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Artificial destratification for reservoir evaporation reduction: is it
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effective?
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Fernanda Helfer*, Fernando Pinheiro Andutta, José Antônio Louzada1, Hong Zhang,
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Charles Lemckert2
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School of Engineering and Built Environment, Griffith University, Queensland, Australia
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*
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*
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(G09_1.25), Southport, QLD 4222, Australia.
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Telephone: +61 (0)7 5552 7886; Facsimile: +61 (0)7 5552 8065
Corresponding author’s e-mail address: f.helfer@griffith.edu.au.
Corresponding author’s postal address: Griffith University, School of Engineering
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Short running title:
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Destratification for evaporation reduction
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Abstract: The aim of this study was to assess the effectiveness of artificial destratification
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by air-bubble plumes in reducing evaporation from reservoirs. DYRESM was used to
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model the evaporation rates and thermodynamic behavior of a temperate reservoir in
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Australia under a number of combinations of destratification designs and operating
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conditions. The designs comprised various numbers of ports and air-flow rates per port.
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The operating conditions involved continuous operation and various intermittent operating
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strategies. Three reservoir depths were considered: 6.5, 11.5 and 16.5 meters,
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characterizing ‘shallow’, ‘medium’ and ‘deep’, respectively. The results showed that,
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provided thermal stratification develops in a reservoir (which was the case for the
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Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Rio Grande do
Sul, Brazil
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Faculty of Science & Technology, University of Canberra, Australian Capital Territory, Australia
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‘medium’ and ‘deep’ reservoirs), artificial destratification is able to reduce surface
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temperatures and evaporation rates. Due to the larger volume of cold water at the bottom,
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deeper reservoirs can derive greater benefit from the use of these systems. After being
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raised to the surface by the air injected through the destratification system, the cold water
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from the bottom will help reduce the surface temperatures. Conversely, shallow lakes, due
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to their typical homothermous regime, are unlikely to benefit from these systems, as these
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reservoirs lack a source of abundant cold water at the bottom. Despite this, the reductions in
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evaporation from deep reservoirs can only be modest. The maximum reduction was only
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2.9% for a deep lake (16.5 meters), using an energy-intensive destratification system. It was
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concluded that the use of destratification systems for reservoir evaporation reduction are
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not warranted because of the modest water savings achieved.
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Keywords: air-bubble plumes, DYRESM, evaporation, hydrodynamic modelling, lake,
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reservoir, vertical mixing, water balance, water temperature
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1. Introduction
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The world is facing growing pressure regarding water resources due to the
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increasing strain imposed by population growth, economic development, extreme drought
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and climate change. The pressure on water resources is being felt more intensively in arid
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and semi-arid locations where water is even more scarce (Bouwer, 2000; Maestre-Valero et
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al., 2013). This scarcity is drawing increasing attention to the development of water-saving
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cost-effective and reliable approaches. Reducing evaporation from reservoirs could
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effectively help arid and semi-arid countries to overcome water scarcity (Martinez Alvarez
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et al., 2008; Gallego-Elvira et al., 2013).
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In Australia, evaporation from reservoirs is regarded as the most adverse factor
contributing to loss of water. It is estimated that 40% of the volume stored in Australian
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reservoirs is lost each year due to high evaporation rates (Craig et al., 2005). Evaporation
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can often exceed 2000 mm per year in most areas in Australia (Department of Natural
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Resources and Mines, 2005). Additionally, climate change has been posing a significant
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threat to water availability in Australian reservoirs as there is a growing body of climate
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evidence in support of increases in Australian air temperatures, with concurrent increases in
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evaporation rates (CSIRO & BoM, 2007). In South-East Queensland, where this study was
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conducted, an investigation showed that annual evaporation will be approximately 8%
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higher than the current long-term average annual evaporation around the year 2040, and
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15% higher around the year 2080 (Helfer et al., 2012a).
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For decades, arid and semi-arid countries such as Australia have been investigating
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and developing mechanisms for reducing evaporation from reservoirs and thus contributing
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to water security. Most of the techniques however, have been shown either not to be
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effective, as in the example of windbreaks (Helfer et al., 2009a; 2009b); to be excessively
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expensive, as in the example of floating covers and shade-cloth covers (Martinez Alvarez et
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al., 2009); to impose potential risks on water quality, as in the example of chemical and
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physical covers (Yao et al., 2010); or to be difficult to implement at field scale, as in the use
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of chemical covers (Barnes, 2008).
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Artificial destratification by air-bubble plumes (the injection of air through a
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diffuser placed at the bottom of a lake) is one technique suggested in the literature as a
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potential mechanism for reducing evaporation from reservoirs (Koberg & Ford, 1965;
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Helfer et al., 2011a; 2011b; 2012b). Destratification devices are tools to mix or circulate
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water vertically in a reservoir. Their primary purpose is to prevent thermal stratification,
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which is frequently associated with substantial hypolimnetic oxygen depletion (Johnson,
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1984; Beduhn, 1994). Hypoxia in lakes and reservoirs stresses aquatic life, threatens fish
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populations, and reduces water quality (Bertone et al., 2015). The potential of this
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technique for controlling evaporation loss lies in cooling the temperature of surface waters
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through vertical mixing (Helfer et al., 2011b). When in operation, destratification systems
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allow the colder hypolimnion water to mix with the warmer surface water, reducing surface
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temperatures and, consequently, evaporation rates. Based on this theory, it follows that
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there has to be significant temperature stratification in a lake for these systems to be
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effective in reducing relative evaporation. To date, however, only a few studies have
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focused on the use of air-bubble plumes to reduce evaporation from reservoirs. Helfer et al.
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(2011a) suggested that the technique could be more effective in deep reservoirs due to the
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development of a pronounced thermal stratification profile, where high differences between
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surface and bottom water temperatures develop. However, further studies were required to
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better quantify the achievable evaporation reduction.
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In this context, the aim of the current study was to undertake a comprehensive study on
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the effect of artificial destratification systems on evaporation reduction from reservoirs.
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Reservoir depth, number of destratification ports (diffuser holes), air-flow rates, and
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operation strategies of destratification systems were investigated and correlated with
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evaporation rates. Three reservoir depths (6.5, 11.5 and 16.5 meters, characterizing a
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‘shallow’, ‘medium’ and ‘deep’ lake), with varying numbers of air diffuser holes, and
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varying air-flow rates per hole were studied. A number of operation strategies were also
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analyzed, including, for example, destratification systems in continuous operation over the
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period of simulation, as well as in various intermittent operation schedules, such as in
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weekly and monthly cycles. The investigations were all conducted with the use of
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modelling and simulation using the one-dimensional model DYRESM, as described in the
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following section.
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2. Materials and Methods
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2.1. DYRESM
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DYRESM (Imberger & Patterson, 1981; Imerito, 2010a; 2010b) is a one-
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dimensional hydrodynamic model used for the prediction of the vertical distribution of
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temperature, salinity and density in reservoirs, at daily and sub-daily time-steps. For over
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30 years, this model has been successfully applied to a range of water bodies with various
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morphologies and climatic conditions to predict water quality parameters (e.g. Shiati, 1991;
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Hamilton & Schladow, 1997; Moshfeghi et al., 2005; Perroud et al., 2009; Weinberger &
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Vetter, 2012; Anderson et al, 2014; Lehman, 2014; Hetherington et al., 2015) as well as
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evaporation rates (Hipsey, 2006; Helfer et al., 2011b; McGloin et al., 2014). By comparing
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a number of one-dimensional lake models, Perroud et al. (2009) found that DYRESM is
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one of the best models to reproduce the variability of the water temperature profiles in lakes
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and reservoirs. As a one-dimensional model, DYRESM assumes that vertical variations in
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density, temperature and salinity are more important than horizontal variations, as these are
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rapidly relaxed by horizontal advection and convection due to the restoring force of
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stratification being greater than the disturbing force of the wind.
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The main input data in DYRESM are the depth-area relationship of the studied lake,
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daily or sub-daily meteorological data (incident short-wave radiation, rainfall, air
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temperature, air vapour pressure, wind speed, and either net long-wave radiation, incident
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long-wave radiation or cloud cover fraction), daily inflows (and inflow temperature and
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salinity), daily outflows, and an initial profile of lake temperature and salinity (from which
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DYRESM derives the initial water depth). The model is based on a Lagrangian layer
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scheme in which the reservoir is represented by a series of adjoining horizontal layers of
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uniform properties that vary in thickness within user-defined limits. At each time step, as
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inflows and outflows enter or leave the reservoir, the affected layers expand or contract,
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and those above, move up or down to accommodate the volume change.
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The mechanisms to heat and cool a lake are shortwave radiation (penetrative and
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non-penetrative), longwave radiation and sensible and latent heat fluxes. The surface mass
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fluxes include rainfall and evaporation. The wind field in DYRESM drives the surface layer
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shear and the latent and sensible heat fluxes. Wind stress, shortwave radiation, heat
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penetration, latent heat, sensible heat and longwave radiation are the main inputs of energy
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for mixing and stratifying the lake. Mixing and surface layer deepening are modelled by
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amalgamation of layers, based on a criterion of available kinetic energy and required
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potential energy for mixing any two layers. Three mixing mechanisms are considered in the
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model: stirring, in which wind energy is transferred to the surface layer; convective
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overturn, in which the energy is provided from a reduction in potential energy due to dense
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water sinking to a lower level; and shear, in which kinetic energy is transferred from the
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upper to the lower layers. The mixing algorithm involves computing the available energy
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for mixing two layers, and comparing this with the required energy for mixing these layers.
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Mixing (amalgamation of layers) occurs when there is enough energy for mixing, in which
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case the excess energy is transferred and used for the mixing of deeper layers.
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The sensible and latent heat fluxes are calculated by mass transfer models.
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Importantly, the evaporative flux at each time-step is calculated as a function of wind speed
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and humidity deficit:
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E = C E ρU (q s − qa )
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where E is evaporation (kg m-2 s-1), ρ is the air density (kg m-3), CE is the bulk aerodynamic
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transfer coefficient for a measurement height of 10 m (=1.3 × 10-3, Fischer et al., 1979;
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Brutsaert, 1982), U is the wind speed (m s-1), and qa and qs are the actual and saturation
(1)
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specific humidities, respectively (kg kg-1). The saturation specific humidity is a function of
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the uppermost water layer.
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One important feature in DYRESM is a sub-routine to model artificial water mixing
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based on destratification devices, such as impellers and air diffusers (bubble plumes). The
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artificial mixing algorithm has been successfully validated with field data in many instances
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(eg, Imteaz & Asaeda, 2000; Lewis et al., 2001; Imteaz et al., 2009). For air diffusers (the
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artificial mixing mechanism simulated in this study, which consists of a device through
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which air is injected at the bottom of the reservoir), the algorithm is based on a simple,
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single core plume, assumed to be circular and non-interacting (McDougall, 1978). The
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motion of this plume is determined from three differential equations of conservation of
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mass, momentum and buoyancy (Patterson & Imberger, 1989). To model the bubble plume
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artificial mixing, DYRESM uses the same layer discretisation used in the main routine,
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with the bubble plume entraining water from each layer as the air bubbles travel through
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them. As the bubble plume rises, the effective buoyancy anomaly between plume and
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ambient water decreases as entrained water lowers the plume density, at the same time that
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the ambient water density decreases with height as a result of thermal stratification. In the
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layer where the buoyancy anomaly becomes zero or negative, the water in the plume is
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ejected horizontally into the reservoir, and the plume model resumes. The model assumes
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that this detrained water immediately routes back to its neutrally buoyant level, without
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further entrainment.
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The bubble plume mixing algorithm requires the following input variables: the
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depth of the diffuser in the water (h), the total air-flow rate at the diffuser level (QT), the
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number of destratification devices in operation, and the number of ports per device. In each
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time step, the total air-flow rate is divided by the number of ports to obtain the air-flow rate
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per port (QB). All calculations are then computed on a ‘per port’ basis.
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The bubble plume model initialises by computing the buoyancy flux (B) resulting
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from the injection of air at the level of the diffuser (QB):
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B = gQB
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where g is the gravity. The flow rate of entrained water at the level of the diffuser (QP) is
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calculated as:
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QP = α
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where α is the entrainment coefficient (= 0.083 - List, 1982; Milgram, 1983), b1 is a
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constant (= 4.7), γ is the plume aspect ratio (plume radius to plume length, assumed to be
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constant and equal to 0.1), and ∆ZB is the layer thickness at the diffuser depth. For
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subsequent layers, the flow rate of air (Q) is computed as a function of the change in
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pressure head, which increases the air-flow rate due to bubble expansion:
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H 
Qi = Qi −1  i −1 
 Hi 
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where (i-1) refers to the layer immediately below, and H is the pressure head at the level of
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the layers. Only for the second layer from the diffuser layer, Qi-1 is replaced by QB. The
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combined buoyancy flux due to air bubbles and entrained water is calculated as:
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 ρ − ρi
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where ρ0i is the density of the ambient water in the current layer, and ρi is the density of the
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plume. The flow rate of the entrained volume in layer i is:
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QPi = α
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where z is the depth of each layer.
(2)
6π
b1γ B1 3 ∆ Z B 5 3
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(3)
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0.71
(4)
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
 QP
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6π
b1γ Bi 1 3 ( zi 5 3 − zi −15 3 ) + QPi−1
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(5)
(6)
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When the combined buoyancy flux becomes zero or negative, the entrained water is
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ejected from the plume and it is assumed to fall to its neutrally buoyant level
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instantaneously. The plume characteristics are then reset, and the air continues to rise and to
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entrain water again.
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2.2. Study area and data collection
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A reservoir used for irrigation of crops, located in South-East Queensland,
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Australia, was used to study the effects of artificial destratification on evaporation. Logan’s
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Dam (27o34’26’’S, 152o20’27’’E, altitude 88 m, Figure 1) has a storage capacity of 0.7
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hm3, a full storage surface area of approximately 17 hectares and a maximum depth of 6.5
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m. The reservoir is roughly rectangular in shape with dimensions of approximately 480 m x
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350 m.
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A comprehensive 2-year investigation on open water evaporation was conducted by
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the Urban Water Security Research Alliance (UWSRA) (www.urbanwateralliance.org.au)
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from 2009 to 2011 at Logan’s Dam. As part of this investigation, measuring equipment
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items, including flow meters, water level sensors, thermistor chains and weather stations,
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were installed at the reservoir to measure inflows, outflows, water levels, water
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temperatures, and to monitor atmospheric conditions. A thorough description of the
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equipment used and measurements taken at Logan Dam’s is provided in McGloin et al.
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(2014) and McJannet et al. (2013a,b). All data sets needed to calibrate, validate and run the
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model DYRESM in this study, including initial water temperatures, lake morphology
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(elevation vs surface area relationship), external forcing daily data for solar radiation, wind
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speed, air temperature and rainfall, outflows and inflows were obtained from the UWSRA
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investigation.
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Figure 1. Aerial view of the agricultural reservoir investigated in the current study
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(from Google Earth) and its location in Australia. Location of centre of reservoir:
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27o34’26’’S, 152o20’27’’E. Surface area: 17 hectares (170,000 m2).
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2.3. Model calibration and validation for water temperatures
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DYRESM was calibrated based on its ability to reproduce measured values of water
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temperature in Logan’s Dam. The model performance was tested by undertaking regression
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analyses between daily temperature measurements and daily temperature outputs from the
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model. The calibration was implemented manually by using the error minimisation method
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(Perroud et al., 2009; Weinberger & Vetter, 2012). The period chosen for calibration was
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29/09/2009 - 06/01/2010 (100 days), covering a significant part of spring and summer
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seasons, and a validation was undertaken over summer and autumn, during the period
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07/01/2010 - 11/04/2010 (95 days). The model was calibrated with respect to the parameter
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light extinction coefficient. The light extinction coefficient influences the thermodynamics
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of the lake through the varying water column heat absorption, but no measured data for this
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parameter was available for Logan’s Dam. A mean annual, depth averaged value was
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assumed for the simulations, following similar studies (e.g. Hetherington et al., 2015).
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Different values were tested, ranging from 0.3 to 2.0 m-1, with 1.3 m-1 yielding the best
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match between simulated and measured water temperatures. This is a realistic value, since
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the lake is relatively shallow and turbidity is high (Oliver et al., 2000).
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2.4. Model validation for evaporation rates
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In the current study, the data collected by the UWSRA investigation, including
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rainfall, reservoir water levels, outflows and inflows, were used to determine observed
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daily evaporation rates by performing a water balance of the water storage. The water
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balance approach is a technique employed to estimate changes in water level by taking into
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account all the inputs and losses from a reservoir in a given period of time. The use of this
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method to determine evaporation rates has been successful in a number of other
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evaporation studies (eg, Abtew, 2001; Gibson, 2002). In the current study, the inputs for the
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water balance model included daily inflows and direct rainfall. The reservoir losses
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included daily outflows, evaporation and leakage. The water balance equation was
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rearranged to determine the unknown daily evaporation rates from Logan’s Dam.
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The water balance method for the determination of daily evaporation rates required
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the specification of an estimate for the leakage term, since this parameter was not measured
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on site. There have been several studies in which leakage losses have been determined for
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Australian man-made reservoirs. For example, Craig (2006) determined leakage losses for
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four reservoirs, comparing modelled evaporation rates with evaporation rates calculated
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using the water balance method. The leakage loss term was determined as being something
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between 1.0 and 2.0 mm day-2, depending on the choice of the evaporation model.
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McJannet et al. (2013a) compared evaporation rates from Logan’s Dam estimated by water
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balance using two leakage loss terms (1.0 and 2.0 mm day-1) with evaporation rates
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measured with scintillometry. It was found that a leakage loss of 1.0 mm day-1 would
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overestimate evaporation rates from Logan’s Dam, whereas a leakage loss of 2.0 mm day-1
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would underestimate it. It was suggested that an optimal fixed leakage value for Logan’s
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Dam would be something between the two aforementioned leakage rates (McJannet et al.,
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2013a). Following this suggestion, in this study, we assumed a constant leakage loss of 1.5
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mm day-1 in the water balance model.
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An indirect validation was also performed comparing the daily evaporation rates
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estimated by DYRESM with daily evaporation rates estimated with a model proposed by
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McGloin et al. (2014; 2015) for Logan’s Dam. Their model predicts eddy covariance
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measurements for latent heat and has the form:
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LEEC = 27.91u ( es − ea ) + 18.34
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where LEEC is the approximated measured latent heat flux (W m-2), u is the wind speed at
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2.4 m height (m s-1), es is the saturation vapour pressure at water temperature (kPa) and ea is
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the vapour pressure of the air (kPa).
(7)
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The evaporation rates calculated through both the water balance model and the
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latent heat model (Eq. 7) were subsequently compared with the daily evaporation rates
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estimated by the model DYRESM for Logan’s Dam to evaluate the model’s performance.
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2.5. Scenarios, simulations and assumptions
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The shallow lake scenario in this study represented the real situation, in which the
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maximum water depth at the deepest section of the reservoir is 6.5 m and the maximum
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surface area is 17 hectares (Figure 1). This is the scenario for which DYRESM was
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calibrated and validated. Hypothetical scenarios, with maximum depths of 11.5 m and 16.5
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m, with 17 hectares of surface area, composed a medium lake scenario and a deep lake
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scenario, respectively.
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In all three scenarios, the initial water depth was set as the maximum depth, and the
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initial water temperature, uniform throughout the profile, at 17.8oC (as measured in the
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field for the real case scenario).
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The baseline simulations for each scenario consisted of the absence of any
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destratification system operating in the reservoir. Then, simulations with a destratification
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system operating continuously throughout the 195 days of study were simulated. The
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destratification system consisted of one air diffuser laid at the bottom of the lake with
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varying number of ports and air-flow rates. The air-flow rates injected through this diffuser
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into the water varied from 0.001 to 1.0 m3 s-1, and the number of ports on each diffuser
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varied from 1 to 40. Table 1 summarises the simulations undertaken using continuous
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artificial destratification.
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Table 1. Summary of the simulations with the artificial destratification system
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operating continuously over the period of study.
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Following the results from the simulations provided in Table 1, the most effective
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systems were then selected for further evaluation of their operational strategies, including
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intermittent operation modes and operation in monthly and weekly cycles.
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noted for analyses. The differences in evaporation rates between destratification conditions
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and normal conditions (baseline simulations) provided an indication of the effectiveness of
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the destratification techniques in reducing evaporation. Analyses were then performed to
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determine the optimum combination of air-flow rate, number of ports and operational
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strategy that provided maximum evaporation reductions.
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3. Results and Discussion
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3.1. Observed meteorological conditions
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Figure 2 shows a time series of the surface meteorology of Logan’s Dam for the
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simulation period (29/09/2009 – 11/04/2010), which covered spring, summer and autumn
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seasons. For reference, in Australia, the spring months are September, October and
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November; summer months are December, January and February; autumn months are
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March, April and May; and winter months are June, July and August.
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Daily average shortwave solar radiation was 253 W m-2, with the highest values
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concentrated in January 2010 (summer). The lowest shortwave radiation values occurred in
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the first week of March 2010 (autumn). The average air temperature for the entire period
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was 24.1oC and ranged between a minimum of 16.2oC and a maximum of 30.3oC. The
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hottest period was the second week of December 2009 (summer), in which the average
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temperature was 28.5oC. The coldest period was the second week of October 2009 (spring).
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The average daily wind speed was 2.8 m s-1, with a maximum of 6.1 m s-1. For 90% of the
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time winds were less than 4 m s-1. The average relative humidity was 67%, and the average
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vapour pressure deficit was 1.0 kPa, ranging from 0.2 to 2.4 kPa. Total rainfall was 365
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mm in the period of simulation. Rainfall was mostly concentrated between the last week of
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January 2010 and the first week of March 2010, when 269 mm rainfall was registered.
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Figure 2. Average daily shortwave solar radiation, air temperature, wind speed,
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vapour pressure deficit and total daily rainfall at Logan’s Dam throughout the study
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period. For reference, in Australia, the spring months are September, October and
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November; summer months are December, January and February; autumn months
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are March, April and May; and winter months are June, July and August.
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3.2. Model calibration and validation for water temperatures
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3.2.1. Calibration period - 29/09/2009 to 06/01/2010 (100 days)
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Figure 3 shows the measured and modelled water temperatures in Logan’s Dam.
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The coefficient of determination (R2) for the adjustment, considering 100 days of measured
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data, was 0.91, the root-mean-square error (RMSE) was 0.7oC, the mean relative error
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between simulated and field measurements (NRMSE) was 1.6% and the mean bias error
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(MBE) was -0.39oC. These indices demonstrate that the model provides a satisfactory
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representation of the real temperatures.
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Figure 3. Isotherms for Logan’s Dam during the calibration period (29/09/2009 to
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06/01/2010). This period covers the spring months of October and November, and the
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summer month of December and part of January. a) Measured data. b) Modelled data
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using DYRESM.
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Since surface temperature is the most important variable in the calculation of
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evaporation (refer to Equation 1 for the evaporation model used in DYRESM), it is crucial
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that the temperature of the surface of the lake be modelled with a high level of accuracy.
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Figure 4 shows a strong correlation (R2 = 0.96) between modelled and measured surface
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water temperatures for the 100 days of calibration. The regression equation for theoretical
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surface temperature values against observed values had a slope that was nearly equal to 1,
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indicating that the model predicts surface temperatures with high accuracy. The root-mean-
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square error was 0.57oC, and the mean bias error was virtually zero (-0.02oC).
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Figure 4. Relationship between daily averages of measured (x-axis) and modelled (y-
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axis) surface water temperatures for the calibration period. The solid line indicates
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the regression equation, and the dashed line represents the 1:1 line.
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3.2.2. Validation period - 07/01/2010 to 11/04/2010 (95 days)
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Figure 5 shows the model’s performance for the validation period (07/01/2010 to
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11/04/2010). The model performed as well as in the calibration period, with a coefficient of
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determination equal to 0.94, a root-mean-square error equal to 0.4oC, a mean relative error
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between simulated and field measurements equal to 1.10% and a mean bias error equal to -
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0.02oC.
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Figure 5. Isotherms for Logan’s Dam during the validation period (07/01/2010 to
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11/04/2010). This period covers the summer months of January and February, and the
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autumn month of March and part of April. a) Measured data. b) Modelled data using
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DYRESM.
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Figure 6 shows a strong correlation (R2 = 0.97) between modelled and observed
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daily surface water temperatures for the 95 days of validation. The regression equation for
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theoretical surface temperature values against observed values had a slope equal to 1.02,
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indicating no tendency for either underestimation or overestimation of surface temperatures
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by the model. The mean bias error was virtually zero (0.02oC) and the root-mean-square
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error was 0.40oC. These results are in accordance with the results from a parallel study
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conducted at Logan’s Dam, in which a strong agreement (R2 = 0.97, RMSE = 0.97oC)
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between hourly surface temperature measurements and hourly surface temperatures
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predicted with DYRESM was found (McGloin et al., 2014).
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Figure 6. Relationship between daily averages of measured (x-axis) and modelled (y-
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axis) surface water temperatures for the validation period. The solid line indicates the
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regression equation, and the dashed line represents the 1:1 line.
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3.3. Model validation for evaporation rates
In Figure 7, a satisfactory agreement between the daily evaporation rates estimated
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using the water balance method and using DYRESM can be seen. The coefficient of
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determination was 0.72 with a root-mean-square error of 0.38 mm day-1 and a mean bias
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error of -0.05 mm day-1. Likewise, Figure 8 shows a good agreement between the daily
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evaporation rates estimated using the eddy covariance model (Eq. 07) and DYRESM. The
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coefficient of determination was 0.85 with a root-mean-square error of 0.57 mm day-1 and a
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mean bias error of -0.20 mm day-1. Based on these results, DYRESM was considered an
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appropriate model for the prediction of daily evaporation rates from Logan’s Dam.
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Figure 7. Relationship between daily evaporation rates estimated using the water
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balance method (x-axis) and DYRESM (y-axis). The solid line indicates the regression
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equation, and the dashed line represents the 1:1 line. Only periods without inflows
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and outflows were considered to minimise errors.
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Figure 8. Relationship between daily evaporation rates estimated using the eddy
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covariance model (x-axis) and DYRESM (y-axis). The solid line indicates the
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regression equation, and the dashed line represents the 1:1 line.
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3.4. Modelled water temperatures
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3.4.1. Water temperatures in the baseline simulations (without artificial destratification)
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The modelled water temperatures of the baseline simulations (without artificial
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destratification) for the shallow, medium and deep lake scenarios are presented in Figure
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9(a), (d) and (g), respectively. The period of simulation was 29/09/2009 to 11/04/2010,
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covering spring, summer and autumn months.
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Figure 9. Modelled water temperatures under normal conditions (without artificial
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destratification) and under “weak” and “strong” artificial destratification conditions
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for a shallow (a—c), medium (d—f) and deep (g—i) lakes throughout the study period
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(29/09/2009 to 11/04/2010). In the “weak destratification” simulations, the number of
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ports was 1, and the air-flow rate per port was 0.001 m3 s-1 (continuous operation). In
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the “strong destratification” simulations, the number of ports was 40 and the air-flow
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rate per port was 1.0 m3 s-1 (continuous operation). For reference, in Australia,
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September, October and November are spring months; December, January and
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February are summer months; and March and April are autumn months.
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Persistent thermal stratification clearly developed in the medium and deep lake
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cases, while the shallow lake was well mixed for almost the entire duration of the study
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period. For the medium and deep lakes, the strongest thermal stratification levels occurred
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between the beginning of December 2009 and the end of February 2010 (summer months).
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This was particularly due to high air temperatures and incoming solar radiation rates
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observed during this period. For both medium and deep lakes, the average depth of the
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well-mixed layer was 0.8 m, reflecting a low level of mixing, and a high level of
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stratification in these lakes. This is opposed to the average depth of the well-mixed layer in
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the shallow lake, which was 3.3 m, showing a high level of mixing and a low level of
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thermal stratification. For the shallow lake, some level of thermal stratification was
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observed in the beginning of November 2009, the second week of December 2009 and in
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the beginning of March 2010, following periods of very low wind conditions (< 1.5 m s-1)
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and relatively high air temperatures (> 26oC), which led to the development of a
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temperature gradient in the metalimnion of about 4.0oC m-1. The difference between bottom
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and surface water temperatures in the shallow lake scenario, however, never exceeded
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5.0oC during the time of study. For the medium lake, the strongest thermal stratifications
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were observed on the 2nd and 20th of December 2009, 1st of February 2010 and from the
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27th of February to the 2nd of March 2010. During these days and periods, the temperature
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gradients in the thermocline were all above 6.0oC m-1. The maximum gradient occurred on
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the 2nd of March 2010 (6.5oC m-1). The differences in water temperature between the
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bottom and the surface of the medium lake ranged from 2.5oC to 13.3oC, with an average of
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8.6oC. For the deep lake case, the strongest thermal stratifications were observed on the 2nd
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of December 2009, between the 31st of January and the 3rd of February 2010, and on the
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26th of February 2010. During these days and periods, the temperature gradients in the
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thermocline were all above 7.0oC m-1, with a maximum of 8.2oC m-1 on the 1st of February
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2010. Similar to the medium lake scenario, the differences in water temperature between
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the bottom and the surface of the deep lake ranged from 2.5oC to 13.5oC, with an average of
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8.6oC.
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Due to the same heat forcing conditions, the water temperatures at the surface of the
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three lakes were very similar, ranging from 20.6oC to 31.1oC over the 195 days of study in
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the shallow lake scenario (with an average of 26.6oC), from 20.2oC to 31.0oC in the
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medium lake scenario (average of 26.3oC) and from 20.1oC to 31.1oC in the deep lake
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scenario (average of 26.3oC). For the three lakes, the highest surface water temperatures
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(>30oC) occurred in the second week of December 2009 (summer)– due to the occurrence
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of the highest air temperatures (>28.6oC) in the study period and relatively high solar
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radiation (around 305 W m-2) – and in the last week of January 2010 (summer) – due to the
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occurrence of the highest solar radiation values (> 320 W m-2) in the study period and
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relatively high air temperatures (around 27oC). The average temperature of the bottom
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water was 25.2oC in the shallow lake, and 17.6oC in the medium and deep lakes. These
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figures demonstrate, again, a high level of mixing in the shallow lake when compared with
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the medium and deep lakes.
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3.4.2. Water temperatures in the simulations with artificial destratification (continuous
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The modelled water temperatures of the simulations with artificial destratification
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for the shallow, medium and deep lake scenarios, and for a “weak” and a “strong”
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destratification cases, are presented in Figure 9 (b, e, h, c, f and i). In the “weak
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destratification” cases (Figure 9 - b, e and h), a 1-port destratification system with a per-
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simulation, was used. In the “strong destratification” cases (Figure 9 - c, f and i), a
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destratification system with 40 ports and an air-flow rate per port equal to 1.0 m3 s-1,
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operating continuously over the 195 days of simulation, was used. Simulations with
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intermediate levels of artificial destratification (i.e. number of ports equal to 5, 10 and 20;
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and air-flow rate per port equal to 0.01 m3 s-1, 0.05 m3 s-1 and 0.25 m3 s-1) were also
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performed, but have not been shown in the figures.
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For the medium and deep lake cases, which developed a strong thermal
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stratification structure under natural conditions, the significance of the effect of artificial
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destratification on lake temperatures was clearly visible, even when a low air-flow rate was
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used. In the “weak destratification” case, however, the time to achieve a homothermous
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condition was longer than this time in the “strong destratification” case, in which a
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homothermous condition was achieved within less than one day.
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Also noticeable from the graphs in Figure 9 is that, in the medium and deep lake
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cases, the artificial destratification system seemed to have promoted heating of the deep
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layers, rather than cooling of the surface layers, as was desired. While there was an
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effective breakdown of the thermocline (particularly in the “strong destratification” cases),
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which can be desirable from the point of view of water quality, the heating of the deep
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layers and the meaningless cooling of the surface layers are not desirable from the point of
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view of evaporation reduction (evaporation reduction will be discussed in more detail in the
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following section). The average surface water temperatures under “strong destratification”
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conditions were 22.0oC, 25.9oC, 28.0oC, 28.4oC, 27.6oC and 24.9oC in October, November,
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December, January, February and March, respectively, in the deep lake scenario, which did
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not differ significantly from the baseline surface water temperatures, calculated as 23.1oC,
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26.6oC, 27.9oC, 28.5oC, 27.2oC and 24.5oC in these months. The “strong destratification”
495
system, therefore, provided only slight temperature changes (-5%, -3%, 0%, 0%, 1%, 2%,
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respectively for the above months) in comparison with the baseline scenario, with higher
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reductions occurring in October and November (i.e. at the beginning of the operation of the
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destratification system, as it had been already demonstrated in a previous study – Helfer et
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al., 2011b).
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In the medium lake scenario, the surface temperatures under “strong
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destratification” were 22.6oC, 26.5oC, 28.1oC, 28.6oC, 27.4oC and 24.5oC in October,
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November, December, January, February and March, respectively. In the baseline
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simulation, these temperatures were 23.1oC, 26.7oC, 27.9oC, 28.5oC, 27.2oC and 24.5oC,
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respectively. The changes observed were, therefore, -2%, -1%, 1%, 0%, 1%, 0% in
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comparison with the baseline surface water temperatures of the medium lake.
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Under “weak destratification” conditions (1 port and 0.001 m3 s-1 air-flow rate per
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port), the surface temperatures were 23.1oC, 26.6oC, 27.9oC, 28.5oC, 27.3oC and 24.6oC in
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the medium lake scenario, and 23.1oC, 26.5oC, 27.7oC, 28.4oC, 27.1oC and 24.5oC in the
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deep lake scenario. These temperatures were virtually the same as the baseline
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temperatures, meaning that the “weak destratification” system did not alter the surface
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conditions of the water in comparison with the baseline conditions.
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A conclusion was drawn that the use of a “weak destratification” system in a
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thermally stratified reservoir (such as the medium and the deep lakes considered in this
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study) would not affect the surface water temperatures in comparison with that expected
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without the use of an artificial destratification system. Therefore, it could be inferred that
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evaporation rates would not be reduced with a “weak destratification” system. As shown in
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Figure 9 (e and h), “weak destratification” systems would mix the water at a much slower
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rate as compared with “strong destratification” systems, meaning that the “stock” of cold
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water at the bottom of the lake would take longer to be depleted with “weak
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“weak destratification” systems, these systems would not be strong enough to lift the cold
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water from the bottom to the surface of the reservoir, and thus promote temperature and
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evaporation reductions.
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Another important conclusion was that “strong destratification” systems operating
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in thermally stratified lakes, as opposed to “weak destratification” systems, would be
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effective in lifting the cold water from the bottom layers to the surface layers, effectively
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reducing the surface temperatures, which would probably translate to some level of
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evaporation reduction. This behaviour, however, would only occur at the beginning of the
529
operation of the destratification system, when the “stock” of cold water at the bottom of the
530
lake would be abundant. Within a short period, however, this stock would be depleted. This
531
postulation was apparent from Figure 9 (f and i), in which it can be seen that the
532
thermocline was disintegrated more rapidly in the “strong destratification” case than in the
533
“weak destratification” case. The rapid exhaustion of the “stock” of cold water under
534
“strong destratification” conditions would also mean that further surface water temperature
535
reductions would not be able to be achieved. These observations implied that there must
536
exist an optimum period of operation, or an operation strategy, of the destratification
537
system that will provide maximum temperature reductions and maximum evaporation
538
reductions. This supposition will be explored in sections 3.5.2 to 3.5.6 of this paper.
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For the shallow lake, which presented a homothermous regime throughout the study
540
period, the use of artificial destratification systems did not bring any noteworthy alteration
541
to the thermal behaviour of the lake. The temperatures were the same as the baseline
542
temperatures in all destratification simulations undertaken in this study. This behaviour was
543
expected as shallow lakes usually do not develop an apparent stratification structure, and
544
thus, they do not possess a large quantity of cold water in their bottom layers.
545
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546
3.5. Modelled evaporation rates
547
3.5.1. Baseline evaporation rates (without artificial destratification)
548
The modelled baseline evaporation rates from the shallow, medium and deep lake
549
scenarios are presented in Figure 10. Due to having the same boundary conditions, the daily
550
evaporation rates for the three scenarios were very similar. For the three lakes, the daily
551
evaporation rates varied from 1.0 mm on the 8th of March 2010 (autumn) to 11 mm on the
552
19th of January 2010 (summer). There were 10 clear peaks when the daily evaporation rates
553
were above 8.0 mm day-1. These peaks in evaporation resulted from a combination of high
554
air temperatures, wind speed, solar radiation fluxes and vapour pressure deficits. The 30-
555
day period with the highest evaporation rates was from 19th of November 2009 to 18th of
556
December, with a daily average of 6.2 mm day-1. The 7-day period with the highest
557
evaporation rates was from the 13th to the 19th of January 2010, with an average of 7.1 mm
558
day-1. Total evaporation for the 195 days was 906 mm in the shallow lake scenario, and 894
559
mm in both the medium and deep lake scenarios. The slightly higher total evaporation in
560
the shallow lake scenario was a result of the surface temperature in the shallow lake being
561
on average slightly warmer than in the deeper lakes.
562
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563
Figure 10. Modelled daily evaporation rates from the shallow, medium and deep lakes
564
throughout the study period (29/09/2009 to 11/04/2010). Baseline values (without
565
artificial destratification).
566
567
3.5.2. Evaporation rates in the simulations with artificial destratification (continuous
568
operation)
569
Figure 11 summarises the 195-day accumulated evaporation for different levels of
570
artificial destratification (i.e. combination of number of ports and air-flow rate per port) for
571
the medium (a) and deep (b) lake scenarios. Since the use of artificial destratification did
Lakes & Reservoirs
Lakes & Reservoirs
24
572
not induce temperature and evaporation changes in the shallow lake scenario, the results for
573
this scenario have been omitted from this point forward. A preliminary conclusion was
574
drawn in the previous section that the use of artificial destratification systems for the
575
purpose of evaporation reduction from lakes that experience homothermous regimes (which
576
is a typical occurrence in shallow lakes) would not be worthwhile.
577
578
Figure 11. Total evaporation as a function of the number of destratification ports and
579
air-flow rate per port for the medium and deep lake scenarios. In Figure (c), the blue
580
lines represent the total evaporation as a function of the number of ports, with a fixed
581
air-flow rate per port equal to 0.001 m3 s-1 (the lowest air-flow rate per port simulated
582
in this study). The red lines represent the total evaporation as a function of the
583
number of ports, with a fixed air-flow rate per port equal to 1.0 m3 s-1 (the highest air-
584
flow rate per port simulated in this study). The total evaporation as a function of the
585
number of ports for air-flow rates per port between 0.001 m3 s-1 and 1.0 m3 s-1 would
586
fall between the curves shown in the graph, although omitted in the figure. The
587
baseline evaporation (without artificial destratification) for both lakes was 894 mm
588
and is represented in the graph when the number of ports = 0.
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Page 24 of 51
As discussed in the previous section, the accumulated evaporation in the baseline
591
simulations (without artificial destratification) was 894 mm for both the medium and deep
592
lakes. Under artificial destratification conditions, for both lakes, the reduction in
593
evaporation increased with the increase in number of ports and air-flow rate per port. The
594
deep lake scenario presented higher evaporation reductions than the medium lake scenario
595
in all artificial destratification conditions simulated in this study. The total evaporation
596
from the medium lake ranged from 888 mm to 891 mm under the use of artificial
597
destratification systems. The lowest value of the range (888 mm) was achieved in the
598
simulation where the highest number of ports (40 ports) and the highest air-flow rate per
Lakes & Reservoirs
Page 25 of 51
25
599
port (1.0 m3 s-1) were assumed. In turn, the highest value of the range (891 mm) was
600
observed in the simulation with the lowest number of ports (1 port) and the lowest air-flow
601
rate per port (0.001 m3 s-1). These evaporation values represent water savings in the order
602
of 0.5% and 0.2%, respectively, which is likely not to justify the use of continuously
603
operating artificial destratification systems for the sole purpose of evaporation reduction in
604
reservoirs of this size.
605
In the deep lake simulations, the accumulated evaporation varied from 868 mm to
606
878 mm with the use of artificial destratification systems, representing savings from 2.9%
607
to 1.8%. It is important to note, however, that the highest reduction (2.9%), which could be
608
interpreted as a meaningful saving in regions where water is scarce and or/expensive,
609
would only be achieved with the use of a very high flow rate per port (1.0 m3 s-1) combined
610
with a large number of ports (40). This condition would require so much energy that
611
implementation of the system would be unlikely justifiable. It was, therefore, concluded
612
that the use of artificial destratification systems in continuous operation would be futile
613
because this operating strategy would only achieve slight evaporation reductions that are
614
unlikely to justify the investment and operational costs of the destratification system.
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Lakes & Reservoirs
615
Time series analyses of the lake temperatures and daily evaporation rates under
616
continuously operating destratification conditions showed that the reductions in surface
617
water temperatures and evaporation rates were more significant immediately after the
618
breakdown of the thermocline. Under “strong destratification” conditions, for example, the
619
breakdown of the thermocline occurred within a few hours from the beginning of operation
620
of the destratification system, and the evaporation reductions started to occur from this time
621
forward, sustaining for about 30 days. After 30 days, there was a period in which the
622
surface temperatures and evaporation rates became practically the same as the baseline
623
temperatures and evaporation rates, followed by a period (end of the period of simulation)
Lakes & Reservoirs
Lakes & Reservoirs
26
624
during which the surface temperatures and evaporation rates under destratification
625
conditions were higher than the baseline values. Under “weak destratification” conditions,
626
evaporation reductions began to be noted about 30 days after the beginning of the operation
627
of the systems. This delay was because the thermocline was dismantled more slowly under
628
these less intense destratification systems. The evaporation time series for these simulations
629
are presented in Figure 12.
630
631
Figure 12. Modelled daily evaporation rates from the medium (a) and deep (b) lakes
632
under continuous (uninterrupted) “weak destratification” conditions (1 port and per-
633
port air-flow rate = 0.001 m3 s-1) – green lines; “strong destratification” condition (40
634
ports and per-port air-flow rate = 1.0 m3 s-1) – red lines; and under baseline conditions
635
(without destratification) – blue lines.
ee
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636
637
The above observations suggested the hypothesis that destratification systems could
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achieve more significant evaporation reductions if they were set to operate over a given
639
period of time, and then switched off for the rest of the period to avoid the increased
640
evaporation rates at the end of their operation. This hypothesis was tested by undertaking a
641
number of simulations that considered a number of different operating strategies, and the
642
results are presented in the next sections. Since the medium lake scenario presented only
643
minimal differences in relation to the baseline evaporation rates, attention in the next
644
sections is devoted only to the deep lake scenario.
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Page 26 of 51
645
646
3.5.3. Evaporation under the use of a destratification system, operating continuously over a
647
30-day period
648
649
Figure 13 shows the modelled time series of daily evaporation rates with the use of
a destratification system set to operate for six different 30-day periods (October, November,
Lakes & Reservoirs
Page 27 of 51
27
650
December, January, February and March). The principle behind this strategy was that
651
destratification would cause reductions in evaporation when in operation, avoiding the
652
increase in evaporation at the end of the simulation period, as observed in the simulations
653
with continuous operation. The destratification system design chosen for these simulations
654
was the one with 40 ports and air-flow rate per port equal to 0.001 m3 s-1. This design was
655
selected based on the curves shown in Figure 11. This graph suggested that a
656
destratification design with a large number of ports and a low air-flow rate per port would
657
bring similar savings to a design with one port only and a high air-flow rate per port. In
658
order to achieve an effective mixing, the use of a low number of ports would require high
659
air-flow rates per port, a condition that would also involve a significant consumption of
660
energy. Conversely, the use of a large number of ports, uniformly distributed in the bottom
661
of the reservoir, would still provide effective mixing even with a very low air-flow rate, and
662
incur much less energy consumption.
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664
Figure 13. Modelled daily evaporation rates from the deep lake with the use of a
665
destratification system in continuous operation for 30 days in October (a), November
666
(b), December (c), January (d), February (e) and March (f) – red lines. The baseline
667
evaporation time series are represented by the blue lines.
668
669
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Lakes & Reservoirs
Figure 13 clearly shows that evaporation reductions took place in times when
670
artificial destratification systems were in operation. However, these reductions were only
671
sustained for the duration of the operation of the destratification systems, reaching the same
672
rates as the baseline rates after they were switched off. The reductions varied from 1.5%
673
(operation in October) to 2.3% (operation in January). The cooling of the surface by the
674
destratification system had a better effect when in operation in January due to the higher
675
atmospheric evaporative demand in this month. The lowest reductions in evaporation were
Lakes & Reservoirs
Lakes & Reservoirs
28
676
found when the system operated in October and in March. This is because the evaporative
677
demand in these months was not as high, and the displacement of cold bottom water to the
678
surface did not generate the desired effect. Despite the reductions noted in these simulations
679
with monthly operation of the destratification systems, a conclusion was drawn that these
680
reductions would not justify investment and operation costs.
681
682
3.5.4. Evaporation under the use of a destratification system operating in monthly and
683
weekly cycles
684
Fo
Further simulations were performed with operation strategies based on weekly and
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monthly cycles. In the monthly cycles, the destratification system was in continuous
686
operation for periods of 30 days, separated by interruptions of 30 days between periods of
687
operation. In the weekly cycles, the system was in operation for periods of 7 days,
688
separated by interruptions of 7 days. The destratification system design adopted in the
689
simulations was the same as in the previous operation strategy (40 ports and air-flow rate
690
per port equalling 0.001 m3 s-1). The principle behind the operation in cycles was that the
691
system would provide evaporation reductions when in operation, and thermal stratification
692
would develop in the lake during the breaks. In this way, at the beginning of each operation
693
period, there would be a good source of cold water to bring about surface temperature and
694
evaporation reductions.
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Page 28 of 51
695
The operation in cycles yielded better results than the previous operation strategy;
696
however, the reductions would still be unlikely to justify investment and operational costs
697
of the destratification system. The reductions were 2.4% for the weekly cycles, and 2.6%
698
for the monthly cycle. The time series of evaporation rates for these simulations are
699
presented in Figure 14.
700
Lakes & Reservoirs
Page 29 of 51
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701
Figure 14. Modelled daily evaporation rates from the deep lake with the use of a
702
destratification system operating in monthly (a) and weekly (b) cycles – red lines. The
703
baseline evaporation time series are represented by the blue lines.
704
705
706
3.5.5. Evaporation under the use of a destratification system operating in pulses
The principle behind this operation strategy is that a strong injection of air, with
707
high intensity and short duration, would break down the thermocline and lift the cold water
708
from the bottom to the surface in a short period, reducing evaporation rates. Then, the
709
system would be interrupted for a period of time long enough to allow the re-establishment
710
of a stratification structure with accumulation of cold water at the bottom. After this period,
711
a pulse of destratification would be provided again. Two different strategic options were
712
tested: the first had a high frequency (the system would be turned on every fortnight) and
713
low intensity (0.01 m3 s-1) pulse; the other had a pulse with low frequency (every month)
714
and high intensity (0.05 m3 s-1). The number of ports was left constant at 40 ports per
715
diffuser, and the duration of the pulse was set as 1 day for both cases. The results are shown
716
in Figure 15.
iew
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Lakes & Reservoirs
718
Figure 15. Modelled daily evaporation rates from the deep lake with the use of a
719
destratification system operating in pulses: (a) higher frequency (fortnightly) and
720
lower intensity (0.01 m3 s-1) pulse; (b) lower frequency (monthly) and higher intensity
721
(0.05 m3 s-1) pulse – red lines. The baseline evaporation time series are represented by
722
the blue lines.
723
724
It was observed that the operation of the destratification system in pulses caused
725
reduction in evaporation rates, particularly immediately after the operation and in months
726
of high evaporative demands. In general, the reductions were sustained from November
Lakes & Reservoirs
Lakes & Reservoirs
30
727
until mid-February in the scenario with fortnightly pulses, but the total reduction was only
728
2.3% in comparison with the baseline scenario. In the scenario with monthly pulses, the
729
reductions were sustained for about two weeks after the pulse, reaching the same
730
evaporation rates as the baseline rates after this time. The total reduction was also 2.3%.
731
Neither way is likely to justify the adoption of these operation strategies.
732
733
3.5.6. Evaporation under the use of a destratification system operating in November and
734
January, and in December and February
735
Fo
A final operation strategy was tested, with the destratification system in operation
rP
736
only in months when the evaporative demands were high. The first option was with the
737
destratification system operating in November and January. The second option was with the
738
destratification system operating in December and February. For both options, the number
739
of ports was 40, and the air-flow rate per port was 0.001 m3 s-1. The results are presented in
740
Figure 16.
ev
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741
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742
Figure 16. Modelled daily evaporation rates from the deep lake with the use of a
743
destratification system operating in November and January (a), and in December and
744
February (b) – red lines. The baseline evaporation time series are represented by the
745
blue lines.
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Page 30 of 51
746
747
Reductions in evaporation occurred in both cases, being 2.2% for the first case
748
(destratification system operated in November and January), and 2.3% for the second case
749
(destratification system operated in December and February). The reductions were more
750
significant in the first month of operation (i.e. in November for the first case, and in
751
December for the second case). The reductions were not significant in the second month of
752
operation (i.e. in January for the first case, and in February for the second case). There were
Lakes & Reservoirs
Page 31 of 51
31
753
slight increases in evaporation after the second month of operation due to the increase in
754
water temperatures brought about by the mixing device. Since only minor reductions were
755
reached with these operation strategies, it was concluded that it would not be worthwhile to
756
use these strategies for evaporation reduction.
757
Table 2 summarises the results of this study, showing the overall evaporation
758
reductions achieved from different destratification operating strategies adopted in the deep-
759
lake scenario.
760
Fo
761
Table 2. Summary of the overall evaporation reductions achieved with the use of
762
various destratification operating strategies. Note: Only the most promising strategies
763
are displayed in the table, and the results are for the deep lake scenario only.
764
765
4. Conclusions
rR
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766
The effectiveness of artificial destratification by air-bubble plumes in reducing
767
evaporation from reservoirs was investigated in this study. The following conclusions were
768
drawn:
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Lakes & Reservoirs
• Artificial destratification systems will reduce evaporation rates if employed in
770
reservoirs that experience thermal stratification and have an abundant ‘stock’ of cold
771
water at the bottom. The reduction in evaporation rates can be higher in deeper
772
reservoirs, because these reservoirs can supply larger volumes of colder water for
773
mixing with surface waters;
774
• The reductions in evaporation rates will increase with the increase in number of ports
775
and with the increase in air-flow rate per port of the destratification system. Although
776
this hypothesis requires testing, increasing the number of ports and keeping a low air-
Lakes & Reservoirs
Lakes & Reservoirs
32
777
flow rate per port is probably more advantageous than reducing the number of ports
778
and increasing the air-flow rate per port, due to the first option requiring less energy;
779
• There was only a small variation in evaporation reduction depending on the operating
780
strategy of the artificial destratification system. The operating strategy that brought
781
about higher evaporation reductions (2.9%) was the continuous operation system with
782
40 ports and air flow rate per port equal to 1.0 m3 s-1;
783
• While seeming small, water savings from destratification systems may be significant
784
in countries where water is scarce and the production of fresh water is highly
785
expensive. A study to investigate energy requirements and the economic feasibility of
786
the most promising destratification designs and operation strategies (as shown in
787
Table 2) is therefore a recommendation for further research.
5. Acknowledgements
ee
Funding for this project was provided by the Griffith School of Engineering and
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789
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790
Built Environment (Australia) and the Conselho Nacional de Desenvolvimento Científico e
791
Tecnológico – CNPq (Brazil) grant number 203576/2014-4. The authors acknowledge the
792
support from the Griffith Climate Change Response Program (GCCRP), and thank the
793
Centre for Water Research at the University of Western Australia for providing the model
794
DYRESM, and the Urban Water Research Security Alliance for supplying the observed
795
data and measurements.
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6. References
798
Abtew W. (2001). Evaporation estimation for Lake Okeechobee in South Florida. Journal
799
800
of Irrigation and Drainage Engineering, 127(3), 140-147.
Anderson M.A., Komor A. & Ikehata, K. (2014). Flow routing with bottom withdrawal to
801
improve water quality in Walnut Canyon Reservoir, California. Lake and Reservoir
802
Management, 30, 131-142.
803
804
805
large water storages. Agricultural Water Management, 95(4), 339-353.
Beduhn R. J. (1994). The effects of destratification aeration on five Minnesota lakes. Lake
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Barnes G. T. (2008). The potential for monolayers to reduce the evaporation of water from
Fo
and Reservoir Management, 9(1), 105-110.
807
Bertone E., Stewart R. A., Zhang H. & O’Halloran K. (2015). Analysis of the mixing
808
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926
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Page 39 of 51
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Figure 1. Aerial view of the agricultural reservoir investigated in the current study (from Google Earth) and
its location in Australia. Location of centre of reservoir: 27o34’26’’S, 152o20’27’’E. Surface area: 17
hectares (170,000 m2).
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Incident solar radiation
W m -2
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Air temperature
30
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C
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m s -1
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kPa
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mm
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11-Apr-2010
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Page 40 of 51
Figure 2. Average daily shortwave solar radiation, air temperature, wind speed, vapour pressure deficit and
total daily rainfall at Logan’s Dam throughout the study period. For reference, in Australia, the spring months
are September, October and November; summer months are December, January and February; autumn
months are March, April and May; and winter months are June, July and August.
Lakes & Reservoirs
Page 41 of 51
Measured water temperatures
b)
Depth (m)
Depth (m)
a)
25
Modelled water temperatures
28
23
28
25
28
Figure 3. Isotherms for Logan’s Dam during the calibration period (29/09/2009 to 06/01/2010). This period
covers the spring months of October and November, and the summer month of December and part of
January. a) Measured data. b) Modelled data using DYRESM.
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24
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y = 0.99x + 0.19
R2 = 0.96
RMSE = 0.57oC
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Measured Water Temperature (oC)
32
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Figure 4. Relationship between daily averages of measured (x-axis) and modelled (y-axis) surface water
temperatures for the calibration period. The solid line indicates the regression equation, and the dashed line
represents the 1:1 line.
Lakes & Reservoirs
Lakes & Reservoirs
a)
Measured water temperatures
b)
Modelled water temperatures
Depth (m)
24
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3
29 0
29
Depth (m)
29
26
25
Figure 5. Isotherms for Logan’s Dam during the validation period (07/01/2010 to 11/04/2010). This period
covers the summer months of January and February, and the autumn month of March and part of April. a)
Measured data. b) Modelled data using DYRESM.
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24
20
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y = 1.02x + -0.44
R2 = 0.97
RMSE = 0.40oC
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Figure 6. Relationship between daily averages of measured (x-axis) and modelled (y-axis) surface water
temperatures for the validation period. The solid line indicates the regression equation, and the dashed line
represents the 1:1 line.
Lakes & Reservoirs
10
y = 0.80x + 1.03
R2 = 0.72
RMSE = 0.38 mm/day
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6
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10
Evaporation Rates from Water Balance (mm/day)
Figure 7. Relationship between daily evaporation rates estimated using the water balance method (x-axis) and
DYRESM (y-axis). The solid line indicates the regression equation, and the dashed line represents the 1:1
Fo
line. Only periods without inflows and outflows were considered to minimise errors.
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R2 = 0.85
RMSE = 0.57 mm/day
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Evaporation Rates from DYRESM (mm/day)
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Evaporation Rates from EC Model (mm/day)
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Figure 8. Relationship between daily evaporation rates estimated using the eddy covariance model (x-axis)
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Evaporation Rates from DYRESM (mm/day)
Page 43 of 51
and DYRESM (y-axis). The solid line indicates the regression equation, and the dashed line represents the
1:1 line.
Lakes & Reservoirs
Lakes & Reservoirs
o
o
o
b) Water temperature ( C) for initial water depth = 6.5 m.
Weakest artificial destratification condition
c) Water temperature ( C) for initial water depth = 6.5 m.
Strongest artificial destratification condition
6
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Depth (m)
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a) Water temperature ( C) for initial water depth = 6.5 m.
Baseline simulation (without artificial destratification)
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Baseline simulation (without artificial destratification)
01-Oct-2009
04-Dec-2009
06-Feb-2010
11-Apr-2010
01-Oct-2009
o
04-Dec-2009
06-Feb-2010
11-Apr-2010
o
e) Water temperature ( C) for initial water depth = 11.5 m.
Weakest artificial destratification condition
f) Water temperature ( C) for initial water depth = 11.5 m.
Strongest artificial destratification condition
10
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04-Dec-2009
06-Feb-2010
11-Apr-2010
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Baseline simulation (without artificial destratification)
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11-Apr-2010
01-Oct-2009
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11-Apr-2010
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Depth (m)
8
06-Feb-2010
i) Water temperature ( C) for initial water depth = 16.5 m.
Strongest artificial destratification condition
12
10
04-Dec-2009
o
h) Water temperature ( C) for initial water depth = 16.5 m.
Weakest artificial destratification condition
Depth (m)
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Depth (m)
04-Dec-2009
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11-Apr-2010
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Figure 9. Modelled water temperatures under normal conditions (without artificial destratification) and under
“weak” and “strong” artificial destratification conditions for a shallow (a—c), medium (d—f) and deep (g—
ev
i) lakes throughout the study period (29/09/2009 to 11/04/2010). In the “weak destratification” simulations,
the number of ports was 1, and the air-flow rate per port was 0.001 m3 s-1 (continuous operation). In the
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Page 44 of 51
“strong destratification” simulations, the number of ports was 40 and the air-flow rate per port was 1.0 m3 s-1
(continuous operation). For reference, in Australia, September, October and November are spring months;
December, January and February are summer months; and March and April are autumn months.
Lakes & Reservoirs
Page 45 of 51
Evaporation (mm day-1)
12
Shallow
Medium
Deep
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02-Nov-2009
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11-Apr-2010
Figure 10. Modelled daily evaporation rates from the shallow, medium and deep lakes throughout the study
period (29/09/2009 to 11/04/2010). Baseline values (without artificial destratification).
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Lakes & Reservoirs
a)
Total evaporation (mm) – Medium lake
10
0
b)
890
Total evaporation (mm) – Deep lake
10
0
890
885
Air-flow rate (m s )
-1
880
10
875
-2
10
-1
3
10
3
-1
Air-flow rate (m s )
885
-1
880
10
875
-2
870
10
c)
-3
10
20
30
Number of ports
40
870
10
865
-3
10
20
30
Number of ports
40
865
Evaporation as a function of number of ports for various air-flow rates and two reservoir depths
900
Fo
Air-flow rate = 0.001 m3/s per port
Air-flow rate = 1.0 m3/s per port
890
Medium lake curves
885
880
875
870
10
Deep lake curves
rR
865
0
ee
Total evaporation (mm)
895
rP
20
Number of ports
30
40
ev
Figure 11. Total evaporation as a function of the number of destratification ports and air-flow rate per port
for the medium and deep lake scenarios. In Figure (c), the blue lines represent the total evaporation as a
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Page 46 of 51
function of the number of ports, with a fixed air-flow rate per port equal to 0.001 m3 s-1 (the lowest air-flow
rate per port simulated in this study). The red lines represent the total evaporation as a function of the number
of ports, with a fixed air-flow rate per port equal to 1.0 m3 s-1 (the highest air-flow rate per port simulated in
this study). The total evaporation as a function of the number of ports for air-flow rates per port between
0.001 m3 s-1 and 1.0 m3 s-1 would fall between the curves shown in the graph, although omitted in the figure.
The baseline evaporation (without artificial destratification) for both lakes was 894 mm and is represented in
the graph when the number of ports = 0.
Lakes & Reservoirs
Page 47 of 51
a) Destratification system in continuous operation over 195 days - Medium Lake
12
Weakest destratification condition (1 port, 0.001 m3s -1 per port). Total = 891 mm (-0.2%)
10
Strongest destratification condition (40 ports, 1.0 m3s -1 per port. Total = 888 mm (-0.5%)
No destratification (baseline simulation). Total = 894 mm
mm day -1
8
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
b) Destratification system in continuous operation over 195 days - Deep Lake
12
Weakest destratification condition (1 port, 0.001 m3s -1 per port). Total = 878 mm (-1.8%)
10
Strongest destratification condition (40 ports, 1.0 m3s -1 per port. Total = 868 mm (-2.9%)
No destratification (baseline simulation). Total = 894 mm
mm day-1
8
Fo
6
4
2
0
01-Oct-2009
02-Nov-2009
ee
rP
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
Figure 12. Modelled daily evaporation rates from the medium (a) and deep (b) lakes under continuous
rR
(uninterrupted) “weak destratification” conditions (1 port and per-port air-flow rate = 0.001 m3 s-1) – green
lines; “strong destratification” condition (40 ports and per-port air-flow rate = 1.0 m3 s-1) – red lines; and
ev
under baseline conditions (without destratification) – blue lines.
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Lakes & Reservoirs
a) Destratification system in operation in October
12
Evaporation with destratification.
Total = 881 mm
Evaporation without destratification. Total = 894 mm
mm day -1
10
8
Change = -1.5%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
b) Destratification system in operation in November
12
Evaporation with destratification.
Total = 875 mm
Evaporation without destratification. Total = 894 mm
mm day -1
10
8
Change = -2.2%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
c) Destratification system in operation in December
12
Evaporation with destratification.
Total = 874 mm
Evaporation without destratification. Total = 894 mm
mm day-1
10
Fo
8
6
4
2
0
01-Oct-2009
02-Nov-2009
Change = -2.2%
rP
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
d) Destratification system in operation in January
12
ee
mm day-1
10
8
6
4
2
02-Nov-2009
04-Dec-2009
Change = -2.3%
05-Jan-2010
06-Feb-2010
ev
0
01-Oct-2009
Evaporation with destratification.
Total = 874 mm
Evaporation without destratification. Total = 894 mm
rR
10-Mar-2010
11-Apr-2010
e) Destratification system in operation in February
12
Evaporation with destratification.
Total = 875 mm
Evaporation without destratification. Total = 894 mm
mm day -1
10
iew
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4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
Change = -2.2%
10-Mar-2010
11-Apr-2010
f) Destratification system in operation in March
12
Evaporation with destratification.
Total = 876 mm
Evaporation without destratification. Total = 894 mm
10
mm day -1
1
2
3
4
5
6
7
8
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8
Change = -2.1%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
Figure 13. Modelled daily evaporation rates from the deep lake with the use of a destratification system in
continuous operation for 30 days in October (a), November (b), December (c), January (d), February (e) and
March (f) – red lines. The baseline evaporation time series are represented by the blue lines.
Lakes & Reservoirs
Page 49 of 51
a) Destratification system operated in monthly cycles
12
Evaporation with destratification.
Total = 871 mm
Evaporation without destratification. Total = 894 mm
mm day -1
10
8
Change = -2.6%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
b) Destratification system operated in weekly cycle
12
Evaporation with destratification.
Total = 873 mm
Evaporation without destratification. Total = 894 mm
mm day-1
10
8
Change = -2.4%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
Figure 14. Modelled daily evaporation rates from the deep lake with the use of a destratification system
Fo
operating in monthly (a) and weekly (b) cycles – red lines. The baseline evaporation time series are
rP
represented by the blue lines.
ee
a) Destratification system operated in fortnightly pulses
12
mm day -1
10
8
6
4
02-Nov-2009
04-Dec-2009
Change = -2.3%
ev
2
0
01-Oct-2009
Evaporation with destratification.
Total = 873 mm
Evaporation without destratification. Total = 894 mm
rR
05-Jan-2010
06-Feb-2010
10-Mar-2010
iew
11-Apr-2010
b) Destratification system operated in monthly pulses
12
Evaporation with destratification.
Total = 873 mm
Evaporation without destratification. Total = 894 mm
10
mm day-1
1
2
3
4
5
6
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6
Change = -2.3%
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
Figure 15. Modelled daily evaporation rates from the deep lake with the use of a destratification system
operating in pulses: (a) higher frequency (fortnightly) and lower intensity (0.01 m3 s-1) pulse; (b) lower
frequency (monthly) and higher intensity (0.05 m3 s-1) pulse – red lines. The baseline evaporation time
series are represented by the blue lines.
Lakes & Reservoirs
Lakes & Reservoirs
a) Destratification system operated in November and January
12
Evaporation with destratification.
Total = 874 mm
Evaporation without destratification. Total = 894 mm
mm day -1
10
8
Change = -2.2%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
b) Destratification system operated in December and February
12
Evaporation with destratification.
Total = 874 mm
Evaporation without destratification. Total = 894 mm
mm day-1
10
8
Change = -2.3%
6
4
2
0
01-Oct-2009
02-Nov-2009
04-Dec-2009
05-Jan-2010
06-Feb-2010
10-Mar-2010
11-Apr-2010
Figure 16. Modelled daily evaporation rates from the deep lake with the use of a destratification system
Fo
operating in November and January (a), and in December and February (b) – red lines. The baseline
rP
evaporation time series are represented by the blue lines.
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Table 1. Summary of the simulations with the artificial destratification system operating continuously over
the period of study.
Simulation name1
Initial water depth (m)
Initial water temperature
Number of ports
Air-flow rate per port
(m3 s-1)
SHAL_N_AFR
DEEP_N_AFR
MEDI_N_AFR
6.5
11.5
16.5
17.8oC throughout column
17.8oC throughout column
17.8oC throughout column
1, 5, 10, 20, 40
1, 5, 10, 20, 40
1, 5, 10, 20, 40
0 (baseline), 0.001, 0.01, 0.05,
0 (baseline), 0.001, 0.01, 0.05,
0 (baseline), 0.001, 0.01, 0.05,
0.25, 1.0
0.25, 1.0
0.25, 1.0
Continuous operation for over
Continuous operation for over
Continuous operation for over
Operation strategy of the
the period of simulation (195
the period of simulation (195
the period of simulation (195
destratification system
days)
days)
days)
1
In the simulation name, N refers to the number of ports and AFR to the air-flow rate per port. For example, the simulation
SHAL_10_005 refers to the shallow lake scenario, with a 10-port destratification system and air-flow rate per port equal to 0.05 m3
s-1. The baseline simulations (without the use of destratification) are represented by the simulations with air-flow rate = 0.
Table 2. Summary of the overall evaporation reductions achieved with the use of various destratification
Fo
operating strategies. Note: Only the most promising strategies are displayed in the table, and the results are
for the deep lake scenario only.
Operation strategy
rP
195-day continuous operation, 1 port, 0.001 m3 s-1 per port
195-day continuous operation, 1 port, 1.0 m3 s-1 per port
195-day continuous operation, 40 ports, 0.001 m3 s-1 per
port
195-day continuous operation, 40 ports, 1.0 m3 s-1 per port
30-day continuous operation in January (summer), 40
ports, 0.001 m3 s-1 per port
30-day continuous operations followed by 30 days
interruptions, 40 ports, 0.001 m3 s-1 per port
7-day continuous operations followed by 7-day
interruptions, 40 ports, 0.001 m3 s-1 per port
Fortnightly destratification pulses of 1-day duration, 40
ports, 0.01 m3 s-1 per port
Monthly destratification pulses of 1-day duration, 40 ports,
0.05 m3 s-1 per port
Continuous operation in November and January, 40 ports,
0.001 m3 s-1 per port
Continuous operation in December and February, 40 ports,
0.001 m3 s-1 per port
Overall evaporation reduction
(deep lake case)
2.2%
2.5%
Section in which operation
strategy is discussed
3.5.2
3.5.2
2.7%
3.5.2
2.9%
3.5.2
2.3%
3.5.3
2.6%
2.4%
3.5.4
3.5.4
2.3%
3.5.5
2.3%
3.5.5
2.2%
3.5.6
2.3%
3.5.6
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