Modelling random uncertainty of eddy covariance flux measurements

被引:0
|
作者
Domenico Vitale
Massimo Bilancia
Dario Papale
机构
[1] University of Tuscia,Department for Innovation in Biological, Agro
[2] University of Bari Aldo Moro,Food and Forest Systems (DIBAF)
来源
Stochastic Environmental Research and Risk Assessment | 2019年 / 33卷
关键词
Eddy covariance; Net ecosystem exchange; Global warming; Uncertainty; Conditional heteroskedasticity; Time series; Ecology;
D O I
暂无
中图分类号
学科分类号
摘要
The eddy-covariance (EC) technique is considered the most direct and reliable method to calculate flux exchanges of the main greenhouse gases over natural ecosystems and agricultural fields. The resulting measurements are extremely important to characterize ecosystem exchanges of carbon, water, energy and other trace gases, and are widely used to validate or constrain parameter of land surface models via data assimilation techniques. For this purpose, the availability of both complete half-hourly flux time series and its associated uncertainty is mandatory. However, uncertainty estimation for EC data is challenging because the standard procedures based on repeated sampling are not suitable for this kind of measurements, and the presence of missing data makes it difficult to build any sensible time series model with time-varying second-order moments that can provide estimates of total random uncertainty. To overcome such limitations, this paper describes a new method in the context of the strategy based on the model residual approach proposed by Richardson et al. (Agric For Meteorol 148(1): 38–50, 2008). The proposed approach consists in (1) estimating the conditional mean process as representative of the true signal underlying observed data and (2) estimating the conditional variance process as representative of the total random uncertainty affecting EC data. The conditional mean process is estimated through the multiple imputation algorithm recently proposed by Vitale et al. (J Environ Inform https://doi.org/10.3808/jei.201800391, 2018). The conditional variance process is estimated through the stochastic volatility model introduced by Beltratti and Morana (Econ Notes 30(2): 205–234, 2001). This strategy is applied to ten sites that are part of FLUXNET2015 dataset, selected in such a way to cover various ecosystem types under different climatic regimes around the world. The estimated uncertainty is compared with estimates by other well-established methods, and it is demonstrated that the scaling relationship between uncertainty and flux magnitude is preserved. Additionally, the proposed strategy allows obtaining a complete half-hourly time series of uncertainty estimates, which are expected to be useful for many users of EC flux data.
引用
收藏
页码:725 / 746
页数:21
相关论文
共 50 条
  • [31] Eddy-Covariance Flux Measurements in the Complex Terrain of an Alpine Valley in Switzerland
    Rebecca Hiller
    Matthias J. Zeeman
    Werner Eugster
    Boundary-Layer Meteorology, 2008, 127 : 449 - 467
  • [32] Eddy covariance measurements of the sea spray aerosol flux over the open ocean
    Norris, Sarah J.
    Brooks, Ian M.
    Hill, Martin K.
    Brooks, Barbara J.
    Smith, Michael H.
    Sproson, David A. J.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2012, 117
  • [33] Tolerance of eddy covariance flux measurement
    Kim, Wonsik
    Cho, Jaeil
    Komori, Daisuke
    Aoki, Masatoshi
    Yokozawa, Masayuki
    Kanae, Shinjiro
    Oki, Taikan
    HYDROLOGICAL RESEARCH LETTERS, 2011, 5 : 73 - 77
  • [34] Design of the AmeriFlux portable eddy covariance system and uncertainty analysis of carbon measurements
    Ocheltree, T. W.
    Loescher, H. W.
    JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2007, 24 (08) : 1389 - 1406
  • [35] Characteristics of the relative sampling error and its application to flux aggregation in eddy covariance measurements
    Kim, Wonsik
    Seo, Hyeongho
    Komori, Daisuke
    Cho, Jaeil
    JOURNAL OF AGRICULTURAL METEOROLOGY, 2020, 76 (02) : 89 - 95
  • [36] Determination of oceanic ozone deposition by ship-borne eddy covariance flux measurements
    Bariteau, L.
    Helmig, D.
    Fairall, C. W.
    Hare, J. E.
    Hueber, J.
    Lang, E. K.
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2010, 3 (02) : 441 - 455
  • [37] Field intercomparison of four methane gas analyzers suitable for eddy covariance flux measurements
    Peltola, O.
    Mammarella, I.
    Haapanala, S.
    Burba, G.
    Vesala, T.
    BIOGEOSCIENCES, 2013, 10 (06) : 3749 - 3765
  • [38] Progress in water and energy flux studies in Asia: A review focused on eddy covariance measurements
    Kang, Minscok
    Cho, Sungsik
    JOURNAL OF AGRICULTURAL METEOROLOGY, 2021, 77 (01) : 2 - 23
  • [39] Eddy covariance CO2 flux measurements in nocturnal conditions:: An analysis of the problem
    Aubinet, Marc
    ECOLOGICAL APPLICATIONS, 2008, 18 (06) : 1368 - 1378
  • [40] Eddy Covariance Measurements of Methane Flux at a Tropical Peat Forest in Sarawak, Malaysian Borneo
    Tang, Angela C. I.
    Stoy, Paul C.
    Hirata, Ryuichi
    Musin, Kevin K.
    Aeries, Edward B.
    Wenceslaus, Joseph
    Melling, Lulie
    GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (09) : 4390 - 4399