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  • English  (3)
  • Japanese
  • 2020-2022  (3)
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  • English  (3)
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  • 1
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    Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands (2021)
    In:  Agricultural and Forest Meteorology
    Details
    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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  • 2
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    The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (2020)
    In:  Scientific Data
    Details
    Publication Date: 2020-12-14
    Description: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
    Language: English
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 3
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    Storm-induced turbulence alters shelf sea vertical fluxes (2021)
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    Publication Date: 2021-11-30
    Description: Storms are infrequent, intense, physical forcing events that represent a potentially significant driver of ocean ecosystems. The objective of this study was to assess changes in water column structure and turbulent fluxes caused by storms using an autonomous underwater glider, as well as the chlorophyll a (Chl a) response to the altered physical environment. The glider was able to measure throughout the complete life cycle of Storm Bertha as it passed over the North Sea in August 2014, from its arrival to dissipation. Storm Bertha triggered rapid mixing of the thermocline through shear instability, increasing vertical fluxes by nearly an order of magnitude, and promoting increases in surface layer Chl a. The results demonstrate that storms represent a significant fraction of seasonal vertical turbulent fluxes, with potentially important consequences for biological production in shelf seas.
    Keywords: 551.46 ; 551.46 ; North Sea ; storm Bertha ; thermocline fluxes ; thermocline mixing ; storm Bertha ; North Sea
    Language: English
    Type: map
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