What causes differences between national estimates of forest management carbon emissions and removals compared to estimates of large-scale models?

Groen, T.A.; Verkerk, Pieter Johannes; Böttcher, Hannes; Grassi, Giacomo; Cienciala, Emil; Black, Kevin; Fortin, Mathieu; Köthke, Margret; Lehtonen, Aleksi; Nabuurs, Gert‐Jan; Petrova, L.I.; Blujdea, Viorel

doi:10.1016/j.envsci.2013.06.005

Cited by 16 publications

(13 citation statements)

References 12 publications

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“…Over the last two decades, there has been an increasing demand for large-area predictions at the regional and national levels mainly due to international agreements concerning climate change (Ciais et al, 2008;Groen et al, 2013). Uncertainty estimation of large-area predictions is not straightforward for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Estimating model- and sampling-related uncertainty in large-area growth predictions

Melo

Schneider

Fortin

2018

Ecological Modelling

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Estimating uncertainty in forest growth predictions is essential to support largearea policies and decisions. The aim of this study was to estimate model and sampling uncertainties at a regional level. To do this, we generated forest growth predictions for three ecotypes in the Bas-Saint-Laurent region of Quebec, Canada. Predictions were generated using the ARTEMIS growth model that allows for stochasticity in some of the sub-models. We used a bootstrap hybrid estimator to estimate the variances arising from the model and the sampling. Moreover, the variance due to the model was further decomposed to determine which dynamic sub-model induced the greatest share of variance. Results revealed that sampling accounted for most of the variance in shortterm predictions. In long-term predictions, the model contribution turned out to be as important as that of the sampling. The variance decomposition per sub-model indicated that the mortality sub-model induced the highest variability in the predictions. These results were consistent for the three ecotypes. We recommend that efforts in variance reduction focus on increasing the sample size in short-term predictions and on improving the mortality sub-model in long-term predictions.

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Section: Introductionmentioning

confidence: 99%

Estimating model- and sampling-related uncertainty in large-area growth predictions

Melo

Schneider

Fortin

2018

Ecological Modelling

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“…The inventory statistics provide estimates of the population structure and composition at various points in time (Groen et al 2013). The activity data are in the form of carbon gains and drains.…”

Section: Gain-loss Methodsmentioning

confidence: 99%

Error propagation in stock-difference and gain–loss estimates of a forest biomass carbon balance

2014

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According to the United Nations International Panel on Climate Change good practice guidance, an annual forest biomass carbon balance (AFCB) can be estimated by either the stock-difference (SD) or the gainloss (GL) method. An AFCB should be accompanied by an analysis and estimation of uncertainty (EU). EUs are to be practicable and supported by sound statistical methods. Sampling and model errors both contribute to an EU. As sample size increases, the sampling error decreases but not the error due to errors in model parameters. Uncertainty in GL AFCB estimates is dominated by model-parameter errors. This study details the delta technique for obtaining an EU with the SD and the GL method applicable to the carbon in aboveground forest biomass. We employ a Brownian bridge process to annualize the uncertainty in SD AFCBs. A blend of actual and simulated data from three successive inventories are used to demonstrate the application of the delta technique to SD-and GL-derived AFCBs during the years covered by the three inventories (SD) and rescaled national wood volume harvest statistics (GL). Examples are limited to carbon in live trees with a stem diameter of 7 cm or greater. We confirm that a large contribution to the uncertainty in an AFCB comes from models used to estimate biomass. Application of the delta technique to summary statistics can significantly underestimate uncertainty as some sources of uncertainty cannot be quantified from the available information. We discuss limitations and problems with the Monte Carlo technique for quantifying uncertainty in an AFCB.

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“…Also the distribution of harvest over thinning and final fellings is different between the models. The projections by both models have also been compared with data from greenhouse gas inventories that European countries submit annually to the UNFCCC (Groen et al 2013). Differences exist between estimates of EFISCEN and G4M on the one hand and national estimates on the other hand.…”

Section: Comparison With Other Datamentioning

confidence: 99%

Assessing impacts of intensified biomass removal and biodiversity protection on European forests

Verkerk¹

2015

Diss. For.

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Verkerk P.J. (2015). Assessing impacts of intensified biomass removal and biodiversity protection on European forests. Dissertationes Forestales 197. 50 p. http://dx.doi.org/10. 14214/df.197 Forests provide many benefits to society and it is important to understand if, and how, policies affect the provisioning of ecosystem services. The objective of this dissertation was to analyze and evaluate impacts of intensified biomass production and biodiversity protection on ecosystem services provided by European forests.Article I assessed to what extent forests are protected and how felling restrictions affect the potential annual wood supply. Felling restrictions applied to currently protected forest areas reduce the long-term potential supply of wood by 35 million m 3 yr -1 . Despite these restrictions, wood harvesting is allowed to a fair extent in these protected forests.Articles II-V assessed the future woody biomass potentials and impacts of different scenarios on forests using the European Forest Information SCENario model (EFISCEN). In article II, the realisable woody biomass potential was estimated at 741 million m 3 yr -1 in 2010, including woody biomass from stems, residues, stumps and other biomass, ranging from 620 to 891 million m 3 yr -1 in 2030. Mobilising these potentials would imply drastic changes in the management of European forests.According to articles III-V intensified biomass removals could involve trade-offs with other forest ecosystem services. Carbon storage in forest biomass, as well as the amount of deadwood, was projected to decline due to measures to intensify the use of forests. An economic valuation showed that intensifying biomass removals could lead to a net economic benefit measured by the aggregated value of five ecosystem services, as compared to projections without measures to intensify use of forest biomass. Larger social benefits could potentially be obtained if biodiversity protection is enhanced in European forests.The results presented in this dissertation illustrate that careful planning is required to accommodate the need for protection of biodiversity, the expected growing demand for wood, as well as the provisioning of forest ecosystem services. Keywords: ecosystem services, EFISCEN, scenario analysis 4 ACKOWLEDGEMENTSFinally the moment is there that the preface of my dissertation can be written. This means that the work on my dissertation is soon completed, which is a liberating thought! This academic dissertation is the result of work carried out during several years in various projects in which I have been involved as part of my work at the European Forest Institute (EFI). I would like to thank EFI and especially my supervisor Marcus Lindner for providing me the opportunity to work on these projects. I also would like to thank him for all the support he provided throughout the years. It has been a real pleasure to work with him. I also would like to thank all (current and former) colleagues in Joensuu and Barcelona that have made EFI an interesting place to...

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What causes differences between national estimates of forest management carbon emissions and removals compared to estimates of large-scale models?

Cited by 16 publications

References 12 publications

Estimating model- and sampling-related uncertainty in large-area growth predictions

Estimating model- and sampling-related uncertainty in large-area growth predictions

Error propagation in stock-difference and gain–loss estimates of a forest biomass carbon balance

Assessing impacts of intensified biomass removal and biodiversity protection on European forests

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