Automated calculation of surface energy fluxes with high-frequency lake buoy data

Woolway, R. Iestyn; Jones, Ian D.; Hamilton, David P.; Maberly, Stephen C.; Muraoka, Kohji; Read, Jordan S.; Smyth, Robyn L.; Winslow, Luke

doi:10.1016/j.envsoft.2015.04.013

Cited by 74 publications

(81 citation statements)

References 52 publications

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“…We further exclude flow distortion and an associated higher proportion of upward winds and overestimated H to be the reason, as we filtered the fluxes appropriately (Text S1). Woolway et al () have discussed the risk of underestimating turbulent fluxes across the air‐water interface for lakes by classical bulk transfer models for oceans, as they do not account for lake‐specific conditions such as wind variation caused by sheltering and fetch limitation of wind (for example, Schladow et al, ). Furthermore, the atmospheric boundary layer stratification over lakes is more variable due to stronger heating and cooling by the adjacent land surface and lower U (Verburg & Antenucci, ).…”

Section: Discussionmentioning

confidence: 99%

“…As the coefficients were calculated for our specific measurement height and are thus site specific, we additionally estimated the coefficients in the reference height of 10 m ( C H 10 and C E 10 ) following Xiao et al () from linear regression involving U at 10‐m height ( U 10 ). For an estimation of the roughness length z 0 , which was needed for the standard logarithmic wind law (neutral stability assumption) in order to estimate U 10 , we applied the flux gradient relations for momentum with stability classes as described in Woolway et al (). The drag coefficient for momentum ( C D ) was estimated as slope of the regression of the square of U versus the square of friction velocity ( u * ).…”

Section: Methodsmentioning

confidence: 99%

“…Lake heat storage is regulated at the air-water interface by turbulent heat fluxes and long-wave radiation (Nordbo et al, 2011). Turbulent heat fluxes further influence the water balance and alter the strength and duration of thermal stratification and mixing, highlighting them as important features in the functioning of a lake (Verburg & Antenucci, 2010;Woolway et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Bulk aerodynamic transfer models were initially developed in ocean research and applied either without modifications or only tentatively modified for lakes and reservoirs (Woolway et al, 2015). However, physical conditions over oceans and lakes can differ remarkably depending on the size of the lake (Verburg & Antenucci, 2010;Woolway et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Lake‐Atmosphere Heat Flux Dynamics of a Thermokarst Lake in Arctic Siberia

et al. 2018

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We conducted eddy covariance measurements from April to August 2014 on a Siberian thermokarst lake. The study site is located in the Lena River Delta and characterized as a floating ice lake. Heat fluxes differed in magnitudes, directions and temporal patterns depending on the lake surface conditions (“frozen” ice cover, ice cover melt, and open water). Significant heat release during frozen ice cover conditions highlighted the importance of lakes for the landscape heat budget and water balance. The energy balance was nearly closed during the open water period and highlighted the impact of melting energy on its closure during the ice cover period. Sensible and latent heat dynamics were driven by temperature and water vapor gradients scaled by wind speed, respectively. We calculated bulk aerodynamics transfer coefficients and evaluated the performance of the derived in situ and three independent heat flux parameterization schemes. We found that bulk transfer models perform moderately to poorly for the different lake surface conditions. During the open water period small‐scale temporal variability could not be represented by the models, particularly in case of latent heat flux. The model results were less sensitive to the specific model type than to the accuracy of the surface water temperature measurement, which is dependent on a well‐thought‐out measurement design. Our study stresses considerations that are crucial for similar campaigns in the future, in order to face the measurement challenges encountered on arctic lakes especially during the ice cover period.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lake‐Atmosphere Heat Flux Dynamics of a Thermokarst Lake in Arctic Siberia

et al. 2018

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“…In this study, we followed the methods of Woolway et al (2015b) and estimated K d as a function of secchi depth (z secchi ) as: K d = 1.75/ z secchi . An average z secchi was estimated from monthly averaged observations from Võrtsjärv.…”

Section: Lake Temperature Modelmentioning

confidence: 99%

Atmospheric stilling leads to prolonged thermal stratification in a large shallow polymictic lake

et al. 2017

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To quantify the effects of recent and potential future decreases in surface wind speeds on lake thermal stratification, we apply the one-dimensional process-based model MyLake to a large, shallow, polymictic lake, Võrtsjärv. The model is validated for a 3-year period and run separately for 28 years using long-term daily atmospheric forcing data from a nearby meteorological station. Model simulations show exceptionally good agreement with observed surface and bottom water temperatures during the 3-year period. Similarly, simulated surface water temperatures for 28 years show remarkably good agreement with long-term in situ water temperatures. Sensitivity analysis demonstrates that decreasing wind speeds has resulted in substantial changes in stratification dynamics since 1982, while increasing air temperatures during the same period had a negligible effect. Atmospheric stilling is a phenomenon observed globally, and in addition to recent increases in surface air temperature, needs to be considered when evaluating the influence of climate change on lake ecosystems.

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Gas transfer velocities in small forested ponds

2017

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Inland waters actively exchange gases with the atmosphere, and the gas exchange rate informs system biogeochemistry, ecology, and global carbon budgets. Gas exchange in medium‐ to large‐sized lakes is largely regulated by wind; yet less is known about processes regulating gas transfer in small ponds where wind speeds are low. In this study, we determined the gas transfer velocity, k600, in four small (<250 m2) ponds by using a propane (C3H8) gas injection. When estimated across 12 h periods, the average k600 ranged from 0.19 to 0.72 m d−1 across the ponds. We also estimated k600 at 2 to 3 h intervals during the day and evaluated the relationship with environmental conditions. The average daytime k600 ranged from 0.33 to 1.83 m d−1 across the ponds and was best predicted by wind speed and air or air‐water temperature; however, the explanatory power was weak (R2 < 0.27) with high variability within and among ponds. To compare our results to larger water bodies, we compiled direct measurements of k600 from 67 ponds and lakes worldwide. Our k600 estimates were within the range of estimates for other small ponds, and variability in k600 increased with lake size. However, the majority of studies were conducted on medium‐sized lakes (0.01 to 1 km2), leaving small ponds and large lakes understudied. Overall, this study adds four small ponds to the existing body of research on gas transfer velocities from inland waters and highlights uncertainty in k600, with implications for calculating metabolism and carbon emissions in inland waters.

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Automated calculation of surface energy fluxes with high-frequency lake buoy data

Cited by 74 publications

References 52 publications

Lake‐Atmosphere Heat Flux Dynamics of a Thermokarst Lake in Arctic Siberia

Lake‐Atmosphere Heat Flux Dynamics of a Thermokarst Lake in Arctic Siberia

Atmospheric stilling leads to prolonged thermal stratification in a large shallow polymictic lake

Gas transfer velocities in small forested ponds

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