Long-term energy flux measurements over an irrigation water storage using scintillometry

Agricultural and Forest Meteorology 168 (2013) 93–107

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Long-term energy flux measurements over an irrigation water storage using scintillometry David McJannet a,∗, Freeman Cook b,a, Ryan McGloin c, Hamish McGowan c, Stewart Burn d, Brad Sherman e a

CSIRO Land and Water, EcoSciences Precinct, Dutton Park, Queensland, Australia Freeman Cook and Associates, 52 Estate Road, Jamboree Heights, Queensland, Australia c Climate Research Group, School of Geography, Planning and Environmental Management, University of Queensland, St. Lucia, Queensland, Australia d CSIRO Land and Water, Highett, Victoria, Australia e CSIRO Land and Water, Christian Laboratory, Black Mountain, Australian Capital Territory, Australia b

a r t i c l e

i n f o

Article history: Received 4 April 2012 Received in revised form 31 August 2012 Accepted 31 August 2012 Keywords: Scintillometer Sensible heat flux Latent heat flux Evaporation Energy budget

a b s t r a c t An analysis is presented of the long-term energy balance of a small water body in south-east Queensland, Australia. The main focus of this study was on the use of scintillometry to determine the turbulent fluxes of sensible and latent heat. A novel approach is utilized for identifying periods where the scintillometry measurement footprint extends beyond the water surface. This approach relies on comparison of ‘inferred’ water surface temperature and measured skin temperature. The ‘inferred’ temperature is an independent assessment of water skin temperature derived through rearrangement of key equations in the scintillometry calculation scheme. An extensive dataset is used to investigate the processes controlling heat and vapour fluxes and to develop simple relationships that can be used for reliable predictions. These relationships are used to fill missing measurements in the dataset and to construct a complete energy balance for an 18 month period. The long-term data set is used to describe the diurnal, seasonal and annual variations in energy fluxes and to explore issues related to energy balance closure. Average energy balance closure across the study was 82%, however closure was much better during the winter than the summer. The key factors likely to lead to errors in energy balance closure are considered and it is concluded that the most likely causes are underestimation of latent heat fluxes, advection of energy that is not measured by the scintillometer, or overestimation of net radiation. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.

1. Introduction The thermal and dynamic properties of inland water bodies make them important stores of energy in a terrestrial landscape. Heat transfer within water bodies is possible by conduction, radiation, convection and advection (Oke, 1987) and understanding the relative importance of the transfer mechanisms is important for understanding energy cycling within lakes. Energy transfer and utilization mechanisms play an important role in the growth and development of aquatic ecosystems and all biological and physical activities that take place in a body of water require some sort of energy, the main source of which is solar radiation reaching its surface. The stored energy in a water body is indicated by its water temperature, which in turn has a strong influence on ∗ Corresponding author. Tel.: +61 7 3833 5584. E-mail addresses: [email protected] (D. McJannet), [email protected] (F. Cook), [email protected] (R. McGloin), [email protected] (H. McGowan), [email protected] (S. Burn), [email protected] (B. Sherman).

water quality and biological and chemical processes (Gianniou and Antonopoulos, 2007). Understanding of the energy fluxes across a water body surface is also essential for understanding the processes controlling latent heat flux, i.e. evaporation losses. Water loss from farm dams through evaporation can represent a significant proportion of the water harvested (Falkenmark et al., 1998; Mugabe et al., 2003; Wallace and Gregory, 2002) and, hence, this loss has an impact on farm revenue (Condie and Webster, 1995; Craig, 2006). Accurate quantification of evaporation from water storages is required for water resource management however evaporation is one of the most difficult terms of the water balance to accurately quantify. Water bodies can influence climates at a range of scales (e.g. Bonan, 1995; Long et al., 2009), however the effect of lakes is currently either neglected or parameterized crudely in numerical weather prediction (Nordbo et al., 2011). A better understanding of the energy balance of water bodies is essential for climate and weather predictions and for understanding potential impacts of climate variability (Blanken et al., 2000; Liu et al., 2009; Spence et al., 2003; Subin et al., 2012).

0168-1923/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agrformet.2012.08.013

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Exchanges of energy between a water body and the surrounding environment can be described by determining the surface energy balance which is given by: Rn − Sw − Sa + Qr − Qp − Qs − H − E = 0,

(1)

where Rn is the net radiation, Sw is the change in heat stored within the water column, Sa is the change in heat stored in the air column below the net radiation measurement height, Qr is the energy added through rainfall, Qp is energy addition or removal via inflows or outflows, Qs is energy transfer to sediments beneath the water, H is the sensible heat flux, and E is the latent heat flux. Heat flux into the water body is positive and heat flux out is negative. This energy balance equation pertains to a closed thermodynamic system where energy inputs are equal to energy outputs. It should be noted that the energy balance equation used here and in most other studies applies to homogenous sites and does not include a specific term for advection of energy from/to the surrounding landscape via sensible or evaporative heat flux. However, in some landscapes, such as irrigated areas and lakes which are surrounded by more arid areas, such fluxes can be important. This issue will be discussed later in the context of energy balance closure. Recently there have been a number of studies reporting turbulent fluxes (H and E) from water bodies of varying sizes from around the world using eddy covariance techniques (e.g. Blanken et al., 2000; Liu et al., 2009; McGowan et al., 2010; Nordbo et al., 2011; Tanny et al., 2008; Venäläinen et al., 1999). More recently, McJannet et al. (2011) have demonstrated how scintillometry might also be used to quantify turbulent fluxes over open water. The advantage of scintillometry is its ability to determine fluxes over a greater spatial extent. In this paper the potential for scintillometry to be used for long-term water body energy balance studies is explored. The analysis includes proposed improvements to calculation methodologies and explores the utilization of a novel technique for identifying issues associated with measurement footprints. Using concurrent measurements of all components of the energy balance, issues around energy balance closure are addressed based on analysis of 18 months of data for an irrigation water storage in south-east Queensland, Australia. 2. Materials and methods 2.1. Study site The study was conducted at an irrigation water storage known as Logan’s Dam (27◦ 34 25.93 S; 152◦ 20 27.45 E; altitude 88 m) in south-east Queensland, Australia. The dam has dimensions of 480 m × 350 m (Fig. 1) and holds 700 ML of water with a maximum depth of 6 m when full. Measurements were made over an 18 month period from 5 November 2009 to 5 May 2011. 2.2. Scintillometer sensible and latent heat flux measurements 2.2.1. Calculation procedures The instrument used to determine turbulent heat fluxes at the field site was a large aperture dual-disk scintillometer (LAS – BLS900, Scintec AG, Rottenburg, Germany). The transmitter and receiver were installed on opposing banks of the dam at a height of 1 m above the full storage level. At the commencement of measurements the scintillometer beam was orientated in a northwest–southeast direction (as shown in Fig. 1) however, during April 2010 the orientation was changed to a northeast–southwest direction. Measurement over the short path length was made possible by using path reduction apertures. The path length was 480 m for northwest–southeast alignment and 350 m for northeast–southwest alignment. The receiver measured scintillations in the electromagnetic beam from the two transmitters.

Measurements were made at 25 Hz with an averaging period of 10 min. Change in path height as water level varied was determined by using pressure transducers located around the dam. According to the manufacturers manual (Scintec, 2009) the sensible heat flux measurement range (in W m−2 ) for the BLS900 over the path length used in this study is between 2*zLAS and 5500*zLAS , where zLAS is the measurement path height. During the study the path height varied from 1 m to a maximum of 4 m, which corresponds to minimum measurable sensible heat flux of 2 W m−2 and 8 W m−2 , respectively. The upper limit for sensible heat flux measurements is never an issue. Given the small size of roughness elements over the water surface (approximate z0 range 0.0001–0.001 m), the scintillometer beam was positioned above the roughness sub-layer (approximated by 35z0 (Katul and Parlange, 1992)) and within the constant flux layer, thereby ensuring that measurements meet the requirements for free convection scaling. Propagation statistics determined at the receiver were used to calculate the structure parameter of the refractive index of air (Cn2 ) and this in turn was used to determine the temperature structure parameter (CT2 ) which is central to sensible heat flux estimates. Once an estimate of H has been made, an estimate of evaporation, E, is made using the ‘linearized-Bowen ratio’ approach which was proposed by Vercauteren et al. (2009) for determining evaporation from open water using sensible heat flux measurements. A detailed description of the calculation procedures developed for determining fluxes over open water using scintillometry is given by McJannet et al. (2011). Those interested in further details are directed to this paper. The only difference in the calculation procedures used in this paper and those previously presented (McJannet et al., 2011) is the introduction of a variable surface roughness value (z0 ). In McJannet et al. (2011) z0 was fixed at 0.0001 m however, longer term observations have revealed much greater variation in wind speed, therefore a variable z0 was included by specifying z0 using the relation first proposed by Zilitinkevich (1969) which has the following form: z0 = c1

v u∗

+

u2∗ c2 g

(2)

where v is kinematic viscosity, c1 = 1.75 and c2 = 81.1. 2.2.2. Data quality control Calculated fluxes were subjected to further quality control procedures to ensure data quality was maintained. Iterative calculations that failed to converge were removed. Flux estimates that produced a Bowen ratio (ˇ) of between 0 and −0.03 were excluded as these solutions result in invalid humidity corrections. For the analysis presented below measurements were excluded when relative humidity was greater than 95%; above this level capacitive humidity sensors become less reliable. Periods when average wind speed over a ten-minute period was less than 1 m s−1 were also excluded as below this wind speed Monin–Obukhov theory becomes questionable (Garratt, 1994). Measurements taken during periods of rain were also excluded. 2.2.3. Measurement footprint analysis Like eddy covariance measurements, scintillometry measurements have a measurement footprint and it is essential that this measurement footprint lies within the confines of the water surface. McJannet et al. (2011) presented a novel approach for footprint estimation on a confined water body which relied on comparison of ‘inferred’ and measured water skin temperature (Tw ). ‘Inferred’ water temperature (so-called because the scintillometry calculation procedures do not require water temperatures to be specified) is derived using the estimates of E in a rearrangement of Eq. (3) (determined after Penman (1948)) which allows determination of

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Fig. 1. Logan’s Dam field site and location of instrumentation. The two scintillometer beam paths represent the two alignments used at different times during the study. ∗ , from which T can be calculated using equations that relate ew w saturated vapour pressure to temperature (e.g. Lowe, 1977).

ea∗ − ea EA ∗ −e = E ew a

(3)

Measured skin temperature is derived from upwelling long wave radiation measurements (see below). The footprint analysis technique is based on the assumption that if accurate fluxes are measured from the water surface then ‘inferred’ and measured water temperatures should be similar. If the measured fluxes originate from the surrounding landscape then the fluxes are likely to be under- or over-estimated and as a result the ‘inferred’ temperature should deviate from that measured. To use differences in ‘inferred’ and measured water temperatures as an indicator for footprint issues it is essential to understand the natural variability in surface temperatures. At Logan’s Dam absolute differences in temperature data recorded at a depth of 10 cm at four locations around the dam (Fig. 1) were compared and these were used to set the temperature-based exclusion criteria. The temperature criteria represents a balance between excluding data which may exhibit differences due to real spatial variability in skin temperature and excluding data which are due to measurement footprint issues. The exclusion of data for footprint issues is based entirely on the failure to meet the defined allowable temperature difference criteria. 2.2.4. Determining the processes controlling fluxes The turbulent exchange of heat and water from a water body depends on a number of factors. It is well known that latent heat fluxes are largely controlled by vapour pressure deficits between the water and air above, and wind speed over the water. While

the sensible heat fluxes are largely controlled by the temperature difference between the water and air and the wind speed over the water. These findings have led to the development of commonly used bulk aerodynamic algorithms for modelling H and E over water:

H = cp CH u(Tw − Ta )

(4)

∗ − ea ) E = Le CE u(ew

(5)

where CH and CE are heat and vapour bulk transfer coefficients, ∗ and e are the vapour pressure at the water surface and in and ew a the air, respectively. As Eqs. (4) and (5) are controlled largely by ∗ − e ), the focus of analysis in this study will u(Tw − Ta ) and u(ew a be on these terms. If strong relationships exist between H and E and these controlling factors, as has been seen in other studies (e.g. Blanken et al., 2000; Liu et al., 2009; Nordbo et al., 2011), then these relationships offer a means by which to infill missing data points and allow long-term energy balance analysis to be undertaken. The advantage of such an approach is that estimates can be made using supplementary measurements that were being made at the dam. Using the extensive quality controlled dataset developed in this study these algorithms were tested across a range of conditions. For this analysis 20% of the available quality controlled data were randomly selected to develop these relationships. The strength of these relationships was then tested by comparing estimated and measured fluxes for the remaining 80% of the quality controlled dataset.

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2.3. Net radiation and meteorological measurements Net radiation above the water (Rn ) was determined by combining individual measurements of incoming and outgoing long wave and short wave radiation (CNR1, Kipp & Zonen, Delft, The Netherlands) which were taken at a height of 1.2 m from a floating weather station (Fig. 1). Other measurements from this platform included wind speed at 2.4 m (014A, MetOne, Oregon, USA), highaccuracy (±0.1 ◦ C) aspirated temperature measurements at heights of 0.4 and 3 m (41,342, RM Young), atmospheric pressure (CS106, Campbell Scientific, Utah, USA), and temperature and humidity at heights of 0.55 m and 2.55 m (CS215, Campbell Scientific, Utah, USA). The outgoing long wave radiation measurements were also used to derive water skin temperature. The aspirated temperature probes were used to determine the stability conditions of the near surface atmosphere (i.e. unstable or stable) which is required for scintillometry calculations. All sensors were sampled at 10 second intervals and averaged over ten minute periods. Surrounding the dam were supplementary weather stations which monitored a range of standard meteorological variables. On the eastern and western sides of the dam, weather stations monitored wind speed and direction (WindSonic, Gill Instruments, UK), solar radiation (Li 200x, LiCor, Lincoln, USA), and temperature and humidity (CS215, Campbell Scientific, Utah, USA) at a height of 3 m. On the northern and southern sides of the dam weather stations monitored wind speed and direction (03002 RM Young Wind Sentry Set, RM Young, Michigan, USA), solar radiation (Li 200x, LiCor, Lincoln, USA), and temperature and humidity (CS215, Campbell Scientific, Utah, USA) at a height of 3 m. The supplementary weather stations also collected and stored data from rain gauges and pressure transducers as described below. 2.4. Water temperature and heat storage Water temperature was measured using four thermistor chains distributed around the dam. The central thermistor chain (PME, California, USA) was suspended below the floating weather station. Water temperature was measured from a depth of 0.1–4.3 m at 0.3 m increments. The remaining thermistor chains (locations shown in Fig. 1) were custom built systems which used Thermometrics P60 thermistors (Thermometrics, New Jersey, USA). All thermistors data was averaged for 10 min periods. The central thermistor chain operated for the duration of the study while the remaining thermistor chains ran for intermittent periods (total of 77 days) during the study to help in the assessment of spatial heat storage variation. The average quantity of heat stored in the lake at a given time was calculated by dividing the lake into horizontal slices with a thermistor at the midpoint of each slice. The change in heat stored over the course of a day (Sw in W m−2 ) was determined from the difference between energy storage at the start of the day and at the end of the day (primed symbols):



Sw =

1 as t

 z

w cw Vz Tz −





w cw Vz Tz

(6)

z

where w is the density of water (kg m−3 ), cw is the specific heat of water (J kg−1 ◦ C−1 ), as is the average dam surface area (m2 ), Tz is the water temperature (◦ C) at depth z (m), t is the time interval (s), and Vz is the volume of the horizontal slice for depth z (m3 ). Loss of heat from the dam results in negative Sw . A survey of Logan’s dam was undertaken by G.L. Irrigation Pty Ltd. to relate its depth to volume and surface area. The survey was made using a dual frequency GPS (NovAtel RTK, NovAtel Inc., Alberta, Canada) with vertical accuracy of 0.02–0.04 m. Storage curves were developed by using cubic polynomials to relate water

level to surface area and volume. The regular shape and slope of the dam walls resulted in very strong relationships (r2 > 0.99) between depth and surface area and the depth and volume. The depth of the water in the dam was determined using pressure transducers (KPSI 501, Esterline, Bellevue, Washington) which were installed at four locations around the dam (Fig. 1). Water depth was measured every 10 s and averaged for 10 min periods. The pressure transducers had an accuracy of ±0.3 mm and a resolution of 0.003 mm. Daily depth changes were determined by subtracting average depth at the start of the day from average depth at the end of the day. All depths were related to a common datum from survey data for consistency. The change in heat stored in the air column below the net radiation measurement height (Sa ) was calculated using the method described by McCaughey (1985). Average daily change in Sa was calculated using temperature and humidity measurements from the profile on the floating weather station. 2.5. Energy loss through irrigation water pumping This irrigation storage has two pipes for pumping water from the sump surrounding the dam into the main storage (Fig. 1) and two pipes for distributing water from the main storage to the surrounding agricultural area. The two distribution pipelines were fitted with high accuracy (±0.2%) flow meters (Magflow Mag5100W, Siemens, Victoria, Australia) and transmitters (Mag6000, Siemens, Victoria, Australia) and total flow was recorded in 10 min intervals using a data logger (CR1000, Campbell Scientific, Utah, USA). One of the distribution pipes had a diameter of 200 mm and the other had a diameter of 250 mm. The temperature of water being extracted from the dam was monitored in the transport pipeline using a platinum resistance thermometer (PT100 RTD, OneTemp, Australia). The heat removed via irrigation water (Qp ) in W m−2 was calculated from: Qp =

w cw Vp Tp t as

(7)

where Vp is the volume of water pumped (m3 ) and Tp is the temperature of the pumped water (◦ C). Using this approach Qp is calculated as a positive value which is then subtracted from the available energy in the energy balance equation (1). The two input pipes are much larger with a diameter of 800 mm. These pipes move very large amounts of water and even the most accurate flow metering would result in high levels of flow uncertainty. For this reason it was decided that periods where these two pipes were activated would be ignored in the analysis. Fortunately, the input pipes are only active for one or two days at a time after periods of heavy rain (17 days in total for this study), whereas the smaller distribution pipes are active much more often and for much longer periods. 2.6. Energy addition from rainfall Direct water input to the storage from rainfall was monitored using a set of four tipping-bucket rain gauges (TB3, Hydrological Services, Sydney, Australia) which were distributed around the dam walls (Fig. 1). These rain gauges have an accuracy of ±2%. Daily rainfall was calculated as the average of these four gauges. Each rain gauge was located with one of the supplementary weather stations. The energy contribution from rainfall (Qr ) in W m−2 was calculated from: Qr = w cw

Pg Twb t

(8)

where Pg is the amount of rainfall (m) and Twb is the wet-bulb temperature of the air (◦ C).

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Fig. 2. Average daily relative humidity (a), air temperature (b), solar radiation (c) and wind speed (d), and total daily rainfall (e) and pumped water losses (f) throughout the study period. The circles in (f) indicate days where the dam was being filled through pumping.

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alignment due to the shrinking and swelling nature of the local soils (13%), or the presence of fog over the water which blocked signal transmission (3%); a common occurrence in the early morning. Of the data which was successfully collected 10% were removed for periods when average wind speed was <1 m s−1 , 7% were removed when relative humidity was >95%, 4% were removed for periods when rain was falling, 2% were removed for invalid ˇ values, and 1% were removed due to lack of convergence to a stable solution. Using the maximum tolerated difference between observed and ‘inferred’ water temperature to identify potential measurement footprint issues resulted in 14% of the data being removed. The conditions resulting in exclusion of data for measurement footprint issues are discussed below in further detail. After data quality control a total of 44,858 ten minute measurement periods remained which is equivalent to 63% of measurements.

Fig. 3. Wind rose for the study period showing predominant wind speeds and directions.

3. Results and discussion 3.1. Background Over the course of the 18 month study period the relative humidity remained high (average = 75%) with very little seasonal variation (Fig. 2a). Average daily air temperatures ranged from 13.9 ◦ C during winter to 24.6 ◦ C during summer with maximum and minimum recorded temperatures of 39.8 ◦ C and 1.4 ◦ C (Fig. 2b). Solar radiation ranged from 147 W m−2 during winter to 244 W m−2 during summer (Fig. 2c). Wind speed showed only weak seasonal variation with an average for the study of 2.7 m s−1 (Fig. 2d). The wind rose for the study period (Fig. 3) shows that the predominant wind direction is from a south easterly direction with winds from the north being very rare. Wind speeds less than 4 m s−1 were recorded for more than 80% of the duration of the study period while wind speeds greater than 6 m s−1 were very rare and accounted for only 4% of the observations. During the 18 months of the study 1761 mm of rain was recorded and 62% of this rain fell during the summer months (Fig. 2e). The highest daily rainfall was 224 mm which resulted in extensive flooding in the region. Loss of water from the dam to supply irrigation needs tended to occur during distinct periods which reflected the stage of the growing seasons and the occurrence of rainfall (Fig. 2f). Periods during the study where the dam was filled by pumping from the surrounding sump are shown as circles in Fig. 2f. The average volume of the dam during the study period was 509 ML (75% of capacity) but this ranged from 170 ML (25% of capacity) to 705 ML (105% of capacity i.e. >safe storage level). In this paper the summer months refer to the period covering December to February, autumn includes March–May, winter includes June–August, and spring includes September–November. 3.2. Sensible and latent heat flux 3.2.1. Scintillometer data quality control For the 18 month study period scintillometer measurements were missing for 16% of the time. The missing data included periods where the scintillometer transmitter or receiver went out of

3.2.2. Measurement footprint analysis In order to use differences in ‘inferred’ and measured water temperatures as a indicator for measurement footprint issues it is essential to understand the natural variability in surface temperatures and set a temperature exclusion criteria. At Logan’s Dam absolute differences in temperature data recorded at a depth of 10 cm at four locations around the dam (Fig. 1) were compared over a 77 day period and these were used to set the temperaturebased exclusion criteria. (N.B. While the central thermistor chain functioned for the entire study period the remaining thermistor chains provided reliable data for just 77 days.) The cutoff criteria for acceptable water temperature difference was set at two standard deviations from the mean which was calculated to be 1.48 ◦ C. The assumption is made that this variability reflects the variability which would be expected for the skin temperature, therefore an absolute difference between ‘inferred’ and observed temperatures of greater than 1.5 ◦ C was used to identify periods with potential footprint issues. An example of a comparison of ‘inferred’ and measured water skin temperature and identification of periods with footprint issues is shown in Fig. 4. In this figure an absolute difference between ‘inferred’ and observed temperatures of greater than 1.5 ◦ C is used to identify periods where measurement footprint might extend beyond the water surface. There are a number of different factors which could be responsible for causing measurement footprint issues at Logan’s Dam, these include wind speed, wind direction and atmospheric stability. Using the methodology for identifying potential measurement footprint problems it is possible to explore these issues further. If the percentage of data excluded from the measurement dataset for different classes of wind speed and wind direction is considered it is possible to determine the conditions most likely to result in measurement footprint issues. Such an analysis is shown in Fig. 5 where it can be seen that, there is an increasing tendency towards footprint issues at higher wind speeds. At wind speeds greater than 6 m s−1 an average of 40% of measurements were excluded compared to around 5% at the lowest wind speeds. To enable analysis across the entire dataset which includes a change in alignment of the scintillometer, the wind direction shown in Fig. 5a is expressed as an angle relative to the scintillometer beam (0–90◦ ). For such analysis a wind direction of 90◦ is perpendicular to the scintillometer beam. The idea behind this analysis is to see whether data exclusion is related in some way to the interaction between scintillometer signal strength, which is strongly weighted to the centre of the beam (Chehbouni et al., 2000; Meijninger et al., 2002a), and wind direction. While there is some suggestion that winds from 60◦ to 70◦ result in the most exclusions, some care is needed in interpretation of results as winds from some directions are rare and therefore the exclusion statistics are less certain.

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34

Skin temperature ( o C)

32

Footprint exclusion Measured Inferred

30 28 26 24 22 20 21/12/09 00:00

22/12/09 00:00

23/12/09 00:00

24/12/09 00:00

25/12/09 00:00

Fig. 4. Example comparison period showing measured and ‘inferred’ skin temperatures. Periods excluded due to violation of foot print criteria are shaded in grey.

The other interesting factor to consider is the effect of atmospheric stability on footprint exclusions. For the entire data set unstable atmospheric conditions prevail for 80% of the time while stable conditions prevail for 20% of the time. Despite this, footprint exclusions do not reflect these figures with stable conditions accounting for 42% of all exclusions and unstable conditions 58%. A separate analysis of the impacts of wind speed and direction on footprint exclusions for stable and unstable conditions in a manner similar to that in Fig. 5 was attempted, however with the smaller datasets, particularly for stable conditions, and poor representation of some wind directions (Fig. 3) such an analysis was not possible. Instead the percentage of measurement footprint exclusions for different wind speed classes during stable and unstable conditions has been compared (Fig. 6). From this analysis it is obvious that the occurrence of footprint issues is much greater for stable conditions than it is for unstable conditions across all wind speed classes, although the difference between the percentage of time with footprint issues for stable and unstable conditions diminishes with wind speed. Stable conditions are known to result in larger measurement footprints (Meijninger et al., 2002a; Von Randow et al., 2008). A similar analysis using wind direction classes revealed that wind direction was not important in controlling the occurrence of footprint issues. Over the duration of the investigation just 14% of measurements were excluded for measurement footprint issues. This value is low considering the relatively small size of the water storage but could be a result of enhanced turbulence which results as airflow passes

Fig. 5. Percentage of measurements excluded from dataset as a result of measurement footprint issues for different wind speed and wind direction classes.

over the dam wall and trees surrounding the dam itself. Working at a small boreal lake in Finland, Vesala et al. (2006) applied a footprint model to simulate turbulent transport in the boundary layer and concluded that the most important phenomenon affecting footprint extent was the turbulence structure. They found that most of the turbulence above the lake originated from the surrounding trees and was advected for many hundreds of meters downwind. The same processes are likely to be occurring at Logan’s Dam and the likely effect of this enhanced mechanical turbulence is to reduce the footprint extent. 3.2.3. Scintillometry and MOST The calculation of fluxes in scintillometry relies on the use of semi-empirical Monin–Obukhov Similarity Theory (MOST) relationships, therefore it is pertinent to test whether the conditions at the study site satisfy the requirements for MOST. To enable such an analysis to take place, eddy covariance measurements made at this field site during four two week long periods were used (see McJannet et al., 2011 for measurement details). The analysis approach follows that used by other authors (De Bruin et al., 1993; Hoedjes et al., 2002, 2007; Liu et al., 2011; Von Randow et al., 2008) 2/3 and involves plotting observed values of CT2 zLAS /T∗2 against zLAS /L and comparing to the corresponding scaling curves (in our case those defined by Andreas (1988)). T* is the temperature scale and L is the Obukhov length. The Cn2 value from the scintillometer and ˇ from the eddy covariance measurements were used to obtain the structure parameter (CT2 ) for this analysis. This is done to avoid MOST relationships already being taken into account in parameter

Fig. 6. Percentage of measurements excluded from dataset as a result of measurement footprint issues for specified wind speed classes and atmospheric stability condition.

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Fig. 7. Observed values of CT2 zLAS /T∗2 plotted against zLAS /L during unstable (a) and stable (b) conditions. The solid lines are the scaling functions proposed by Andreas (1988) and used in the scintillometer calculation scheme.

estimation, which is the case for scintillometer measurements. T* and L were also taken from eddy covariance methods. Data from the four observation periods were grouped and split into those representing stable and unstable conditions. During unstable conditions 2/3 (Fig. 7a) the observed values of CT2 zLAS /T∗2 and zLAS /L followed the shape of the scaling curve proposed by Andreas (1988) giving confidence in the approach used. Deviations of the measurements from the scaling curve will represent differences in flux estimates from the two techniques (Von Randow et al., 2008) however there is no tendency for consistent under or over estimation. During stable conditions (Fig. 7b) there were far fewer data points but there was a tendency for some points to greatly exceed the scaling curve. Closer inspection of these points show that they tend to represent periods of very low H (<−10 W m−2 ) and small ˇ. Excluding these points, the remaining points tend to sit around the scaling curve. As stated by De Bruin et al. (1993) the most conclusive test of any MOST approach is a comparison between fluxes derived from MOST relationships and independently measured fluxes. McJannet et al. (2011) undertook such an analysis and reported excellent agreement between flux measurements made by eddy covariance and scintillometry at this field site. The analysis undertaken here and the comparison of fluxes undertaken in other studies gives confidence in the use of scintillometry to determine fluxes at this site. 3.2.4. Processes controlling fluxes Using the extensive quality controlled dataset, the strength of ∗ − e ) and H and u(T − T ) the relationships between E and u(ew a w a were considered. Relationships were determined using a random selection of 20% of the available quality controlled dataset (n = 8971). Latent heat flux was shown to be strongly controlled ∗ − e ) (Fig. 8a). A single relationship fit to all data regardless by u(ew a of stability conditions explained 93% of the observed variation in evaporation. Relationships fitted to data points grouped by stability conditions further improved predictions with 95% of the observed

Fig. 8. Latent heat flux as a function of the vapour pressure gradient multiplied by the wind speed (a), and sensible heat flux as a function of temperature difference between the surface and the air multiplied by the wind speed (b). The line represents the fit to all data points. Equations for the line of best fit for a single fitted relationship and relationships based on atmospheric stability are given in Table 1.

variation being explained by these separate fitted relationships (Table 1). When the derived relationships were tested against predictions from the remaining 80% of the quality controlled dataset (n = 35,884) the agreement was excellent with a RMSE of less than 16 W m−2 and an r2 of 0.95, when separate relationships were used for stable and unstable conditions (Table 1). Similarly strong agree∗ − e ) has been reported by ment between latent heat flux and u(ew a Blanken et al. (2000) – r2 = 0.84, with slightly weaker relationships reported by Nordbo et al. (2011) – r2 = 0.59 and Liu et al. (2009). It is interesting to note that the results reported by Nordbo et al. (2011) ∗ using water differ from the others reported in that they calculate ew temperature measured at a depth of 0.2 m rather than directly at the air–water interface (skin temperature), hence temperature stratification may be partly responsible for the increased scatter reported in this study. The reported relationships are similar to, but not the same as, the commonly reported wind function (f(u)) (e.g. McJannet et al., 2012; Sweers, 1976) which forms the basis of Dalton (1802) type ∗ − e ) where f (u) = a + bu). evaporation equations (i.e. E = f (u)(ew a The wind function for our site (in units of W m−2 kPa−1 ) is f (u) = 37.7 + 30.6u with r2 = 0.80. Sensible heat flux was shown to be strongly controlled by u(Tw − Ta ) (Fig. 8b). A single relationship between sensible heat flux and u(Tw − Ta ) explained 83% of the observed variation in sensible heat flux, while separate relationships for stable and unstable conditions explained 70% of the observed variation in H (Table 1). The RMSE of predicted sensible heat flux when compared to the remaining 80% of the quality controlled measurement dataset was

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Table 1 Equations for the line of best fit for latent heat flux (W m−2 ) and the vapour pressure gradient (kPa) multiplied by the wind speed (m s−1 ) and sensible heat flux (W m−2 ) and temperature difference (K) between the surface and the air multiplied by the wind speed (data in Fig. 8). Equations are shown for a single fitted relationship for all data and separate relationships for stable and unstable conditions and are based on 20% of the available dataset. Also shown is the Root Mean Square Error (RMSE) and coefficient of determination (r2 ) of the modelled latent and sensible heat fluxes when compared to measurements from the remaining 80% of the available dataset.

Latent heat flux Best fit equation for sample data (n = 8971)

RMSE of modelled versus measured (n = 35,884) r2 of modelled versus measured (n = 35,884) Sensible heat flux Best fit equation for sample data

(n = 8971)

RMSE of modelled versus measured (n = 35,884) r2 of modelled versus measured (n = 35,884)

Single fitted relationship

Separate stability relationships

All conditions (r2 = 0.93) ∗ − ea ) + 23.92 E = 32.11u(ew

Unstable (r2 = 0.95) ∗ − ea ) + 21.60 E = 35.11u(ew Stable (r2 = 0.95) ∗ E = 31.79u(ew − ea ) + 6.23 15.80 W m−2 0.95

18.94 W m−2 0.92 All conditions (r2 = 0.84) H = 2.19u(Tw − Ta ) + 6.86

6.00 W m−2 0.83

less than 6 W m−2 when separate relationships were used for stable and unstable conditions. At times unstable data points occur when u(Tw − Ta ) is <0, these points represent periods where the air temperature at the lower sensor is greater than that at the upper sensor whereas Tw is less than upper air temperature. Closer analysis of the periods reveal that they tend to be transitional periods between stability conditions. Nordbo et al. (2011) and Liu et al. (2009) also explored the relationship between sensible heat flux and u(Tw − Ta ) and report good agreement. Nordbo et al. (2011) reports an r2 of 0.62 which is not as strong as that observed in the current study, however, this could be partly due to the depth at which they measured water temperature, as discussed above. The derived relations for predicting latent and sensible heat fluxes provide a robust and simple means by which to fill missing measurements in the 18 month dataset. This allowed a complete estimate of fluxes for the entire study period which, when

Unstable (r2 = 0.70) H = 2.45u(Tw − Ta ) + 6.28 Stable (r2 = 0.70) H = 1.32u(Tw − Ta ) − 2.45 5.54 W m−2 0.86

combined with other measurements, enabled an analysis of energy balance of the dam to be constructed. Analysis of the average diurnal course of H and E shows some distinct differences across the seasons. During the summer there is a very strong diurnal variation in E with peak fluxes that are almost four times those occurring during the night (Fig. 9a). During the winter, the diurnal course of E is far less variable with only a slight increase in evaporation during the daylight hours. The diurnal course of E during autumn and spring was almost identical. The trends observed in this study are similar to those reported for other water bodies (e.g. Blanken et al., 2000; Liu et al., 2009; Nordbo et al., 2011; Venäläinen et al., 1999; Vesala et al., 2006) although the amplitude of the variation between seasons tends to be greater reflecting the warmer temperatures experienced in Queensland. Fig. 9b shows the vapour pressure difference between the water surface and the air above which is one of the key

Fig. 9. Hourly average diurnal course of latent heat flux (a), vapour pressure deficit between water surface and air above (b), sensible heat flux (c) and temperature difference between water surface and air above (d) for summer (January 2010), autumn (April 2010), winter (July 2010) and spring (October 2010) conditions.

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driving factors in the bulk transfer relationships for E. The seasonal variations in vapour pressure difference closely match those for E with the exception that the vapour pressure difference in spring is greater than that in autumn whereas E is almost identical. This difference is explained by slower daytime wind speeds during autumn as compared to spring. Sensible heat fluxes show an opposite diurnal trend to latent heat flux, however fluxes vary in magnitude only slightly across the seasons (Fig. 9c). H tended to be weakly positive during the night and weakly negative during the day. H during autumn and spring was very similar and showed the least amount of diurnal variation. Interestingly, H does not vary much between the summer and winter months, although daytime fluxes are slightly more negative during summer. Fig. 9d shows the temperature difference between the water surface and the air above which can be considered one of the key driving factors in the bulk transfer relationships for H. Diurnal variations in temperature difference show similar trends to that exhibited for H. Spring and autumn diurnal trends in temperature difference between the surface and air above are almost identical. The temperature difference between the surface and air is smallest during the summer months however this does not correspond to the smallest H fluxes as winds are stronger during summer than they are during the other months. 3.3. Long-term energy balance analysis Comparison of daily fluxes of each of the measured energy balance components shows that some components exhibit distinct seasonal trends (Fig. 10). As is expected, net radiation peaks during the summer months and reaches a minimum during the winter months and this trend is mimicked by latent heat fluxes (Fig. 10b). For the duration of the study the average E was 98 W m−2 with a maximum value of 290 W m−2 . Apart from a period of negative fluxes during the winter of 2010, H does not show strong seasonal variation (Fig. 10c). Average H was just 7.8 W m−2 with maximum and minimum daily values of 25.1 and −16.9 W m−2 , respectively. Fig. 10d shows the percentage of daily E and H flux data that were calculated using the equations detailed in Table 1 rather through direct scintillometry measurements. In this figure there are a few obvious periods of extensive data filling (>80%) and these are related to periods where the scintillometer transmitter or receiver went out of alignment due to the shrinking and swelling nature of the local soils. Other than these periods, Fig. 10d also shows a distinct increase in data filling during the cooler winter months. This is largely due to footprint exclusions related to stable atmospheric conditions. Filling occurs throughout the study periods due to rainfall, low wind speeds (<1 m s−1 ) and regular (almost daily) occurrence of fog over the water in the early morning. To determine if the heat storage in the dam calculated from the central thermistor chain was representative of the conditions within the whole dam a comparison of estimates derived from the centrally located thermistor with those calculated using four thermistor chains distributed around the dam was undertaken (Fig. 11). Comparison took place over 77 days using depth-weighted average temperatures for each thermistor chain and it can be seen that differences in calculated Sw were only small (RMSE = 10.4 W m−2 ). The 95% confidence intervals on the line of best fit in Fig. 11 show that the relationship between the two estimation methods is not statistically different from 1:1, thus increasing confidence in the long-term measurement approach. More importantly, there was no large systematic under- or over-estimation of Sw when using the central thermistor chain alone and it can be concluded that reliance on the central thermistor chain for long-term estimates would not introduce considerable error to the energy balance analysis. As has been observed in other lake studies (e.g. Anderson, 1954; Gianniou and Antonopoulos, 2007), heat storage at Logan’s Dam

Fig. 10. Daily energy fluxes of net radiation (a), latent heat flux (b), sensible heat flux (c), change in water heat storage (d), rainfall (e) and pumping (f) for the 18month study period. Also shown is the percentage of daily E and H data filled using equations from Table 2 (d).

Fig. 11. Plot of Sw calculated from central thermistor chain alone versus Sw calculated using four distributed thermistor chains (see Fig. 1). The grey lines represent the 95% confidence intervals of the line of best fit. The equation of the line of best fit is y = 1.05x + 0.51, r2 = 0.96.

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increased during the summer months and decreased during the winter months. The largest changes in stored heat content occurred during periods were large volumes of water were pumped into the dam from the surrounding sump. The daily changes in heat storage in the dam are shown in Fig. 10e. The change in heat storage in the air between the water surface and the height of net radiation measurements (Sa ) is not shown in Fig. 10 as day to day differences were so found to be very small (±0.4 W m−2 ). Energy addition through rainfall was a very minor component of the energy balance for the vast majority of the year (Fig. 10f), however its importance increased during the summer with progression of the rainy season. On particularly wet days the contribution of energy input through rainfall can exceed net radiation. Loss of energy through removal of water from the dam via pumping occurs sporadically during the growing season (Fig. 10g) and when it does happen it is not unusual for energy loss to exceed 30 W m−2 . As with rainfall energy, energy lost through pumping was relatively minor. 3.4. Energy balance closure Fig. 12. Plot of total daily turbulent fluxes (H + E) versus daily energy availability (Rn − Sw + Qr − Qp ) for the 18 month study period.

The energy balance equation (1) can be expressed as Rn − Sw − Sa + Qr − Qp − Qs = H + E

(9)

where the balance of the left hand side of the equation gives the energy available for turbulent fluxes. Any difference between the two sides of the equation will reflect differences in closing the energy balance with available measurements. Note, energy fluxes into the sediment beneath the water have not been measured but can be estimated (see further discussion below). Fig. 12 presents a comparison of daily total turbulent fluxes versus daily energy availability and shows that the turbulent fluxes are often less than the available energy. Note that we ignore Sa in our energy balance closure analysis as it is so small (<±0.4 W m−2 ). The average daily residual during the study was ∼20 W m−2 . A number of lake energy balance studies using eddy covariance instrumentation have found similar rates of energy balance closure to those found in this study. Nordbo et al. (2011) found energy balance closure of 82% and 72% in consecutive years for a small lake in Finland, while Liu et al. (2009) reports average energy balance component rates for a 5 month period for a reservoir in Mississippi which resulted in energy balance closure of approximately 80%. Studies conducted over shorter time periods tend to report better energy balance closure with Tanny et al. (2008) reported energy balance closure of 91% over

a 14 day period for a reservoir in Israel and Blanken et al. (2000) reporting closure of 96% for a large lake in Canada over a period of 49 days. The energy balance closure problem is not unique to water bodies as similar energy balance closure issues have been reported for many terrestrial studies and literature from this field can be drawn upon for guidance (e.g. Eigenmann et al., 2011; Foken et al., 2010; Liu et al., 2011; Savage, 2009; Twine et al., 2000). An overview of the energy balance closure problem is given by Foken (2008a) who looked at 20 years of research into this issue and found typical energy balance closure of approximately 80% for terrestrial studies. A similar imbalance was reported for 22 FLUXNET sites by Wilson et al. (2002). It is also interesting to consider the changes to energy balance closure throughout the year. Fig. 13 shows a distinct increase in energy balance residual during the warmer months and almost perfect closure during the winter months. The magnitude of the differences in energy balance closure suggest that the smaller energy balance components are unlikely to be the cause. In particular, energy inputs through rain (monthly average 2.66 W m−2 ), and

Fig. 13. Comparison of average monthly turbulent fluxes (H + E) versus average monthly energy availability (Rn − Sw + Qr − Qp ) illustrating the seasonal variations in energy balance closure.

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energy loss through pumping (monthly average 2.73 W m−2 ) are unlikely to be the cause. Also, exclusion of days with rainfall and pumping from the comparison in Fig. 12 does change the slope of the relationship. Some of the observed difference could be due to the lack of inclusion of fluxes of energy into the underlying sediments. Inclusion of such a term would improve closure, however, the magnitude of such fluxes is not likely to be big enough to explain the observed variation and this term is often ignored in energy balance studies for water bodies (e.g. Anderson, 1954; Assouline and Mahrer, 1993; Gianniou and Antonopoulos, 2007; Tanny et al., 2011). Studies that attempt to include sediment heat fluxes generally report very small fluxes. For example, Smith (2002) measured energy flux rates into sediments in a shallow lagoon and reported average values of 2.1 W m−2 , while Miguel et al. (2005) found average flux rates into sediment in a shallow water in southern Spain of 2.2 W m−2 . Working at two lakes in Wisconsin, Likens and Johnson (1969) report annual sediment heat fluxes of 1 and 1.3 W m−2 . With the smaller components of the energy balance unlikely to be the cause of observed lack of closure, the potential contribution from the larger components of the energy balance will be discussed in more detail in the following sections. 3.4.1. Heat storage The potential for introducing error in heat budget studies of water bodies through heat storage estimates have been noted by a number of authors (e.g. Nordbo et al., 2011; Stannard and Rosenberry, 1991; Vercauteren et al., 2009; Webb, 1960). Schertzer et al. (2003) compared distributed thermistor chains in the Great Slave Lake to show how the spatial variation in water temperature and hence, heat storage within a water body can be large. In contrast, a comparison of heat storage calculations using different numbers of thermistor chains at Logan’s Dam shows that spatial variation in heat storage cannot account for the observed residual in energy balance closure (Fig. 11). Although small temperature differences result in large changes in heat storage estimates, these variations will introduce random type errors (Nordbo et al., 2011), not the systematic type discrepancy which is observed in this study. Another source of potential error in heat storage calculations arises from uncertainty in the relationship between water depth and dam volume. However, this effect will be small because it is only the relative difference in volume between days that will influence heat storage calculations. 3.4.2. Net radiation Net radiation errors have also been considered as a reason for the lack of closure in the energy balance (e.g. Foken, 2008a; Halldin and Lindroth, 1992; Wilson et al., 2002). Generally such measurements are considered to be reliable and have a reasonable accuracy (e.g. Brotzge and Duchon, 2000; Kohsiek et al., 2007), however these studies tend to involve inter-comparison of sensors that are not high standard references. Michel et al. (2008) undertook an analysis of the performance and uncertainty of the Kipp and Zonen CNR1 net radiometer (as used in this study) by comparing to high standard reference radiation instruments. When using manufacturer supplied calibration coefficients and an instrument without ventilation and heating, Michel et al. (2008) found that uncertainty in total net radiation was 26% on daily averages with a bias of ∼10 W m−2 , much greater than the 10% claimed by the manufacturer. Average annual net radiation during their comparison period was 62 W m−2 , almost exactly half of that measured in this study. It is unclear whether the greater net radiation at our site would increase or decrease uncertainty, however such issues cannot be discounted as a potential cause of the observed energy balance residual. Michel et al. (2008) also mention that heat transmission

Table 2 Comparison of average sensible and latent heat fluxes for the study period using variable and fixed vales for z0 . Also shown is the overall energy balance closure for the entire study period. z0

¯ (W m−2 ) H

E¯ (W m−2 )

Variable (Eq. (3)) 0.0001 0.0005 0.001

7.9 6.8 7.0 6.7

98.2 83.6 111.9 128.8

Energy balance closure 84% 72% 94% 107%

to the thermopiles could affect radiation estimates; an observation also made by Halldin and Lindroth (1992). Such an affect could lead to seasonal differences in net radiation estimates. As an example of the potential impact of net radiation measurement errors, if the daily net radiation data is corrected using a fixed value of 10 W m−2 (the bias value for net radiation reported by Michel et al. (2008)) the average energy balance closure across the study period increases to 95%. 3.4.3. Turbulent fluxes The other potential cause of the energy balance closure issue is underestimation of the turbulent fluxes. In other scintillometry studies over land where the structure parameters of temperature is adjusted for humidity effects the Bowen ratio is defined by measuring all other terms of the energy balance and forcing closure (known as the ‘ˇ closure method’). This is not the case in this study as the ‘linearized ˇ method’ was used (McJannet et al., 2011). Underestimation of the fluxes may result from some of the empirical or theoretical relationships used in the derivation of fluxes, however this is very hard to quantify and a number of studies have demonstrated good agreement with other measurement methods (e.g. Hartogensis and De Bruin, 2006; Meijninger et al., 2002b; Savage, 2009). If the cause of the greater energy balance residual during the warmer months (Fig. 13) is due to underestimation of the turbulent fluxes then any errors introduced in the calculations procedures must be greatest for higher flux rates. Also, if underestimation of the turbulent fluxes is suspected then the latent heat fluxes are more likely to be the cause as they dominate over the sensible heat fluxes. In a recent study by Bouin et al. (2012) scintillometry was used to determine the sensible heat flux over water in the south of France. This study showed that the choice of z0 (fixed or variable) value had very little influence on the resultant H fluxes and concluded that adjusting z0 during data processing caused no significant changes in the final results. To see if the same held true for this current study a similar comparison was undertaken using the variable z0 value and fixed values of 0.0001 m, 0.0005 m and 0.001 m and the results are shown in Table 2. In agreement with the results of Bouin et al. (2012) this analysis shows that mean H varies only slightly depending on the choice of z0 . It is also worth noting that the estimated values of H for different z0 methods are highly correlated (r2 > 0.97). However, taking this analysis further it can be seen that the impact of different z0 specifications on calculated E can be large (Table 2). Despite estimated values of E being highly correlated for different z0 methods (r2 > 0.99), mean E values differ by as much as 30% from the variable method. It can be seen from Table 2 that improvements in energy balance closure can be achieved by selecting an appropriate z0 value but taking such an approach ignores the well documented variation in z0 over water with u* (see Foken, 2008b for summary). Forcing closure by using the best value of z0 is not scientifically robust and clearly there would be much benefit to be gained from detailed future studies on the appropriate specification of z0 . This being said, under-estimation of turbulent fluxes (in particular E) cannot be discounted as the cause of the lack of energy balance closure.

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A further test for whether low evaporative fluxes might be the cause of the lack of energy balance closure is to compare results with evaporation estimates made using an evaporation model. For this analysis the Penman open water evaporation model (Penman, 1948) with adjustments for heat storage (as described by Finch (2001)) was used. Over the duration of the study period average evaporation estimates for the scintillometer and Penman model varied only slightly with values of 98.3 W m−2 and 104.5 W m−2 , respectively. Modelled E estimates were also strongly correlated with those derived through scintillometry (EPen = 0.99 * EScin + 6.09, r2 = 0.79). When modelled evaporation was combined with measured H, the over-all energy balance closure during the summer months was slightly better at 90% as compared to 82% for scintillometer estimates. Winter energy balance closure changed very little with 95% closure for the Penman model and 96% for estimates using the scintillometer evaporation. Energy balance closure using the modelled results was only slightly better than those made using scintillometer estimates therefore this analysis provides no strong evidence that scintillometer evaporation estimates are too low. Another important aspect to consider with respect to the calculation of turbulent fluxes is whether the assumption of equal diffusivities of water vapour and heat is suitable. Assouline et al. (2008) used eddy correlation measurements to explore this assumption for three water bodies and showed that the ratio of diffusivities varied depending on advection and the thermal inertia of the water body. This issue has been explored previously by McJannet et al. (2011) at this same location by comparing scintillometer and eddy covariance derived ˇ. If the diffusivities of heat and water vapour were not equal then the ˇ calculated from the two independent measurement systems would be expected to show distinct differences, however, McJannet et al. (2011) showed very good agreement between the two methods (r2 = 0.83, RMSE = 0.06) suggesting the assumption is justified. One simplified correction for energy balance closure is to distribute the residual between sensible and latent heat fluxes according to the measured Bowen ratio (Foken, 2008a). While such an approach is not advocated it provides an opportunity to further explore the issue of energy balance closure through use of derived evaporation estimates in a water balance analysis. A suite of measurements for determining the water balance of Logan’s Dam (flows in and out, storage changes, rain, and evaporation) also exists and this water balance analysis is the subject of another paper where leakage losses are derived (McJannet et al., in review). Taking January 2010 as a demonstration period (which is the month with largest energy balance residual) we can combine all components of the water balance to derive leakage as the residual using existing evaporation estimates and corrected evaporation estimates. Using measured evaporation rates an average daily leakage loss of 1.1 mm is calculated which is a value similar to that determined for other water storages (e.g. Ham, 2002). If corrected evaporation rates are used the average daily leakage loss becomes −0.3 mm. Such a value suggests net flow of water to the dam which is not physically possible as this dam is constructed above the surrounding landscape. This analysis suggests that the energy balance closure issue cannot be entirely attributed to underestimation of turbulent fluxes. 3.4.4. Measurement footprint and advection In a review of energy balance closure issues for eddy covariance systems Foken (2008a) hypothesizes that the most likely cause is that energy transport with very large eddies is not captured during the measurement period. Foken (2008a) also notes that if larger eddies have a significant contribution to the energy exchange then they must be generated at the boundary between different land uses which are normally excluded from measurements due to their influence on the measurement footprint. The boundary between the surrounding land and the dam may provide the conditions for

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such eddies to develop in this study. Interestingly for water bodies, the upwind edge is likely to be the zone of highest evaporation as air moving from the land over the water encounters a rapid surface roughness change which enables the air to accelerate. Evaporation is also likely to be greatest at the upwind edge because as you move downwind the cumulative entrainment of moisture into the air is likely to reduce the vapour pressure gradient (Webster and Sherman, 1995). The measurement footprint of the scintillometer may not always extend to the upwind zone of the water surface, therefore the spatial extent of measurements could also play an important role in closing the energy balance. A number of authors have also identified the failure to account for advected energy as a potential contributor to the problem of energy balance closure in varying landscapes (Eigenmann et al., 2011; Foken, 2008a; Leclerc et al., 2003; Li and Yu, 2007; Wilson et al., 2002). A study by Oncley et al. (2007) used several profile towers and flux gradient methods to estimate horizontal mean advection for an irrigated agricultural area and showed minimal advected sensible heat fluxes but advected latent heat fluxes of up to 30 W m−2 . Oncley et al. (2007) demonstrated that the advected latent heat was greatest in the afternoon when the residual in energy balance closure was greatest. It was proposed that the advected energy was not included at the measurement because fluxes at the measurement height may have been less than the actual flux at the surface. If similar processes are occurring at Logan’s Dam then advection could also be partly responsible for the lack of closure. Enhanced advected latent heat flux loss during summer months could also help to partially explain seasonality in energy balance closure.

4. Conclusions An investigation has been conducted into the energy balance of a small water body in south-east Queensland, Australia for a period of 18 months. The focus of this study was on the use of scintillometry to determine the turbulent fluxes. Identification of periods where measurement footprint extended beyond the water surface by comparison of ‘inferred’ and measured skin temperature resulted in just 14% of measurements being excluded for footprint issues, thus, illustrating the suitability of scintillometry for determining fluxes in such environments. Footprint issues were found to be strongly related to wind speed but wind direction was not shown to have a strong influence. Stable conditions were found to account for 42% of footprint exclusions despite only representing 20% of measurements. The product of wind speed and the vapour pressure difference between the water surface and the air above was found to be a robust predictor of latent heat flux. Similarly, the product of wind speed and the temperature difference between the water surface and the air was also found to be very strong. The derived aerodynamic algorithms for predicting latent and sensible heat fluxes provided a reliable and simple means for filling missing measurements and constructing a complete 18 month dataset. Using measurements of all energy balance components for the full 18 month period it was found that energy balance closure across the study was 82%; a value similar to that found in many other studies. By assessing the seasonality of energy balance closure it was found that much better closure occurred during the winter than the summer. The key factors likely to lead to errors in energy balance closure were considered and it was concluded that the most likely causes were underestimation of latent heat fluxes, advection of latent heat fluxes which are not measured by the scintillometer, or overestimation of net radiation. Latent heat flux estimates were shown to be sensitive to specification of z0 and it was demonstrated that complete energy

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balance closure was possible by selecting an appropriate fixed z0 value, however, this approach is not scientifically valid and makes the assumption that latent heat fluxes are too low; an assumption not supported by modelling and water balance tests. More detailed investigations into the suitability of z0 specification are needed for open water environments. Use of ventilated net radiometers with up-welling and down-welling components calibrated to a high standard reference is also recommended for minimizing measurement uncertainty for this component. Finally, inclusion of additional instrumentation to enable quantification of any advected energy is also recommended for smaller water bodies. These recommendations will help in minimizing energy balance closure issues and enable verification and improvement of future scintillometry flux measurements. Acknowledgements The authors wish to acknowledge the cooperation of Linton and Melinda Brimblecombe who allowed access to the site and installation of equipment. Darren Morrow, Geoff Carlin, Tim Ellis, Rex Keen, Joseph Kemei, and Grant Beckett provided assistance with the design, installation and maintenance of the equipment. G.L. Irrigation Pty Ltd. kindly allowed access to survey data for the site. Ian Webster provided valuable comment on the paper as did the anonymous reviewers. Funding for this research was provided by the Urban Water Security Research Alliance and CSIRO Water for a Healthy Country. References Anderson, E.R., 1954. Energy budget studies. Water-loss investigations: Lake Hefner studies. U.S. Geological Survey. Professional Paper 269. Andreas, E.L., 1988. Atmospheric stability from scintillation measurements. Appl. Opt. 27 (11), 2241–2246. Assouline, S., Mahrer, Y., 1993. Evaporation from Lake Kinneret 1. Eddy correlation system measurements and energy budget estimates. Water Resour. Res. 29, 901–910. Assouline, S., Tyler, S.W., Tanny, J., Cohen, S., Bou-Zeid, E., Parlange, M.B., et al., 2008. Evaporation from three water bodies of different sizes and climates: measurements and scaling analysis. Adv. Water Resour. 31 (1), 160–172. Blanken, P.D., Rouse, W.R., Culf, A.D., Spence, C., Boudreau, L.D., Jasper, J.N., et al., 2000. Eddy covariance measurements of evaporation from Great Slave Lake, Northwest Territories, Canada. Water Resour. Res. 36 (4), 1069–1077. Bonan, G.B., 1995. Sensitivity of a GCM simulation to inclusion of inland water surfaces. J. Climate 8 (11), 2691–2704. Bouin, M., Legain, D., Traullé, O., Belamari, S., Caniaux, G., Fiandrino, A., et al., 2012. Using scintillometry to estimate sensible heat fluxes over water: first insights. Boundary Layer Meteorol. 143 (3), 451–480. Brotzge, J.A., Duchon, C.E., 2000. A field comparison among a domeless net radiometer, two four-component net radiometers, and a domed net radiometer. J. Atmos. Oceanic Technol. 17 (12), 1569–1582. Chehbouni, A., Watts, C., Lagouarde, J.P., Kerr, Y.H., Rodriguez, J.C., Bonnefond, J.M., et al., 2000. Estimation of heat and momentum fluxes over complex terrain using a large aperture scintillometer. Agric. For. Meteorol. 105 (1–3), 215–226. Condie, S.A., Webster, I.T., 1995. Evaporation mitigation from on-farm water storages. Technical Report No. 90. CSIRO Centre for Environmental Mechanics. Craig, I.P., 2006. Comparison of precise water depth measurements on agricultural storages with open water evaporation estimates. Agric. Water Manage. 85 (1–2), 193–200. Dalton, J., 1802. Experimental essays on the constitution of mixed gases; on the force of steam or vapour from water and other liquids at different temperatures, both in a Torricellian vacuum and in air; on evaporation; and on the expansion of gases by heat. Lit. Phil. Soc. Manchester, Memoirs 5–11, 535–602. De Bruin, H.A.R., Kohsiek, W., Hurk, B.J.J.M., 1993. A verification of some methods to determine the fluxes of momentum, sensible heat, and water vapour using standard deviation and structure parameter of scalar meteorological quantities. Boundary Layer Meteorol. 63 (3), 231–257. Eigenmann, R., Kalthoff, N., Foken, T., Dorninger, M., Kohler, M., Legain, D., et al., 2011. Surface energy balance and turbulence network during the Convective and Orographically-induced Precipitation Study (COPS). Q. J. R. Meteorol. Soc. 137 (S1), 57–69. Falkenmark, M., Lundqvist, J., Klohn, W., Postel, S., Wallace, J., Shuval, H., et al., 1998. Water scarcity as a key factor behind global food insecurity: round table discussion. Ambio 27 (2), 148–154. Finch, J.W., 2001. A comparison between measured and modelled open water evaporation from a reservoir in south-east England. Hydrol. Processes 15, 2771–2778.

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