms, and the lake does not turnover (Figure3.4(c,d)).513.2. Summer SimulationsAfter Event 4 (DOY 205.9 - 224)Immediately after the strong winds from the fourth wind event subside, warm waters fromP3 are downwelled at P2, and cooler waters from P2 are upwelled at P3. Following this, thelake begins to return to equilibrium, and the epilimnion at both platforms starts to warm again.Weaker winds on DOY 209, 212.2, and 218 cause the thermocline to oscillate at P2 and P3(Figure 3.8(c,d)), resulting in brief drops in thermal stability (Figure 3.9(c)). However, thelake generally maintains a strong stratification, with a relatively warm epilimnion and a cool,saline hypolimnion. Additionally, the lake’s thermal stability and heat content continue to rise,although at a slower rate than before the fourth wind event.Figure 3.9: Comparison of summer 2017 (June 20th - August 11th) model and field WedderburnNumber, HC, and SSI. (a) Wedderburn number of the average of P2 and P3 moorings, withperiods when W < 1 for most, if not all, of the duration of the summer internal seiche period(≈ 12 hours) highlighted in grey. (b) HC and (c) SSI time series. The average of the P2 andP3 moorings ( ) is compared to the model ( ).GLM SimulationComparison of Figure 3.8(f) with (e) shows that the model captures the overall evolutionof the thermal structure of the lake during summer 2017. Differences between the thermal523.2. Summer Simulationsstructure at P2 and P3 are largely due to upwelling, downwelling, and internal seiching. These2-D effects are smoothed by averaging P2 and P3, in which the effects of the storms is reflectedas a deepening of the thermocline, see Figure 3.8(e).Figure 3.8(f) presents the model results for this period, showing that, similar to the temper-ature contours of i¯ in Figure 3.8(e), the effects of the four storms are reflected as the deepeningof the thermocline. Notably, during the first wind event, the warmer epilimnion at the surfacemixes deeper, causing the hypolimnion to become thinner. While the model heat content closelyfollows that from the field data (Figure 3.9(b)), the model fails to capture the secondary strat-ification within the epilimnion as the summer progresses (Figure 3.8(e,f)). This suggests that,while the model simulates the correct amount of heating observed in the field, it distributesthis heat differently. Comparing the temperature contours in Figures 3.8(f) and (e), we observethat the model predicts a thicker and cooler hypolimnion compared to the field observations.This discrepancy is further evident when comparing the thermal stability of the model to thatof the field, as the model overestimates the thermal stability after the third wind event (Figure3.9(c)). Additionally, the model does not fully capture the intensity of the epilimnion heatingbefore wind event 2 or between wind events 2 and 3.These discrepancies in thermal structures are also reflected in the comparison with the ob-served Wedderburn number, where the model generally underestimates the Wedderburn num-ber, especially during storms. This indicates that the model struggles to capture the fullintensity of the storm effects (Figure 3.9(a)). This is especially evident in Figure 3.9(c), wherethe observed thermal stability drops significantly during these periods, while in the model thedecline in thermal stability is much smaller, reflecting a dampened version of the observedbehavior.Although GLM effectively models the overall thermal structure of BML, it struggles tocapture the intense thermal variations following each wind event. This limitation is likely dueto the model’s 1-D representation of the lake. The limitations of a 1-D approach will be exploredfurther in the next chapter.533.2. Summer SimulationsCTD CastsFigure 3.10 presents a comparison of the temperature, salinity, and density profiles fromthe CTD casts at P2 and P3 with the model results. The top row of panels corresponds to thesecond CTD cast, which was used to initialize the model using the average temperature andsalinity profiles from the P2 and P3 CTD casts. The model output following the first time stepon June 20th (DOY 171) closely matches the average of both CTD casts, see Table 2.5.Figure 3.10: Comparison of summer 2017 model and CTD profiles. The top row demonstratesthe temperature, salinity, and density profiles from the second CTD casts at P2 ( ), P3 ( ), andthe model results ( ) on June 20th (DOY 171). The bottom row presents the same parametersfor the third CTD cast on August 11th (DOY 223); dashed lines indicate the imd (≈ 3O54 ◦C).In the bottom row of panels, the salinity profile on August 11th (DOY 223) from the modelvery closely aligns with the average of the two CTD casts. However, the model temperatureprofile on this day shows a sharper metalimnion and thicker epilimnion interface comparedto the field observations. Additionally, the model underestimates the temperature in the hy-polimnion. This discrepancy may be due to an inaccurate modelling of the heat transport orof the porewater expressed from the underlying tailings.543.2. Summer Simulations3.2.2 Summer 2016 (Day of Year 187.5 - 225.0)We identified three wind events during the summer of 2016 where W < 1 for a duration of atleast the internal seiche period (≈ 12 hours during summer), see Figure 3.11(b). The timing ofthese events is:• Event 1: July 21st (DOY 203.3 - DOY 203.8)• Event 2: July 24th - July 25th (DOY 206.3 - DOY 207.1)• Event 3: August 1st (DOY 214.3 - DOY 214.8)Figure 3.11: Time series for July 5th - August 11th, 2016 of (a) air temperature; and (b)Wedderburn number of the average of P2 and P3 moorings, with major wind events highlightedin grey. Temperature contours for the (c) P2 mooring, (d) P3 mooring, (e) the average of theP2 and P3 moorings and (f) the model-simulated temperature contours.Before Event 1 (DOY 187.5 - DOY 203.3)On DOY 188.5, temperatures at P2 begin with a relatively shallow epilimnion, a thickthermocline, and a deep hypolimnion, while at P3, we observe a thicker and warmer epilimnionwith a thinner hypolimnion. Although there is no pronounced upwelling and downwelling553.2. Summer Simulationsduring this period, a clear seiche is evident at both platforms, see Figure 3.11(c,d). Overall,the epilimnion at P3 remains warmer than at P2 throughout this period.Event 1 (DOY 203.3 - DOY 203.8)Strong winds from southwest on DOY 203.2 (Figure 3.3(b)) lead to partial upwelling ofcooler waters at P2 and downwelling of warmer waters at P3. This event results in a 6% and3.6% decrease in the lake’s heat content and thermal stability, respectively (Figure 3.12(b,c)).Between Events 1 and 2 (DOY 203.8 - DOY 206.3)The first event is followed by a sudden drop in air temperature on DOY 204.2 (Figure3.11(a)). After the winds from the first event subside, the lake begins to regain its previ-ous equilibrium, although with a noticeably cooler epilimnion at both platforms, see Figure3.11(c,d). The heat content of the lake start to increase again, just before the second windevent occurs, while the thermal stability remains lower (Figure 3.12(b,c)).Event 2 (DOY 206.3 - DOY 207.1)On DOY 206.3, strong southwesterly winds cause warm waters to be pushed deeper at P2,while cooler hypolimnetic waters are partially upwelled at P3 (Figure 3.11(c,d)). This resultsin a 10% decrease in the lake’s thermal stability, see Figure 3.12(c).Between Events 2 and 3 (DOY 207.1 - DOY 214.3)After the second storm, the air temperature rises and the epilimnion at both platformswarms. During this period, an oscillation in the thermocline depth across the two platformsis observed, caused by weak winds that result in a slight seiching of the thermocline. Despitethis, both the thermal stability and heat content of the lake continue to rise, with thermalstability peaking on DOY 212.6, just before it begins to decrease again before the third windevent (Figure 3.12(c)).Event 3 (DOY 214.3 - DOY 214.8)At the onset of the third wind event, strong westerly winds (Figure 3.3(b)) cause the partialdownwelling of warmer waters at P3, while cooler hypolimnetic waters at P2 are partiallyupwelled (Figure 3.11(c,d)). During this event the heat content increases slightly, while the563.2. Summer Simulationsthermal stability exhibits a small decline, see Figure 3.12(b,c).After Event 3 (DOY 214.8 - DOY 225)Although mooring data at P2 is missing for part of this period, we can observe that thenortherly winds on DOY 220 caused a dramatic deepening of the thermocline at P3, indicatingthat upwelling of cooler waters likely occurred at P2, see Figure 3.11(c,d). After DOY 220,The usual oscillation in thermocline depth was observed at P3, suggesting a similar responseat P2. Despite the weak seiching during this time, the lake’s thermal stability continues torise at a similar rate as before the third wind event, and the lake maintains a strong summerstratification.Figure 3.12: Comparison of summer 2016 (July 5th - August 11th) model and field WedderburnNumber, HC, and SSI. (a) Wedderburn number of the average of P2 and P3 moorings, withperiods when W < 1 for most, if not all, of the duration of the summer internal seiche period(approximately 12 hours) highlighted in grey. (b) HC and (c) SSI time series. The average ofthe P2 and P3 moorings ( ) is compared to the model ( ).GLM SimulationThe temperature contours of i¯ smooth out the 2-D processes, reflecting the effects of thestorms as a deepening of the thermocline (Figure 3.11(e)). In contrast to the i¯ temperature573.2. Summer Simulationscontours for the summer of 2017 (Section 3.2.1), the i¯ temperature contours for the summerof 2016 show less mixing throughout the water column during all of the storms, with the coolsaline hypolimnion maintained throughout the summer.Figure 3.11(f) presents the model results, which similarly reflect the effects of these threemajor wind events as deepening of the thermocline. The thermocline gradually deepens overtime with a very gentle slope. However, we can also observe that the model struggles to simulatethe smooth transition from the epilimnion to the hypolimnion, as seen in the field observations.Instead, the model predicts a sharp interface between these layers.Up until the first storm, the model predicts a slight lower Wedderburn number than what isobserved in the field, see Figure 3.12(a). After the second storm, however, the model predicts aslightly higher Wedderburn number than the observed values. Despite this, the model accuratelypredicts the timing of variations in the Wedderburn number, especially during the three stormsmentioned, although it fails to fully capture the severity of second and third storms. Thisdiscrepancy is particularly evident in Figure 3.12(b,c), where the observed heat content andespecially thermal stability drop significantly during these periods, while the decline in themodel predictions are much smaller.While the model follows the rate of heat content increase observed in the field, by the end ofthe simulated period it slightly overestimates the heat content of the lake (Figure 3.12(b)). Wealso see that each major storm causes a drop in the observed thermal stability, and followingthe storm the thermal stability increases at the same rate it was increasing before the storm(Figure 3.12(c)). This behaviour is similar to what we observed in the summer of 2017 (Figure3.9(c)). However, the model fails to capture this pattern. Instead, it predicts only a small dropin the lake’s thermal stability after each storm.The two largest declines in the observed thermal stability resulted from wind events 1 and3. Before the first wind event, the thermal stability of the model increases at a rate similar tothat observed in the field, although with a small consistent overestimate. During this period,the heat content predicted by the model aligns relatively closely with the field observations.This suggests that the model predicts stronger stratification than observed in the field whilemaintaining the same amount of heat throughout the water column, which could indicate astrong, cold saline hypolimnion and a warm epilimnion - consistent with our observations from583.2. Summer Simulationsthe model results so far, see Figure 3.13(e,f). Between wind event 1 and 3, and after wind event3, the discrepancy between the model and the observations increases, as both the heat contentand stability in the model becomes much higher than in the observations (Figure 3.12(b,c)).While GLM effectively models the surface and bottom temperatures, it struggles to capturethe temperature in the metalimnion and predicts a sharper metalimnion than what is observed.Similar to the model results from the summer of 2017, GLM has difficulty capturing the intensethermal variations that follow each storm.CTD CastsFigure 3.13 shows a comparison of the temperature, salinity, and density profiles from theCTD casts at P2 and P3 with the model results. As shown in the top row of panels, the modelwas initialized using the average temperature and salinity from the P2 and P3 CTD casts, withthe model’s first time step on July 5th (DOY 187) closely matching the field data from bothCTD casts.Figure 3.13: Comparison of summer 2016 model and CTD profiles. The top row demonstratesthe temperature, salinity, and density profiles from the second CTD casts at P2 ( ), P3 ( ), andthe model results ( ) on July 5th (DOY 187). The bottom row presents the same parametersfor the third CTD cast on August 11th (DOY 224); dashed lines indicate the imd (≈ 3O54 ◦C).593.2. Summer SimulationsIn the bottom row of panels, we observe that the temperature and salinity profiles from themodel exhibit a sharp interface transitioning from the warm upper layer to the cooler, moresaline bottom layer. This sharp transition in the thermocline is most obvious when examiningthe density profiles and comparing them to field observations, where we see a smoother tran-sition between these layers. This is consistent with our observations in Figure 3.11. Despitethis discrepancy, the model predictions of the temperature and salinity in the hypolimnion andepilimnion are reasonable.603.3. Spring Simulations3.3 Spring Simulations3.3.1 Spring 2017 (Day of Year 131.5 - 172.0)GLM was used to model BML for spring of 2017, from midday on May 11th (DOY 131.5) untilmidnight on June 21st (DOY 172.00), see Table 2.5. Our analysis begins by annotating thenotable wind events highlighted where W < 1, see Figure 3.14(b). These wind events have aduration of at least the internal seiche period, which is approximately 24 hours during spring.The timing of these events is:• Event 1: May 11th - May 15th (DOY 131.6 - DOY 135.5)• Event 2: May 22nd - May 26th (DOY 142.4 - DOY 146.3)• Event 3: June 9th - June 10th (DOY 160.3 - DOY 161.2)Figure 3.14: Time series for May 11th - June 20th, 2017 of (a) air temperature; and (b) Wed-derburn number of the average of P2 and P3 moorings, with major wind events highlighted ingrey. Temperature contours for the (c) P2 mooring, (d) P3 mooring, (e) the average of the P2and P3 moorings and (f) the model-simulated temperature contours. The colourbar was limitedto 15 ◦C to emphasize small temperature differences; note that any temperatures ≥ 15 ◦C areshown with the same colour (red).613.3. Spring SimulationsEvent 1 (DOY 131.6 - DOY 135.5)During Event 1, strong winds from the northeast on DOY 131 and 133 (Figure 3.4(b))alternate with weaker winds on DOY 132 and 134. The strong winds on DOY 131 and 133cause upwelling of the thermocline at P2 and downwelling at P3, see Figure 3.14(c,d), resultingin cooler water at P2 compared to P3. On DOY 132 and 134, the relaxation of the winds allowsa basin-scale internal seiche to move warm water from P3 back towards P2. As a result, theepilimnion deepens at P2 and becomes shallower at P3. This back-and-forth oscillation may beresonantly enhanced, as the basin-scale internal seiche has a period of approximately 24 hoursduring this time. It is important to note that each wind event induces some mixing betweenthe layers, causing the thermal stability of the lake to decrease to a minimum on DOY 135.5(Figure 3.15(c)). However, as shown in Figure 3.4(c,d), turnover does not occur.Between Events 1 and 2 (DOY 135.5 - DOY 142.4)After the first wind event, there are several sunny days (Figure 3.2(a)), the air temperaturebegins to rise, which is reflected in Figures 3.14(a), and we observe the thermal stratificationintensifying at both platforms (Figures 3.14(c,d)). During this period, the winds lack sufficientenergy to disrupt this stratification. As a result, the heat content and thermal stability graduallyincrease, see Figure 3.15(b,c).Event 2 (DOY 142.4 - DOY 146.3)Immediately prior to this event, the surface waters at P3 were slightly warmer (≈ 1 ◦C) thanthose at P2. However, on DOY 142.5, winds from the south intensified, causing upwelling atP3 and downwelling at P2. Within a few hours, the surface waters at P2 became significantlywarmer (≈ 3 ◦C) than at P3, and this condition persisted throughout DOY 143.On DOY 144, the wind direction reversed and the wind speed began to increase (Figure3.4(a,b)). By DOY 144 the pattern observed earlier had reversed now with upwelling at P2and downwelling at P3 with the temperature at P2 significantly warmer than at P3. Becauseof the increased wind speed, the temperature at P3 became nearly uniform with depth, rangingfrom 10O6 ◦C to 11O2 ◦C. At P2, the temperature was also nearly uniform with depth but colder,holding steady at 7O8 ◦C most of the time. It is important to note that even though the thermalstability of the lake approaches zero on DOY 144.5 (Figure 3.15(c)), the lake has not fully623.3. Spring Simulationsmixed, as the temperature at P2 is still significantly lower than at P3.Between Events 2 and 3 (DOY 146.3 - DOY 160.3)On DOY 146.3, the wind relaxes and the lake restratifies at both stations. From DOY 146.3to DOY 160.3 air temperatures rise and the surface waters of the lake gradually warm resultingin a gradual increase in heat content and a noticeable strengthening of the thermal stratificationat both platforms (Figure 3.14(c,d)). During elevated winds on DOY 147.5, the lake beginsto seiche, with the thermocline at both platforms oscillating. On DOY 153.7 and 155.8, thethermocline oscillates out of phase, indicating the usual pattern of downwelling and upwelling.Although a temporary decrease in the thermal stability of i¯ is observed during these storms,in general the thermal stability of the lake continues to rise (Figure 3.15(c)). In summary, thewinds, while strong, are not sufficient to disrupt the lake’s stability during this period.Event 3 (DOY 160.3 - DOY 161.2)With the onset of Event 3, air temperatures begin to decrease, which is reflected in theslower rate of heat content increase in i¯ . Strong winds from the northeast cause upwellingat P2, while downwelling at P3. By DOY 160.5, the temperature profiles at both P2 and P3approach uniformity, although P3 remains several degrees warmer than P2 throughout muchof the depth (Figure 3.4(c,d)).After Event 3 (DOY 161.3 - DOY 172.0)After the winds subside, the thermal structure of the lake returns to nearly the same con-dition as before the storm, with little apparent mixing of the water column. The lake remainsstrongly stratified, with a gradual increase in the heat content over time (Figure 3.15(b)). Oc-casional wind-driven seiching occurs throughout the remainder of the record (e.g., on DOY165); however, these events do not lead to permanent changes in the thermal structure.633.3. Spring SimulationsFigure 3.15: Comparison of spring 2017 (May 11th - June 20th) model and field WedderburnNumber, HC, and SSI. (a) Wedderburn number of the average of P2 and P3 moorings, withperiods when W < 1 for most, if not all, of the duration of the spring internal seiche period (≈24 hours) highlighted in grey. (b) HC and (c) SSI time series. The average of the P2 and P3moorings ( ) is compared to the model ( ).GLM SimulationThe average of the observed temperature at P2 and P3, i¯ , is shown in Figure 3.14(e). Dif-ferences between the thermal structure at P2 and P3 are largely due to upwelling, downwelling,and internal seiching. These 2-D effects are smoothed by averaging P2 and P3 temperatures,reflecting the effects of the storms as a deepening of the thermocline, see Figure 3.14(e). Forexample, the two upwelling events on DOY 144.5 and DOY 160.5 give rise a to nearly uniformvertical temperature in i¯ . On the other hand, the cold bottom temperature at P3 during thethird wind event on DOY 160.5, is mirrored in i¯ , as seen in Figure 3.4(c,d), where the bottomtemperature in the model remains cold.Figure 3.14(f) presents the model results. Comparison of this with the observed temperature,i¯ , in Figure 3.14(e) shows that the model captures the overall evolution of the thermal structureof the lake during spring 2017. In the model the effects of the three major events occur as a643.3. Spring Simulationsdeepening of the thermocline. The model predictions for Wedderburn number, heat contentand thermal stability are compared to those from the observations in Figure 3.15. The modelslightly overestimates the values of the Wedderburn number, though it still follows the overalltrends in the observed values Figure 3.15(a).While the model generally tracks the observed heat content well (Figure 3.15(b)), it sig-nificantly underestimates the thermal stability (Figure 3.15(c)). This suggests that the modelmay be capturing the same amount of heat as observed in the field, but it likely distributes theheat differently throughout the lake. In contrast, in the field, most of the heat appears to bestored near the top of the water column. This discrepancy is further supported by the model’sunderprediction of thermal stability, which indicates that the model is distributing heat moreevenly throughout the column, resulting in weaker stratification. This is consistent with themodel temperature contours observed in Figure 3.14(f).During Event 1, the model showed sudden mixing in the top 8m of the lake, with cold, salinewater remaining at the bottom until the middle of Event 2 (Figure 3.14(f)). It is only duringwind event 2 that the strong winds disrupt most of the the hypolimnion, and the model predictsspring turnover, with its thermal stability reaching zero on DOY 147.5 (Figure 3.15(c)). Themodel also captures the cooler bottom waters during this event, as seen in comparison with i¯in Figure 3.14(e).After Event 2, as air temperatures continue to rise, the epilimnion in the model heats upin a manner similar to the field observations. However, the model struggles to replicate thesmooth transition from the epilimnion to the thermocline and hypolimnion, as seen in the fielddata. Instead, the model predicts a sharp interface between these layers. This abrupt transitionis also evident in the model’s temperature and salinity profiles (Figure 3.16) and persists untilthe end of the simulation period.During Event 3, the model fails to capture the partial turnover observed in the field andinstead predicts a relatively thick epilimnion compared to what is observed in i¯ . This discrep-ancy is also evident in the thermal stability, where the strong winds of event 3 that caused asignificant drop in the observed thermal stability of i¯ resulted in only a slight reduction in thethermal stability in the model (Figure 3.15(c)).653.3. Spring SimulationsCTD CastsFigure 3.16 presents a comparison of the temperature, salinity, and density profiles from theCTD casts at P2 and P3 with the model results. The top row of panels corresponds to the firstCTD survey on May 11th (DOY 131), the average of which was used to initialize the model,see Table 2.5. Also shown in these panels is the model output following the first time step onMay 11th, which closely matches the average of both CTD casts.Figure 3.16: Comparison of spring 2017 model and CTD profiles. The top row demonstratesthe temperature, salinity, and density profiles from the first CTD casts at P2 ( ), P3 ( ), andthe model results ( ) on May 11th (DOY 131). The bottom row presents the same parametersfor the second CTD cast on June 20th (DOY 171); dashed lines indicate the imd (≈ 3O54 ◦C).The bottom row of panels show the CTD profiles and model output at the end of the modelrun on June 20th (DOY 171). The model’s salinity profile on DOY 171 closely matches theaverage of the two CTD casts. However, the model’s temperature profile for this day shows asharper metalimnion interface and a thicker epilimnion compared to the field observations, sim-ilar to the temperature contours observed following wind event 2 (Figure 3.14(f)). Additionally,the model more closely matches the epilimnion temperatures at P3 and the hypolimnion tem-peratures at P2. On the other hand, the model overestimates the salinity in the hypolimnion,663.3. Spring Simulationswhich may be due to the salinity of the porewater from the underlying tailings, for which wemay lack sufficient data to accurately characterize in the model.673.3. Spring Simulations3.3.2 Spring 2016 (Day of Year 124.625 - 188.0)We identified six wind events during the spring of 2016 where W < 1 for a duration of at leastthe internal seiche period (≈ 24 hours during spring), see Figure 3.17(b). The timing of theseevents is:• Event 1: May 4th - May 5th (DOY 125.6 - 126.9)• Event 2: May 8th - May 10th (DOY 129.5 - DOY 131)• Event 3: May 18th - May 23rd (DOY 139.9 - DOY 144.9)• Event 4: May 25th - May 27th (DOY 146.6 - DOY 148.1)• Event 5: June 9th - June 10th (DOY 161.1 - DOY 162.1)• Event 6: June 15th - June 21st (DOY 167.2 - DOY 173.1)Figure 3.17: Time series for May 3rd - July 5th, 2016 of (a) air temperature; and (b) Wedderburnnumber of the average of P2 and P3 moorings, with major wind events highlighted in grey.Temperature contours for the (c) P2 mooring, (d) P3 mooring, (e) the average of the P2 andP3 moorings and (f) the model-simulated temperature contours. The colourbar was limitedto 15 ◦C to emphasize small temperature differences; note that any temperatures ≥ 15 ◦C areshown with the same colour (red).683.3. Spring SimulationsEvent 1 (DOY 125.6 - 126.9)After ice-off on April 27th (DOY 118), the lake experiences an extreme heat event on DOY124 and 125. This is followed by the first wind event of spring, with strong westerlies. Theeffects of these are clearly reflected in the temperature contours, see Figure 3.17(c,d). At P2,the temperature contours show a warm epilimnion above a thick, cold hypolimnion, while atP3, a relatively cooler and deeper epilimnion appears. These cold waters observed after ice-offare also reflected in the heat content of i¯ , which begins with a very low value and increasesabruptly with the start of this warm wind event, see Figure 3.18(b).Between Events 1 and 2 (DOY 126.9 - DOY 129.5)Following the first strong wind event, the heat content of the lake continues to rise (Figure3.18(b)), driven by record-breaking high air temperatures which lead to wildfires during thisperiod. Increasing heat content is most clearly observed in the temperature contours of P3,where the epilimnion at P3 warms.Event 2 (DOY 129.5 - DOY 131)At the onset of the second wind event, strong winds from the west (Figure 3.3(b)) pushwarm water toward P2 and upwell cold water at P3. During this period, the water at P2 isrelatively warm (≈ 7 ◦C) and vertically uniform down to the bottom sensor at 8m, with thewater column remaining homogeneous for roughly 19 hours. In contrast, the water at P3 iscooler (≈ 5 ◦C). While the water column at P3 appears relatively homogeneous down to 9m inFigure 3.17(d), weak stratification persists at the deeper sensors, as shown in Figure 3.3(c,d).As shown in Figure 3.18(c), the thermal stability of i¯ drops to zero.Between Events 2 and 3 (DOY 131 - DOY 139.9)After the second wind event, on DOY 131 the relaxing of the winds allows a basin-scaleinternal seiche to move warm water back towards P3 from P2. As a result, the epilimniondeepens at P3 and becomes shallower at P2. As air temperatures rise, the lake continues tostratify at both platforms, leading to a gradual increase in both the thermal stability and heatcontent of the lake (Figure 3.18(b,c)).693.3. Spring SimulationsEvent 3 (DOY 139.9 - DOY 144.9)At the onset of wind event 3, a dramatic drop in air temperature, coupled with strongwinds from the north, causes significant changes in the pre-established stratification at bothplatforms. During this period, the deeper waters at P3 warm up by approximately 4 ◦C and theentire profile becomes homogeneous at ≈ 10 ◦C for approximately 91 hours (3.8 days), whilesurface waters at P2 cool down by approximately 4 ◦C and a weak stratification persists at P2(Figure 3.3(c,d)). As a result in the thermal stability of i¯ almost goes to zero (Figure 3.18(c)).During this period, the heat content of i¯ is relatively unchanged (Figure 3.18(b)).On DOY 142, there is a brief wind reversal to southerly winds which causes a slight (≈ 1 ◦C)warming of the epilimnion at P2 while the temperature at P3 remain relatively unchanged.Between Events 3 and 4 (DOY 144.9 - DOY 146.6)Following wind event 3, the air temperature begins to rise again, but at a slower rate thanbefore, and the lake begins to restratify at both platforms.Event 4 (DOY 146.6 - DOY 148.1)Wind event 4, beginning on DOY 146.6, disrupts the lake’s stratification once again, causingcool water to upwell at P2, while warmer waters are downwelled at P3. This event leads toa 20% decrease in the lake’s thermal stability (Figure 3.18(c)). The water column at bothplatforms partially mixes (Figure 3.3(c,d)), and the thermocline deepens at both platforms.Between Events 4 and 5 (DOY 148.1 - DOY 162.1)As air temperatures continue to rise, stratification strengthens at both platforms, and thelake’s thermal stability increases (Figure 3.18(b)). Smaller wind events lack the energy requiredto fully mix the lake and instead lead to further deepening of the thermocline. Although theseevents cause temporary declines in thermal stability, the gradual increase in heat content allowsthe lake to recover, resulting in an overall increase in thermal stability over the period (Figure3.18(c)).Event 5 (DOY 161.1 - DOY 162.1)During this period, a slight drop in air temperature combined with increased wind leadsto the upwelling of cooler water at P2 and the downwelling of warmer water at P3, while the703.3. Spring Simulationscolder bottom waters at P3 remain largely unaffected (Figure 3.3(d)). This event results in a60% decrease in the thermal stability of the lake (Figure 3.18(c)).Between Events 5 and 6 (DOY 162.1 - DOY 167.2)Following wind event 5, the lake’s thermal stability increases and peaks by DOY 162.9,recovering 88% of the stability lost during the event within 19 hours. When the next moderatewinds occur on DOY 165, it causes another drop in thermal stability, during which warm wateris fully downwelled at P2 and cooler water is partially upwelled at P3. It is important to notethat, while the temperatures at P2 become homogeneous during this period, a relatively strongstratification remains at P3, and the lake’s overall thermal stability does not reach zero. As aresult, spring turnover does not occur.Event 6 (DOY 167.2 - DOY 173.1)As the air temperatures begin to rise, water temperature and stratification increased atboth platforms. The strong storm on DOY 167, lasting approximately 6 days, has a dramaticeffect on the thermal stability of i¯ , causing it to nearly reach zero. During this period, thetemperature at both P2 and P3 warms up and nearly mix vertically, while the deeper and salinewaters (9m to 10m) remain slightly cooler than the rest of the water column (Figure 3.3(c,d)).After Event 6 (DOY 173.1 - DOY 188)Following wind event 6, the lake recovers the lost thermal stability, and the thermal stabilitybegins to increase at the same rate as previously observed. The heat content of the lakecontinues to rise, and a relatively constant thermal stratification is observed at both platformsas the winds calm down, with no further strong forces disrupting the lake’s thermal structure.713.3. Spring SimulationsFigure 3.18: Comparison of spring 2016 (May 3rd - July 5th) model and field WedderburnNumber, HC, and SSI. (a) Wedderburn number of the average of P2 and P3 moorings, withperiods when W < 1 for most, if not all, of the duration of the spring internal seiche period (≈24 hours) highlighted in grey. (b) HC and (c) SSI time series. The average of the P2 and P3moorings ( ) is compared to the model ( ).GLM SimulationSimilar to our previous observations from the spring and summer of 2016 and 2017, Figure3.17(e) shows that the temperature contours of i¯ smooth out 2-D processes, reflecting theeffects of the storms as a deepening of the thermocline and presenting a dampened responseto the processes happening at both platforms. Notably, the turnover on DOY 140 (wind event3) is reflected in i¯ , where uniform temperatures are observed. Additionally, the cold bottomtemperatures at P3 during wind event 6, on DOY 172, are mirrored in i¯ where the bottomtemperature of i¯ remains cold, consistent with what was observed at P3.Figure 3.17(f) presents the model results, where we observe that the model fully mixes thewater column at the onset of the first wind event. Upon examining the model’s predictionof the Wedderburn number, we see it is much lower than the field observations for the firsthalf of the model period, suggesting the model is predicting upwelling (Figure 3.18(a)). This723.3. Spring Simulationsbecomes even more apparent in Figure 3.18(c), where the model’s thermal stability drops tozero, indicating spring turnover at this time. This spring turnover lasts for approximately 11.5days, which is much longer than what we observed in i¯ . During this period, the heat content ofthe lake predicted by the model increases at a rate similar to that of i¯ . This heat accumulationis evident in the model temperature contours, where, as the water column warms, a warmepilimnion begins to form on DOY 137.5, and the thermal stability of the model increasesslightly (Figure 3.18(c)). During wind event 3 the model once again fully mixes the watercolumn, predicting spring turnover for the second time on DOY 140. This closely matches thepartial spring turnover observed in the field, see Figure 3.17(e,f).After wind event 3 subsides and as the air temperature begins to rise, the lake starts toweakly stratify, and the thermal stability of the model increases, although at a slower ratecompared to the field observations (Figure 3.18(c)). The model predicts the effects of windevents 4 and 5 as a deepening of the thermocline, which is similar to the temperature contoursobserved in i¯ . However, the model struggles to replicate the smooth transition from theepilimnion to the hypolimnion observed in the field data, instead predicting a sharp interfacebetween these layers. This abrupt transition is also evident in the model’s temperature andsalinity profiles (Figure 3.19) and persists until the end of the simulation period. Moreover, itis worth noting that the model underestimates the temperatures in the epilimnion in the periodbetween events 4 and 5.During wind event 5, the dramatic drop in the thermal stability of i¯ is not fully capturedby the model, which predicts a less pronounced decrease in stability. Furthermore, the modelmaintains the cold and saline hypolimnion throughout this period, up until the onset of windevent 6. In contrast, the cooler and saline hypolimnion of i¯ is disrupted at the start of windevent 5. Interestingly, the model follows the observed heat content quite well during this time.This discrepancy suggests that while the model has captured the same amount of heat as thefield, it distributes the heat differently throughout the water column.Between wind events 5 and 6, weak winds cause upwelling at P3, leading to a drop in thethermal stability of i¯ , which then recovers immediately. However the model predicts a steadyincrease in thermal stability during this time.During wind event 6, the model’s thermal stability decreases in a manner similar to field733.3. Spring Simulationsobservations in i¯ , almost reaching zero on DOY 172.25. Following this, the model’s thermalstability begins to increase again at a rate similar to that in the field observations. However,the model underestimates the magnitude of the observed thermal stability and heat content(Figure 3.18(b,c)). During this period, the model predicts a thinner hypolimnion compared tothe field observations (Figure 3.17(f)).As air temperatures begin to decrease on DOY 186, the thermal stability of i¯ decreases.Although the model does not capture this decline in stability, the simulation concludes withstability values that are consistent with the field observations.CTD CastsFigure 3.19 presents a comparison of the temperature, salinity, and density profiles fromthe CTD casts at P2 and P3 alongside the model results. As shown in the top row of panels,the model was initialized using the average temperature and salinity from the P2 and P3 CTDcasts.Figure 3.19: Comparison of spring 2016 model and CTD profiles. The top row demonstratesthe temperature, salinity, and density profiles from the first CTD casts at P2 ( ), P3 ( ), andthe model results ( ) on May 3rd (DOY 124). The bottom row presents the same parametersfor the second CTD cast on July 5th (DOY 187); dashed lines indicate the imd (≈ 3O54 ◦C).743.3. Spring SimulationsIn the bottom row of panels, the model’s salinity profile on July 5th (DOY 187) closelymatches the average of the two CTD casts. However, the model predicts a fresher hypolimnioncompared to the field observations. As discussed previously, this discrepancy may be attributedto inaccuracies in vertical mixing or uncertainties in the salinity of the porewater from theunderlying tailings. It is also important to note that the sharp increase in salinity observed inthe deeper waters at P3 on DOY 187 is due to the instrument approaching the tailings duringthe CTD cast, where the salinity is 4 ppt.75Chapter 4DiscussionIn the previous chapter, we examined the lake’s response, focusing specifically on observationsfrom platforms P2 and P3, during significant wind events in the spring and summer of 2016 and2017. Additionally, we compared field observations from each platform, including the spatiallyaveraged temperature of both platforms, i¯ , against the model simulation results.In this chapter, we first introduce a simplified conceptual model of the response of the ther-mal structure to wind, inspired by the framework of Spigel and Imberger, 1980, the laboratoryexperiments of Monismith, 1986, and the field observations from BML and other locations. Wethen apply this conceptual model to analyze the two most intense wind events that occurredduring the spring seasons of 2016 and 2017. This analysis provides insight into the springturnover dynamics observed at BML. Following this, we will revisit the results of our fourcase studies from spring and summer of 2016 and 2017 to assess how model accuracy variesseasonally and compares to i¯ .4.1 Conceptual ModelTo better interpret BML’s response to wind events and clarify the underlying physical processes,a modified conceptual model was developed (Figure 4.1). This conceptual model includes fivedistinct Regimes:Regime I This Regime is referred to as Thermocline tilting. In this regime, the thermo-cline slightly slopes downward towards the downwind end of the lake; however,no water from the metalimnion or hypolimnion upwells to the lake surface, seeFigure 4.1(a). This scenario corresponds to the Regime 4 of Spigel and Imberger,1980.764.1. Conceptual ModelRegime II This Regime is referred to asMetalimnetic upwelling. In this regime the met-alimnion broadens towards the upwind end of the lake with some metalimneticwater reaching the surface. At the downwind end of the lake the thermocline tiltsslightly downward, but does not reach the bottom of the lake, see Figure 4.1(b).This Regime corresponds to the upwelling flows observed in the laboratory ex-periments of Monismith, 1986 and in the field observations of Mortimer, 1954and Robb et al., 2025. Regime II roughly corresponds to Regime 3 of Spigel andImberger, 1980, although their analysis focused on a two-layer thermal structureand did not explicitly account for the presence of a metalimnion.Regime III This Regime is referred to as Upwelling/Downwelling. In this Regime down-welling as well as upwelling occurs, see Figure 4.1(c). This Regime combineselements of Regimes 2 and 3 of Spigel and Imberger, 1980.Regime IV This Regime is referred to as Partial turnover. Regime IV occurs when strongwinds persist long enough to vertically homogenize the water column at indi-vidual locations, while horizontal temperature gradients across the lake remain,see Figure 4.1(d). For instance, at BML, the water column is vertically uniformat both P2 and P3, yet substantial temperature differences can persist betweenthese stations. Regime IV corresponds to Regime 2 described by Spigel andImberger, 1980.Regime V This Regime is referred as Complete turnover. Regime V occurs when per-sistent strong winds fully mix the water column, resulting in the lake becominghomogeneous both vertically and horizontally, see Figure 4.1(e). This Regimewas not observed at BML in either the spring of 2016 or 2017. Regime V corre-sponds with Regime 1 of Spigel and Imberger, 1980.Partial and complete spring turnovers are critical periods for lake ventilation, as they facil-itate the replenishment of dissolved oxygen (DO) throughout the water column, particularly tothe deepest layers. This oxygen renewal is essential for maintaining healthy habitats for aquaticorganisms. Additionally, these turnover events redistribute nutrients throughout the lake, pro-774.1. Conceptual Modelmoting biological productivity and significantly influencing ecosystem health, fish habitats, andoverall lake biogeochemistry for the following stratified season.A summary of lake responses to individual wind events during the spring and summer of2016 and 2017, based on the conceptual model presented above, is provided in Table 4.1.Table 4.1: Classification of the response of BML to wind events.Some wind events resulted inmultiple responses within the lake, indicated using subscripts (i.e., ×1, ×2, and ×3) representingthe chronological order in which each response occurred.Period WindEventRegime I Regime II Regime III Regime IV Regime VSW NE SW NE SW NE SW NESpring20161 ×2 ×1L3 ×23 ×1 ×2 ×4 ×34 ×5 ×6 ×Spring20171 ×2 ×12 ×1 ×2L4 ×33 ×Summer20161 ×2 ×3 ×Summer20171 ×1 ×22 ×1 ×23 ×4 ×784.1. Conceptual ModelFigure 4.1: Classification of thermal structure responses to wind forcing. (a) Thermoclinetilting - corresponding to Spigel and Imberger, 1980 Regime 4, (b) Metalimnetic upwelling, notexplicitly mentioned in Spigel and Imberger, 1980, (c) Upwelling/Downwelling - correspondsroughly with Spigel and Imberger, 1980 Regime 3, (d) Partial turnover - corresponds roughlywith Spigel and Imberger, 1980 Regime 2, (e) Complete turnover - corresponding to Spigel andImberger, 1980 Regime 1.794.2. Spring Turnover in Base Mine Lake Demonstration4.2 Spring Turnover in Base Mine Lake Demonstration4.2.1 Spring Turnover in 2016Among the six significant wind events recorded during the spring of 2016, only the third ex-hibited conditions approaching a spring turnover in the lake. As such, here we will focus onanalyzing the thermal dynamics of BML during that particular storm. Using the conceptualmodel described in Section 4.1, we examine the lake’s physical response at different stages ofthis wind event.Figure 4.2(a,b) presents temperature contours at P2 and P3 during wind event 3, with thecorresponding temperature profiles during key stages of the wind event shown in panels 4.2(c-f).At the onset of the event, on DOY 139.9, the water column at P2 is noticeably warmer thanat P3, with epilimnetic temperatures of 12O7 ◦C and 10O6 ◦C, respectively. Additionally, thethermocline at P2 is deeper than that at P3 (Figure 4.2(c)). These conditions are consistentwith prior strong southwesterly winds that induced a metalimnetic upwelling, characteristic ofRegime II in Figure 4.1(b).Approximately 4.8 hours later, on DOY 140.1, the wind direction shifted to northerly,producing an opposite response in the lake. At this point, the water at P3 became noticeablywarmer than at P2, and the thermocline at P3 deepened (Figure 4.2(d)). This pattern reflectsa reversal in metalimnetic upwelling, consistent with the response for Regime II depicted inFigure 4.1(b).Approximately 14 hours later, on DOY 140.7, the persistent northerly winds led to a furthershift in lake conditions. At this time, the water columns at both P2 and P3 became nearlyhomogeneous, with vertical temperature differences of only 0O7 ◦C and 0O3 ◦C throughout theentire depth, respectively (Figure 4.2(e)). Notably, the overall temperature at P3 was higherthan at P2, with a difference of 2O3 ◦C. This thermal structure suggests the occurrence of apartial turnover, consistent with the characteristics of Regime IV illustrated in Figure 4.1(d).Approximately 67 hours (2.8 days) later, on DOY 143.5, temperatures at P3 are completelyhomogeneous. At P2 the epilimnion warms up by 1O8 ◦C and while the hypolimnetic tempera-tures remain relatively the same and a clear two-layer stratification is observed (Figure 4.2(f)).This pattern reflects a complete downwelling at P3 and a partial upwelling at P2, consistent804.2. Spring Turnover in Base Mine Lake DemonstrationFigure 4.2: Spring 2016 field temperature measurements during wind event 3. Temperaturecontours from (a) P2 mooring and (b) P3 mooring with dashed lines indicating four key times.Temperature profiles from both P2 ( ) and P3 ( ) at (c) DOY 139.9 - Regime II, (d) 140.1 -Regime II, (e) 140.7 - Regime IV, and (f) 143.5 - Regime III.814.2. Spring Turnover in Base Mine Lake Demonstrationwith Regime III illustrated in Figure 4.1(c).As shown in Figure 4.2(a,b), the water column at P3 remains relatively homogeneous forapproximately four days, from around DOY 140.4 to 144.3. In contrast, at P2, the water columnis homogenized for only about two hours before re-stratifying shortly after DOY 140.7. Thisindicates that partial spring turnover (Figure 4.1(d)) only lasted only two hours in 2016.824.2. Spring Turnover in Base Mine Lake Demonstration4.2.2 Spring Turnover in 2017Among the three significant wind events recorded during the spring of 2017, only wind event 2exhibited conditions approaching a spring turnover in the lake. As such, here we will focus onanalyzing the thermal dynamics of BML during that particular storm.Figure 4.3(a,b) presents temperature contours at P2 and P3 during wind event 2, with thecorresponding temperature profiles during key stages of the wind event shown in panels 4.3(c-f).At the onset of the event, on DOY 142.4, the water column at P2 is warmer than at P3, withepilimnetic temperatures of 12O3 ◦C and 11O6 ◦C, respectively. Additionally, the thermocline atP2 is deeper than that at P3 (Figure 4.3(c)). These conditions are consistent with the strongsoutherly winds that induced a metalimnetic upwelling, characteristic of Regime II in Figure4.1(b).Approximately 50 hours later, on DOY 144.5, northerly winds drive the opposite response.At this time, the water column at P3 was nearly homogeneous, with temperatures rangingfrom 10O3 ◦C to 10O7 ◦C, showing only a 0O4 ◦C variation through the depth (Figure 4.3(d)).Meanwhile, at P2, the temperature was noticeably cooler ranging from 5O4 ◦C to 8O5 ◦C. Thisthermal pattern indicates a complete downwelling of warm surface waters at P3 and a partialupwelling of cooler waters at P2, consistent with Regime III illustrated in Figure 4.1(c).Approximately 2.4 hours later, on DOY 144.6, the persistent northerly winds led to afurther shift in lake conditions. At this time, the epilimnion at P2 cools down by 0O8 ◦C whilethe hypolimnion warms up by 1O4 ◦C, resulting in a relatively homogeneous water column withonly a 1 ◦C temperature difference throughout the depth (Figure 4.3(e)). At P3, the watercolumn remains homogeneous. These observations suggest the lake has undergone partial springturnover, consistent with the Regime IV illustrated in Figure 4.1(d).Approximately 34 hours later, on DOY 146.0, the lake undergoes another thermal transition.At P2, the water column is stratified, with temperatures ranging from 10O6 ◦C near the surfaceto 6O2 ◦C at depth (Figure 4.3(f)). In contrast, the water column at P3 remains well mixedand uniform at 11O3 ◦C. This thermal pattern reflects a complete downwelling of warm surfacewaters at P3 and a partial upwelling of cooler waters at P2, again consistent with the upwellingclassification scenario of Regime III illustrated in Figure 4.1(c).834.2. Spring Turnover in Base Mine Lake DemonstrationFigure 4.3: Spring 2017 field temperature measurements during wind event 2. Temperaturecontours from (a) P2 mooring and (b) P3 mooring with dashed lines indicating four key times.Temperature profiles from both P2 ( ) and P3 ( ) at (c) DOY 142.4 - Regime II, (d) 144.5 -Regime III, (e) 144.6 - Regime IV, and (f) 146.0 - Regime III.844.2. Spring Turnover in Base Mine Lake DemonstrationIt is evident from Figure 4.3(a,b) that the water column at P3 remains relatively homoge-neous for approximately two days. In contrast, stratification at P2 re-establishes shortly afterDOY 144.6, indicating that partial spring turnover (Figure 4.1(d)) persisted for only a briefperiod, of approximately two hours, during 2017.854.2. Spring Turnover in Base Mine Lake Demonstration4.2.3 Implications for Dissolved OxygenSpring turnover is a critical period for lake ventilation, as it facilitates the vertical redistributionof DO and nutrients, which directly affects water quality and ecosystem health. Therefore, theability to accurately model the timing and intensity of spring turnover events is essential forreliable long-term water quality predictions.Based on the observational data from 2016 and 2017, complete spring turnover was not ob-served in either year. Most wind events during the spring led to metalimnetic upwelling (RegimeII) and upwelling/downwelling (Regime III). In addition wind event 3 in spring 2016, and windevent 2 in spring of 2017, resulted in partial spring turnover (Regime IV) that lasted only afew hours in both years. The wind-driven dynamics of Regime III and IV likely contributed toincreases in DO in the hypolimnion.During the summer, BML was predominantly influenced by metalimnetic upwelling (RegimeII), with the notable exception of the wind event from DOY 172.1 - 173.8 in 2017. This eventcaused complete downwelling at P3 and partial upwelling at P2 (Regime III), see Figure 3.4(c,d).As a result, DO levels at 6m depth increased at P1 (Figure 3.4(e)).864.3. Model Performance4.3 Model PerformanceAssessing the model’s performance across all four periods - spring and summer of 2016 and 2017- is essential for understanding its reliability in capturing the lake’s seasonal thermal dynamicsunder varying wind events. These evaluations not only help validate the model’s ability tosimulate key physical processes such as spring turnover, but also provide critical insight into itsapplicability for long-term water quality forecasting and management. Accurate representationof mixing events and turnover timing is particularly important for modelling DO dynamics, asthese physical processes directly influence oxygen distribution throughout the water column.Misrepresenting them can lead to significant errors in predicting oxygen depletion, nutrientcycling, and the overall ecological health of the lake.In BML, competing physical processes such as the settling of the underlying tailings whichtriggers the expression of saline porewater and deepens the lake, coupled with the formation ofhydrocarbon sheens on the surface made modelling uniquely complex and challenging. Thus,evaluating the model’s performance across multiple years helps to reveal both the strengths andlimitations of the 1-D modelling framework.The performance of GLM was evaluated by calculating the root mean square error (RMSE)values between i¯ and simulated temperatures, iGLM . Figure 4.4 presents the 12-hour movingaverage RMSEs for all four periods. The average RMSEs for each season were approximately1 ◦C. During each seasons there are fluctuations of the RMSE of temperature. These fluc-tuations are typically larger and more rapid during wind events, presumably because of therapid changes in the observed thermal structure (i¯ ). Under these circumstances the RMSE oftemperature is not necessarily a good measure of the accuracy of GLM, but may be a resultof i¯ not accurately representing the horizontally averaged temperature in the lake, since itis calculated from measurements at just two locations. Between wind events the temperatureRMSE generally increases gradually or remains relatively constant. The fluctuations in theRMSE of temperatures were generally less than during wind storms.874.3. Model PerformanceFigure 4.4: RMSE between i¯ and iGLM , during spring and summer of 2016 and 2017 withaverage RMSE for the entirety of each record shown with dashed lines. Periods of major windevents are highlighted in grey.Similarly to the RMSE of temperature, the RMSE of the heat content and the RMSE ofthe thermal stability were approximately the same between seasons and years, see Table 4.2.Table 4.2: RMSE between i¯ and the iGLM for heat content and thermal stability.Year Season Temperature Heat Content Thermal StabilityRMSE (◦C) RMSE (MJm−2) RMSE (Jm−2)2016 Spring 0.97 10.3 12.92Summer 0.91 7.76 15.662017 Spring 1.12 9.1 11.64Summer 1.31 6.62 11.97Overall, the model demonstrated reasonable performance in simulating the general ther-mal structure and the timing of complete downwelling events. However, it often representedwind-driven responses as a gradual deepening of the thermocline rather than discrete mixingevents. While i¯ offered a helpful baseline for assessing model predictions during wind events, itrepresents a spatially averaged condition and does not reflect the unique dynamics at the lake884.3. Model Performancecenter, where 2-D processes such as internal seiches and localized upwelling/downwelling areless pronounced. Given that GLM is a 1-D model, its results are most appropriately comparedwith field measurements taken at the lake’s center.89Chapter 5Conclusions5.1 SummaryField data from Base Mine Lake Demonstration (BML), and the results of numerical simulationsperformed using the General Lake Model (GLM), were analyzed for the spring and summerseasons of 2016 and 2017. A conceptual model, based on that of Spigel and Imberger, 1980,was developed to characterize the lake’s variable thermal response during wind-driven events(Figure 4.1). This model provides a valuable framework for identifying and characterizing theseresponses.Complete spring turnover did not occur in BML in either year, instead, the lake underwentpartial spring turnover, which lasted only approximately two hours in both 2016 and 2017(Regime IV), see Figure 4.1(d). The spring thermal dynamics of BML during wind events wereprimarily characterized by complete downwelling of epilimnetic surface waters at the downwindend of the lake, and metalimnetic upwelling at the upwind end of the lake (Regime III), seeFigure 4.1(c). These wind-driven events are particularly important since they provide an op-portunity for deep oxygen renewal between ice-off and summer stratification. In contrast, insummer the response of the lake to wind events was dominated by metalimnetic upwelling withminimal downwelling and minimal deep oxygen replenishment (Regime II), see Figure 4.1(b).The performance of GLM was evaluated by comparing model output to the horizontallyaveraged temperature profile, i¯ , derived from moorings at the north-east and south-west endof the lake (Figure 1.4). The model demonstrated reasonable performance in reproducingthe general thermal structure of the lake. However, given the one-dimensionality of GLM, themodel results cannot be relied upon during intense wind events to accurately reflect horizontallyaveraged lake conditions or represent the conditions at the lake center. This highlights the905.2. Future Workbenefits of additional moored instruments across the lake to capture spatial heterogeneity andimprove model calibration and validation.It is also important to note that i¯ alone cannot distinguish between partial turnover (RegimeIV) and complete turnover (Regime V), see Figure 4.1(d,e). While i¯ may suggest that completeturnover has occurred, it can mask spatial variability, such as cases where only one end of thelake has mixed. This distinction is critical, as only complete turnover results in full lake-wideventilation. Recognizing the difference between partial and complete turnover is essential foraccurately interpreting seasonal oxygen dynamics and making informed assessments of waterquality.5.2 Future WorkThis research opens the door to several opportunities for future investigation. First, a morecomprehensive understanding of BML’s thermal dynamics could be achieved by extending theanalysis to additional years. Additionally, comparing the lake’s response to wind events duringspring to wind events in the fall, when complete turnover occurs, would offer valuable insightsinto seasonal differences in mixing dynamics.Deploying additional moorings, together with intense CTD surveys, would provide criticalspatial data to assess how well the horizontally averaged profile, i¯ , represents true lake-wideconditions. This would also help validate model performance in capturing lake dynamics duringkey mixing events.Finally, implementing a 3-D hydrodynamic model for the spring and summer seasons couldoffer valuable insights into lateral variability in thermal structure, especially with the probableimportance of Coriolis effects (Hurley, 2017). A 3-D model should be able to distinguish betweenpartial and complete turnover, which is not possible using a 1-D approach.91BibliographyAdrian, R., O’Reilly, C. M., Zagarese, H., Baines, S. B., Hessen, D. O., Keller, W., Livingstone,D. M., Sommaruga, R., Straile, D., Van Donk, E., Weyhenmeyer, G. A., & Winder,M. (2009). Lakes as sentinels of climate change. Limnology and Oceanography, 54(6),2283–2297. https://doi.org/10.4319/lo.2009.54.6_part_2.2283Ayala, A. I., Moras, S., & Pierson, D. C. (2020). Simulations of future changes in thermalstructure of lake erken: Proof of concept for ISIMIP2b lake sector local simulationstrategy. Hydrology and Earth System Sciences, 24(6), 3311–3330. https://doi.org/10.5194/hess-24-3311-2020Bird, R. E. (1984). A simple, solar spectral model for direct-normal and diffuse horizontalirradiance. Solar Energy, 32(4), 461–471. https://doi.org/10.1016/0038-092X(84)90260-3Briegleb, B. P., Minnis, P., Ramanathan, V., & Harrison, E. (1986). Comparison of regionalclear-sky albedos inferred from satellite observations and model computations. https://doi.org/10.1175/1520-0450(1986)025<0214:CORCSA>2.0.CO;2CEMA. (2012). End pit lakes guidance. Cumulative Environmental Management Association.Retrieved April 6, 2025, from https://www.cclmportal .ca/resource/end- pit- lakes-guidance-document-2012Chang, S. (2020). Heat budget for an oil sands pit lake [Doctoral dissertation, University ofBritish Columbia]. https://doi.org/10.14288/1.0392976Clark, M. G., Drewitt, G. B., & Carey, S. K. (2021). Energy and carbon fluxes from an oil sandspit lake. 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Limnology andOceanography Letters, 6(5), 253–261. https://doi.org/10.1002/lol2.1020198Appendix AExample of GLM Namelist FileGLM spring 2016 namelist File1 ! general model setup2 !-------------------------------------------------------------------------------3 !4 ! sim_name [string] title of simulation5 ! max_layers [integer] maximum number of layers6 ! min_layer_vol [real] minimum layer volume (m3 * 1000)7 ! min_layer_thick [real] minimum layer thickness (m)8 ! max_layer_thick [real] maximum layer thickness (m)9 ! Kw [real] background light attenuation (m**-1)10 ! coef_inf_entrain [real] entrainment coefficient for inflows11 ! coef_mix_conv [real] mixing efficiency - convective overturn12 ! coef_wind_stir [real] mixing efficiency - wind stirring13 ! coef_mix_turb [real] mixing efficiency - unsteady turbulence effects14 ! coef_mix_shear [real] mixing efficiency - shear production15 ! coef_mix_KH [real] mixing efficiency - hypolimnetic Kelvin-Helmholtzturbulent billows16 ! coef_mix_hyp [real] mixing efficiency - hypolimnetic turbulence17 !-------------------------------------------------------------------------------18 !&glm_setup19 ! sim_name = 'GLM Simulation '20 ! max_layers = 50021 ! min_layer_vol = 0.02522 ! min_layer_thick = 0.1523 ! max_layer_thick = 0.3024 ! !-- Light Parameters25 ! Kw = 0.826 ! !-- Mixing Parameters99Appendix A. Example of GLM Namelist File27 ! coef_mix_conv = 0.12528 ! coef_wind_stir = 0.2329 ! coef_mix_shear = 0.230 ! coef_mix_turb = 0.5131 ! coef_mix_KH = 0.332 ! coef_mix_hyp = 0.533 ! Benthic_Imin = 0.134 ! mobility_off = .true.35 ! non_avg = .true.36 ! deep_mixing = .true.37 !/38 &glm_setup39 sim_name = 'Simulation '40 max_layers = 10041 min_layer_vol = 0.02542 min_layer_thick = 0.1543 max_layer_thick = 1.5044 density_model = 045 /46 &mixing47 surface_mixing = 148 coef_mix_conv = 0.249 coef_wind_stir = 0.2350 coef_mix_shear = 0.351 coef_mix_turb = 0.5152 coef_mix_KH = 0.353 deep_mixing = 254 coef_mix_hyp = 0.555 diff = 0.56 /575859606162 !-------------------------------------------------------------------------------100Appendix A. Example of GLM Namelist File63 ! fabm setup64 !-------------------------------------------------------------------------------65 ! ode_method [integer]66 ! ODE numerical scheme for source and sink dynamics67 ! 1: first-order explicit (not positive)68 ! 2: second-order explicit Runge-Kutta (not positive)69 ! 3: fourth-order explicit Runge-Kutta (not positive)70 ! 4: Patankar (first-order, not conservative)71 ! 5: Patankar-RK (second-order, not conservative)72 ! 6: Patankar-RK (does not work, not conservative)73 ! 7: Modified Patankar (1st-order, conservat., posit.)74 ! 8: Modified Patankar-RK (2nd-order, conservat., posit.)75 ! 9: Modified Patankar-RK (does not work, conservat.,76 ! posit.)77 ! 10: Extended Modified Patankar (1st-order, conservat.,78 ! posit.)79 ! 11: Extended Modified Patankar -RK (2nd-order, conservat.,80 ! posit.)81 ! This variable is used only if bio_calc = True82 ! split_factor [integer, minimum = 1]83 ! number of biogeochemical time steps per physical time step84 ! bioshade_feedback [bool]85 ! feedback of bio-turbidity to temperature equation86 ! repair_state [bool]87 ! FABM option to repeair state variables that have -ve's88 ! fabm_nml_file [string]89 ! name of fabm.nml file to be passed to FABM90 ! multi_ben [bool]91 ! GLM specific option for FABM to do benthic fluxes only92 ! in bottom layer, or on flanks of all layers (.true.)93 !-------------------------------------------------------------------------------94 !&wq_setup95 !!-- Choose 'aed2' or 'fabm' (aed2 is the default)96 ! wq_lib = 'aed2'97 !! wq_nml_file = 'aed/aed2.nml'98 ! ode_method = 1101Appendix A. Example of GLM Namelist File99 ! split_factor = 1100 ! bioshade_feedback = .true.101 ! repair_state = .true.102 !! mobility_off = .false.103 !! multi_ben = .true.104 ! benthic_mode = 1105 !/106107 !-------------------------------------------------------------------------------108 ! lake details109 !-------------------------------------------------------------------------------110 !111 ! name [string]112 ! name of the lake113 ! latitude [float, minimum = -90, maximum = 90, unit = deg North]114 ! latitude115 ! longitude [float, minimum = -360, maximum = 360, unit = deg East]116 ! longitude117 ! base_elev [float]118 ! base elevation (m)119 ! crest_elev [float]120 ! crest elevation (m)121 ! bsn_len [float]122 ! basin length at crest (m)123 ! bsn_wid [float]124 ! basin width at crest (m)125 ! bsn_vals [integer]126 ! number of depth points on height-area relationship127 ! H [float]128 ! elevations (m) (comma separated list, len=bsn_vals)129 ! A [float]130 ! area (m2 * 1000) (comma separated list, len=bsn_vals)131 !132 !-------------------------------------------------------------------------------133 &morphometry134 lake_name = 'BML'102Appendix A. Example of GLM Namelist File135 latitude = 57136 longitude = 111.6137 base_elev = 296.50, !it Never uses the base elevation138 crest_elev = 308.2,139 bsn_len = 3500,140 bsn_wid = 2200,141 bsn_vals = 125,142 ! H (m) A (m2)143 A = 2.55,15.34,2332.00,16313.00,38566.00,63962.00,91093.00,121440.00,144 160700.00,218620.00,283120.00,352370.00,416590.00,482150.00,550340.00,145 619270.00,694710.00,783930.00,891350.00,1013500.00,1149100.00,1294500.00,146 1447300.00,1628200.00,1835900.00,2092200.00,2371800.00,2659900.00,147 2931100.00,3196600.00,3460000.00,3717200.00,3967400.00,4197300.00,148 4396800.00,4569400.00,4720300.00,4850900.00,4973100.00,5091100.00,149 5215700.00,5334800.00,5442200.00,5542600.00,5661800.00,5809100.00,150 5935900.00,6044400.00,6142300.00,6237100.00,6328700.00,6400600.00,151 6460600.00,6508600.00,6545300.00,6575200.00,6819500.00,6837000.00,152 6854400.00,6871800.00,6889200.00,6903800.00,6918400.00,6933000.00,153 6947600.00,6962200.00,6975100.00,6988000.00,7000900.00,7013900.00,154 7026800.00,7038800.00,7050800.00,7062900.00,7074900.00,7086900.00,155 7100900.00,7114900.00,7128800.00,7142800.00,7156700.00,7168100.00,156 7179400.00,7190700.00,7202000.00,7213300.00,7223600.00,7233800.00,157 7244000.00,7254200.00,7264500.00,7274400.00,7284300.00,7294200.00,158 7304200.00,7314100.00,7323400.00,7332800.00,7342100.00,7351400.00,159 7360700.00,7372600.00,7384400.00,7396300.00,7408100.00,7420000.00,160 7448500.00,7477000.00,7505500.00,7534100.00,7562600.00,7584700.00,161 7606700.00,7628800.00,7650900.00,7673000.00,7680400.00,7687700.00,162 7695100.00,7702500.00,7709800.00,7711200.00,7712500.00,7713900.00,163 7715200.00,164 H = 296.50,296.60,296.70,296.80,296.90,297.00,297.10,297.20,297.30,165 297.40,297.50,297.60,297.70,297.80,297.90,298.00,298.10,298.20,166 298.30,298.40,298.50,298.60,298.70,298.80,298.90,299.00,299.10,167 299.20,299.30,299.40,299.50,299.60,299.70,299.80,299.90,300.00,168 300.10,300.20,300.30,300.40,300.50,300.60,300.70,300.80,300.90,169 301.00,301.10,301.20,301.30,301.40,301.50,301.60,301.70,301.80,170 301.90,302.00,302.10,302.20,302.30,302.40,302.50,302.60,302.70,103Appendix A. Example of GLM Namelist File171 302.80,302.90,303.00,303.10,303.20,303.30,303.40,303.50,303.60,172 303.70,303.80,303.90,304.00,304.10,304.20,304.30,304.40,304.50,173 304.60,304.70,304.80,304.90,305.00,305.10,305.20,305.30,305.40,174 305.50,305.60,305.70,305.80,305.90,306.00,306.10,306.20,306.30,175 306.40,306.50,306.60,306.70,306.80,306.90,307.00,307.10,307.20,176 307.30,307.40,307.50,307.60,307.70,307.80,307.90,308.00,308.10,177 308.20,308.30,308.40,308.50,308.60,308.70,308.80,308.90,178 /179 !-------------------------------------------------------------------------------180 ! duration of run181 !-------------------------------------------------------------------------------182 !183 ! timefmt [integer]184 ! method to specify start and duration of model run185 ! 1: duration computed from number of time steps, MaxN (bogus start186 ! date used) [no longer implemented!!]187 ! 2: duration computed from given start and stop dates (number of time188 ! steps MaxN computed)189 ! 3: duration computed from number of time steps, MaxN (start date as190 ! s specified , stop date comuted)191 ! start [string, format = "yyyy-mm-dd hh:mm:ss"]192 ! nominal start date193 ! This variable is used only if timefmt != 1194 ! stop [string, format = "yyyy-mm-dd hh:mm:ss"]195 ! nominal stop date196 ! This variable is used only if timefmt = 2197 ! dt [float, minimum = 0.001, maximum = 86400, unit = s]198 ! Time step for integration199 ! numb_days [number of days to run the simulation ]200 ! This variable is used only if timefmt != 2201 !202 !-------------------------------------------------------------------------------203 &time204 timefmt = 2205 start = '2016-05-03 15:00:00'206 stop = '2016-07-06 00:00:00'104Appendix A. Example of GLM Namelist File207 dt = 3600.0208 num_days = 182209 !timezone = 7.0210 /211 !-------------------------------------------------------------------------------212 ! format for output and filename(s)213 !-------------------------------------------------------------------------------214 !215 ! out_dir [string]216 ! path to output directory (set permissions)217 ! out_fn [string]218 ! name of output netcdf file219 ! out_lkn [logical]220 ! output Lake Number to a file221 ! nsave [integer, minimum = 1, maximum = 86400]222 ! save results every 'nsave' timesteps223 ! csv_lake_fname [string]224 ! name of lake.csv lake simulation daily summary information225 ! csv_point_nlevs [integer]226 ! number of depths at which to dump a csv time-series file227 ! csv_point_at [real]228 ! height from bottom for point csv file(s) (comma separatedlist)229 ! csv_point_fname [string]230 ! name of csv output file(s) (comma separated list)231 ! csv_point_nvars [integer]232 ! number of variables to output into csv233 ! csv_point_vars [string]234 ! list of names of variables to output, - order IS important235 !236 !-------------------------------------------------------------------------------237 &output238 out_dir = 'output'239 out_fn = 'output'240 nsave = 1241 ! General summary file105Appendix A. Example of GLM Namelist File242 csv_lake_fname = 'lake'243 ! Depth specific outputs244 csv_point_nlevs = 1245 csv_point_fname = 'WQ_'246 csv_point_at = 2.247 csv_point_nvars = 6248 csv_point_vars = 'temp',249 'salt',250 'TRC_SS1'251 'OXY_oxy',252 'Uorb'253 'Taub'254 'SIL_rsi',255 'NIT_amm',256 'NIT_nit',257 'PHS_frp',258 'OGM_don',259 'OGM_pon',260 'OGM_dop',261 'OGM_pop',262 'OGM_doc',263 'OGM_poc',264 'PHY_green ',265 'PHY_crypto ',266 'PHY_diatom '267268 ! Combined outlet file & overflow269 csv_outlet_allinone = .false.270 csv_outlet_fname = 'outlet_'271 csv_outlet_nvars = 17272 csv_outlet_vars = 'flow',273 'temp',274 'salt',275 'OXY_oxy',276 'SIL_rsi',277 'NIT_amm',106Appendix A. Example of GLM Namelist File278 'NIT_nit',279 'PHS_frp',280 'OGM_don',281 'OGM_pon',282 'OGM_dop',283 'OGM_pop',284 'OGM_doc',285 'OGM_poc',286 'PHY_green ',287 'PHY_crypto ',288 'PHY_diatom '289 csv_ovrflw_fname = "overflow"290291 /292 !-------------------------------------------------------------------------------293 ! initial condition profiles294 !-------------------------------------------------------------------------------295 !296 ! lake_depth [float] initial lake depth (m)297 ! num_depths [integer] number of depths provided for initial profiles298 ! the_depths [float] the depths of the initial profile points (m)299 ! the_temps [float] the temperature of the initial profile points (C)300 ! the_sals [float] the salinity of the initial profile points (psu)301 ! num_wq_vars [integer] number of non GLM (ie FABM) vars to be initialised302 ! wq_names [string] names of non GLM (ie FABM) vars to be initialised303 ! wq_init_vals [float] array of FABM vars (rows = vars; cols = depths)304 !305 !-------------------------------------------------------------------------------306 &init_profiles307 lake_depth = 11.7308 num_depths = 12309 the_depths = 0.00,0.50,1.00,2.00,3.00,4.00,5.00,6.00,7.00,8.00,9.00,11.70310 the_temps = 8.41,8.41,8.41,6.34,4.61,3.85,3.43,3.12,2.88,2.87,2.87,2.87311 the_sals = 1.95,1.95,1.95,1.96,2.00,2.07,2.12,2.17,2.18,2.19,2.19,2.19312 ! WQ313 num_wq_vars = 0107Appendix A. Example of GLM Namelist File314 wq_names = 'OXY_oxy',315 'TRC_ss1'316 !wq_init_vals = 2, 2, 2, 10, 20, 30, 30, 30, 30, 30, 30, 30, 30,317 ! 2, 2, 2, 10, 20, 30, 30, 30, 30, 30, 30, 30, 30318 /319 !-------------------------------------------------------------------------------320 ! meteorology321 !-------------------------------------------------------------------------------322 !323 ! met_sw [bool] switch to include surface meteorological forcing324 ! lw_type [string] type of longwave data supplied (LW_IN/LW_CC/LW_NET)325 ! rain_sw [bool] include rainfall nutrient composition326 ! snow_sw [bool] include snowfall (m/d)327 ! atm_stab [bool] account for non-neutral atmospheric stability (notpossible yet}328 ! meteo_fl [string] name of file with meteorology input data329 ! wind_factor [float] wind multiplication factor (-)330 ! ce [float] bulk aerodynamic coefficient for latent heat transfer331 ! ch [float] bulk aerodynamic coefficient for sensible heat transfer332 ! coef_wind_drag [float] bulk aerodynamic coefficient for transfer of momentum333 !334 !-------------------------------------------------------------------------------335 &meteorology336 met_sw = .TRUE.337 lw_type = 'LW_NET'338 rain_sw = .false.339 atm_stab = .false.340 !catchrain = .true.341 rad_mode = 2342 albedo_mode = 2343 cloud_mode = 1344 !-- BC file details345 subdaily = .true.346 meteo_fl = 'bcs/BML_met.csv'347 wind_factor = 0.6348 sw_factor = 1.0108Appendix A. Example of GLM Namelist File349 lw_factor = 1.0350 at_factor = 1.0351 rh_factor = 1.0352 rain_factor = 1353 !-- Parameters354 ce = 0.001355 ch = 0.0013356 cd = 0.0013357 !rain_threshold = 0.001358 !runoff_coef = 0.0359 /360 &light361 light_mode = 1362 !n_bands = 4363 !light_extc = 1.0, 0.5, 2.0, 4.0364 !energy_frac = 0.51, 0.45, 0.035, 0.005365 !Benthic_Imin = 0.10366 Kw = 4367 /368 !-------------------------------------------------------------------------------369 ! bird_model370 !-------------------------------------------------------------------------------371 !372 ! AP = 973 Atmospheric Pressure in milibars373 ! Oz = 0.279 Ozone concentration in atm-cm374 ! WatVap = 1.1 Total Precipitable water vapor in atm-cm375 ! AOD500 = 0.033 Dimensionless Aerosol Optical Depth at wavelength 500 nm376 ! AOD380 = 0.038 Dimensionless Aerosol Optical Depth at wavelength 380 nm377 ! Albedo = 0.2 Default Albedo value378 !379 !-------------------------------------------------------------------------------380 &bird_model381 AP = 973382 Oz = 0.279383 WatVap = 1.1384 AOD500 = 0.033109Appendix A. Example of GLM Namelist File385 AOD380 = 0.038386 Albedo = 0.2387 /388389 !-------------------------------------------------------------------------------390 ! inflows391 !-------------------------------------------------------------------------------392 !393 ! num_inflows [integer] number of inflowing streams (0+)394 ! names_of_strms [string] names of streams (comma separated list)395 ! strm_hf_angle [float] stream half angle (degrees)396 ! strmbd_slope [float] streambed slope (degrees)397 ! strmbd_drag [float] streambed drag coefficient (-)398 ! inflow_factor [float] inflow flow rate multiplier (-)399 ! inflow_fl [string] inflow data filename(s) (comma separated list)400 ! inflow_varnum [integer] number of columns (excluding date) to be read401 ! inflow_vars [string] variable names of inflow file columns402 ! This should be a comma separated list, and must403 ! include FLOW, SALT & TEMP (for GLM), and404 ! optionally can include FABM var names.405 !406 !-------------------------------------------------------------------------------407 &inflow408 num_inflows = 1409 names_of_strms = 'PumpIn','PoreWater '410 subm_flag = .false., .true.411 strm_hf_angle = 45.0, 45.0412 strmbd_slope = 0.001, 100413 strmbd_drag = 0.0160, 0414 subm_elev = 9, 0415 inflow_factor = 1,1416 inflow_fl = 'bcs/BML_inflow.csv','bcs/BML_PoreWaterInflow_2.csv'417 inflow_varnum = 3418 inflow_vars = 'FLOW',419 'TEMP',420 'SALT'110Appendix A. Example of GLM Namelist File421 coef_inf_entrain = 0.422 ! time_fmt = 'YYYY-MM-DD hh:mm:ss'423 /424 !-------------------------------------------------------------------------------425 ! outflows426 !-------------------------------------------------------------------------------427 !428 ! num_outlet [integer] no. of outlets429 ! flt_off_sw [bool] floating offtake switches430 ! outl_elvs [float] outlet elevations (comma separated list)431 ! bsn_len_outl [float] basin length at outlets (m)432 ! bsn_wid_outl [float] basin width at outlets (m)433 ! outflow_fl [string] outflow data file434 ! outflow_factor [float] outflow flow rate multiplier (-)435 !436 !-------------------------------------------------------------------------------437 &outflow438 num_outlet = 1439 !flt_off_sw = .false.440 outlet_type = 1441 outl_elvs = 307.2442 !bsn_len_outl = 3500443 !bsn_wid_outl = 2200444 outflow_fl = 'bcs/BML_outflow.csv'445 outflow_factor = 1.0446 !seepage = .true.447 !seepage_rate = 0.01448 /449450 !-------------------------------------------------------------------------------451 &sediment452 sed_heat_Ksoil = 0.6453 sed_temp_depth = 0.5454 sed_temp_mean = 10455 sed_temp_amplitude = 4456 sed_temp_peak_doy = 240111Appendix A. Example of GLM Namelist File457 benthic_mode = 1458 n_zones = 1459 zone_heights = 10., 40., 90.460 sed_reflectivity = 0.1, 0.01, 0.01461 sed_roughness = 0.1, 0.01, 0.01462 /463 !-------------------------------------------------------------------------------464 &snowice465 snow_albedo_factor = 1.0466 snow_rho_max = 300467 snow_rho_min = 50468 /469 !-------------------------------------------------------------------------------470 !&debugging471 !disable_evap = .true.472 !/112Appendix BHeat Content and Thermal StabilityThe heat content calculated from the field measurements is shown in Figure B.1. As depictedin the figure, the HC of the P2 and P3 moorings gradually increases after ice-off (DOY 118and 125 in 2016 and 2017 respectively), peaking around the beginning of August (around DOY220), before starting to decrease as the lake approaches fall. An oscillation in the HC betweenthe two platforms is observed, due to the different thermal structures caused by wind-inducedlake seiching. See further discussion in Chapter 3.The thermal stability calculated from field measurements is presented in Figure B.2. Asshown, stability at both the P2 and P3 moorings gradually increases following ice-off, peakingaround the beginning of August (approximately DOY 220), before declining in fall.113AppendixB.HeatContentandThermalStabilityFigure B.1: Heat content of 2016 and 2017 at the P2 ( ) and P3 ( ) moorings, with corresponding CTD measurements marked by stars:(T) for P2 and (T) for P3.114AppendixB.HeatContentandThermalStabilityFigure B.2: Thermal Stability of 2016 and 2017 at the P2 ( ) and P3 ( ) moorings, with corresponding CTD measurements marked bystars: (T) for P2 and (T) for P3.115Appendix CMooring MeasurementsMooring measurements from P2 and P3 for the spring and summer of 2016 and 2017 arepresented in Figures C.1, C.2, C.3, and C.4. Note for the spring of 2016 the P2 mooring wascarried by ice and therefore located 300 meters east of it’s normal location.116AppendixC.MooringMeasurementsFigure C.1: Time series of mooring temperatures at BML for the spring of 2016 with selected wind events highlighted in grey. The toppanel shows the temperature time series from 1m to 8m at P2, the middle panel displays the temperatures from 0O5m to 9m at P3, andthe bottom panel represents the average P2 and P3 temperatures from 1m to 8m.117AppendixC.MooringMeasurementsFigure C.2: Time series of mooring temperatures at BML for the spring of 2017 with selected wind events highlighted in grey. The toppanel shows the temperature time series from 0O5m to 11O5m meters at P2, the middle panel displays the temperatures from 1m to 9mmeters at P3, and the bottom panel represents the average P2 and P3 temperatures from 1m to 9m.118AppendixC.MooringMeasurementsFigure C.3: Time series of mooring temperatures at BML for the summer of 2016 with selected wind events highlighted in grey. The toppanel shows the temperature time series from 1m to 10m at P2, the middle panel displays the temperatures from 0O5m to 9m at P3,and the bottom panel represents the average P2 and P3 temperatures from 1m to 9m.119AppendixC.MooringMeasurementsFigure C.4: Time series of mooring temperatures at BML for the summer of 2017 with selected wind events highlighted in grey. The toppanel shows the temperature time series from 0O5m to 11O5m at P2, the middle panel displays the temperatures from 1m to 9m at P3,and the bottom panel represents the average P2 and P3 temperatures from 1m to 9m.120
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lawrence, Gregory A.
Laval, Bernard
Stratification during spring and summer in a brackish lake subject to ice cover
Text