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  • 1
    Publication Date: 2024-03-06
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (〈3 m s〈sup〉−1〈/sup〉), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10〈sup〉−3〈/sup〉, 1.4·10〈sup〉−3〈/sup〉, 1.0·10〈sup〉−3〈/sup〉, respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds.〈/p〉
    Description: Plain Language Summary: In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy‐covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Bulk transfer coefficients exhibit a substantial increase at low wind speed〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The increase is explained by wind gustiness and capillary wave roughness〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉At higher wind speed, drag coefficient and Stanton number decrease with lake surface area〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: SHESF, Sao Francisco Hydroelectric Company
    Description: DOE Ameriflux Network Management Project
    Description: NSF North Temperate Lakes LTER
    Description: U.S. Department of Energy Office of Science
    Description: Japan Society for the Promotion of Science KAKENHI
    Description: Swedish Research Council
    Description: ÚNKP‐21‐3 New National Excellence Program of the Ministry for Innovation and Technology, Hungary
    Description: Russian Science Foundation http://dx.doi.org/10.13039/501100006769
    Description: Helmholtz Young Investigators Grant
    Description: Helmholtz Association of German Research Centers
    Description: Austrian Academy of Sciences
    Description: Autonome Provinz Bozen‐Südtirol
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: Russian Ministry of Science and Higher Education
    Description: National Research, Development and Innovation Office
    Description: ICOS‐Finland, University of Helsinki
    Description: https://doi.org/10.5281/zenodo.6597828
    Keywords: ddc:551.5 ; bulk transfer coefficients ; eddy‐covariance ; lakes ; reservoirs
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2021-08-16
    Description: 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.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 3
    Publication Date: 2020-02-12
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 4
    Publication Date: 2021-07-05
    Description: While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 5
    Publication Date: 2021-07-14
    Description: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).
    Language: English
    Type: info:eu-repo/semantics/article
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  • 6
    Publication Date: 2020-02-12
    Description: Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 7
    Publication Date: 2020-02-12
    Type: info:eu-repo/semantics/article
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  • 8
  • 9
    Publication Date: 2020-02-12
    Type: info:eu-repo/semantics/article
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  • 10
    Publication Date: 2020-02-12
    Description: The dataset comprises three tables: - Data Set 1: Half-hourly measurement dataset (quality controlled and filtered), derived variables and energy balance components (Franz_ds01.csv) - Data Set 2: Half-hourly fluxes and transfer coefficients derived by bulk aerodynamic transfer models (Franz_ds02.csv) - Data Set 3. Daily courses and cumulative sums of energy balance components for the average day per subperiod including measured and modelled H and LE (Franz_ds03.csv)Eddy covariance measurements were conducted from 23 April to 16 August 2014 on a thermokarst lake in the Siberian Lena River Delta, yielding direct measurements of sensible (H) and latent (LE) heat flux on half-hourly basis. Ancillary measurements including meteorological variables and water temperature measurements were gathered during the campaign. We derived bulk aerodynamic transfer coefficients in order to parameterize the heat fluxes and compare this in-situ model with independent heat flux parameterization schemes, which are also based on the common bulk transfer algorithm. We further investigated the components of a simple energy balance including measured and modelled H and LE. The dataset was created within the framework of a publication of the study results in Journal of Geophysical Research - Atmospheres (Lake-atmosphere heat flux dynamics of a thermokarst lake in arctic Siberia, by Franz et al.)
    Language: English
    Type: info:eu-repo/semantics/workingPaper
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