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  • English  (7)
  • 2020-2023  (4)
  • 2020-2022  (3)
  • 1955-1959
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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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    Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (2021)
    In:  Scientific Data
    Details
    Publication Date: 2022-02-08
    Description: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions. © 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
    Language: English
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 3
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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
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  • 4
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    A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps (2022)
    In:  Remote Sensing of Environment
    Details
    Publication Date: 2022-02-11
    Description: Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 5
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    A safe operating space for New Zealand/Aotearoa (2020)
    Ministry for the Environment
    Details
    Publication Date: 2022-03-21
    Language: English
    Type: info:eu-repo/semantics/report
    Format: application/pdf
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    CITATION PIK
  • 6
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    Comprehensive analysis of chemical and biological problems associated with browning agents used in aquatic studies (2021)
    John Wiley & Sons, Inc. | Hoboken, USA
    Details
    Publication Date: 2022-04-07
    Description: Inland waters receive and process large amounts of colored organic matter from the terrestrial surroundings. These inputs dramatically affect the chemical, physical, and biological properties of water bodies, as well as their roles as global carbon sinks and sources. However, manipulative studies, especially at ecosystem scale, require large amounts of dissolved organic matter with optical and chemical properties resembling indigenous organic matter. Here, we compared the impacts of two leonardite products (HuminFeed and SuperHume) and a freshly derived reverse osmosis concentrate of organic matter in a set of comprehensive mesocosm‐ and laboratory‐scale experiments and analyses. The chemical properties of the reverse osmosis concentrate and the leonardite products were very different, with leonardite products being low and the reverse osmosis concentrate being high in carboxylic functional groups. Light had a strong impact on the properties of leonardite products, including loss of color and increased particle formation. HuminFeed presented a substantial impact on microbial communities under light conditions, where bacterial production was stimulated and community composition modified, while in dark potential inhibition of bacterial processes was detected. While none of the browning agents inhibited the growth of the tested phytoplankton Gonyostomum semen, HuminFeed had detrimental effects on zooplankton abundance and Daphnia reproduction. We conclude that the effects of browning agents extracted from leonardite, particularly HuminFeed, are in sharp contrast to those originating from terrestrially derived dissolved organic matter. Hence, they should be used with great caution in experimental studies on the consequences of terrestrial carbon for aquatic systems.
    Description: Marie Curie International Outgoing Fellowship
    Description: Swedish Research Council Formas http://dx.doi.org/10.13039/501100001862
    Description: Knut and Alice Wallenberg Foundation http://dx.doi.org/10.13039/501100004063
    Keywords: ddc:551.48 ; ddc:550.724
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 7
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    Storm-induced turbulence alters shelf sea vertical fluxes (2021)
    Details
    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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    CITATION GEO-LEO
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