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  • 1
    Publication Date: 2012-03-26
    Description: While the Emissions Database for Global Atmospheric Research (EDGAR) focuses on global estimates for the full set of anthropogenic activities, the Land-Use, Land-Use Change and Forestry (LULUCF) sector might be the most diverse and most challenging to cover consistently for all world countries. Parties to UNFCCC are required to provide periodic estimates of GHG emissions, following the latest approved methodological guidance by the International Panel on Climate Change (IPCC). The aim of the current study is comparing the IPCC GPG 2003 and the IPCC AFOLU 2006 by calculating the C stock changes in living forest biomass, and then using computed results to extend the EDGAR database. For this purpose, we applied the IPCC Tier 1 method at global level, i.e. using spatially coarse activity data (i.e. area, obtained combining two different global forest maps: the Global Land Cover map and the eco-zones subdivision of the GEZ Ecological Zone map) in combination with the IPCC default C stocks and C stock change factors. Results for the C stock changes were calculated separately for Gains, Harvest, Net Deforestation and Fires (GFED3), for the years 1990, 2000, 2005 and 2010. At the global level, results obtained with the two set of IPCC guidance differed by about 40%, due to different assumptions and default factors. The IPCC Tier 1 method unavoidably introduced high uncertainties due to the "globalization" of parameters. When the results using IPCC AFOLU 2006 for Annex I countries are compared to other international datasets (UNFCCC, FAO) or scientific publications, it emerges a significant overestimation of the sink. For developing countries, we conclude that C stock change in forest remaining forest can hardly be estimated with Tier 1 method. Overall, confronting the IPCC 2003 and 2006 methodologies we conclude that IPCC 2006 suits best the needs of EDGAR and provide a consistent global picture of C stock changes in living forest biomass independent of country estimates.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 2
    Publication Date: 2012-08-30
    Description: While the Emissions Database for Global Atmospheric Research (EDGAR) focuses on global estimates for the full set of anthropogenic activities, the Land Use, Land-Use Change and Forestry (LULUCF) sector might be the most diverse and most challenging to cover consistently for all countries of the world. Parties to United Nations Framework Convention on Climate Change (UNFCCC) are required to provide periodic estimates of greenhouse gas (GHG) emissions, following the latest approved methodological guidance by the International Panel on Climate Change (IPCC). The current study aims to consistently estimate the carbon (C) stock changes from living forest biomass for all countries of the world, in order to complete the LULUCF sector in EDGAR. In order to derive comparable estimates for developing and developed countries, it is crucial to use a single methodology with global applicability. Data for developing countries are generally poor, such that only the Tier 1 methods from either the IPCC Good Practice Guide for Land Use, Land-Use Change and Forestry (GPG-LULUCF) 2003 or the IPCC 2006 Guidelines can be applied to these countries. For this purpose, we applied the IPCC Tier 1 method at global level following both IPCC GPG-LULUCF 2003 and IPCC 2006, using spatially coarse activity data (i.e. area, obtained combining two different global forest maps: the Global Land Cover map and the eco-zones subdivision of the Global Ecological Zone (GEZ) map) in combination with the IPCC default C stocks and C stock change factors. Results for the C stock changes were calculated separately for gains, harvest, fires (Global Fire Emissions Database version 3, GFEDv.3) and net deforestation for the years 1990, 2000, 2005 and 2010. At the global level, results obtained with the two sets of IPCC guidance differed by about 40 %, due to different assumptions and default factors. The IPCC Tier 1 method unavoidably introduced high uncertainties due to the "globalization" of parameters. When the results using IPCC 2006 for Annex I Parties are compared to other international datasets such as (UNFCCC, Food and Agriculture Organization of the United Nations (FAO)) or scientific publications, a significant overestimation of the sink emerges. For developing countries, we conclude that C stock change in forest remaining forest can hardly be estimated with the Tier 1 method especially for calculating the C losses, mainly because wood removal data are not separately available on harvesting or deforestation. Overall, confronting the IPCC GPG-LULUCF 2003 and IPCC 2006 methodologies, we conclude that IPCC 2006 suits best the needs of EDGAR and provide a consistent global picture of C stock changes from living forest biomass independent of country estimates.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2011-10-01
    Print ISSN: 1748-9318
    Electronic ISSN: 1748-9326
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Institute of Physics
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  • 4
    Publication Date: 2019-08-24
    Description: Tree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D2H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D2H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D2H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D2H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D2H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D2H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D2H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D2H without the adverse effects of loss in biomass prediction accuracy.
    Print ISSN: 0015-752X
    Electronic ISSN: 1464-3626
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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