Mattias Lundblad

Stand transpiration and sapflow density in relation to weather, soil moisture and stand characteristics

Sapflow density was measured in six stands in a boreal forest in central Sweden, to assess its dependence on soil moisture and stand characteristics. The stands were mixed and pure Scots pine and Norway spruce stands, which were between 34 and 105 years old. Sapflow was measured in 12 trees per stand using the Granier method during two contrasting growing seasons; one warm and dry and one wet and cool. The canopy conductance of the stands was estimated by the inverse of the Penman-Monteith equation, using time-lag-adjusted sapflow as input. Maximum canopy conductance varied between 8 mm s(-1) and 88 mm s(-1) for the stand with the lowest and highest conductance, respectively. Transpiration was higher in the dry, warm season, mean values for the different stands ranging between 1.30 to 4.64 mm day(-1) during July to September. The corresponding range in the wet, cool season was 0.95 to 2.65 mm day(-1). Besides climatic factors, stand age, stem density and diameter explained most of the variation in sapflow density. By use of multiple regression analysis for 5-day periods it was possible to estimate sapflow density and transpiration for a larger area of the forest. This upscaled area) transpiration was compared with evaporation measured by an eddy-correlation system located centrally in the area. It was shown that areal transpiration constituted 78% of total evaporation in the warm, dry season and 52% in the wet, cool season. It was not possible to establish with confidence a critical limit for soil water at which transpiration began to be reduced, mainly because of wide scatter in the relationship between potential and actual transpiration. (Less)

The incentive gap: LULUCF and the Kyoto mechanism before and after Durban

To‐date, forest resource‐based carbon accounting in land use, land use change and forestry (LULUCF) under the United Nations Framework Convention on Climate Change (UNFCCC), Kyoto Protocol (KP), European Union (EU) and national level emission reduction schemes considers only a fraction of its potential and fails to adequately mobilize the LULUCF sector for the successful stabilization of atmospheric greenhouse gas (GHG) concentrations. Recent modifications at the 2011 COP17 meetings in Durban have partially addressed this basic problem, but leave room for improvement. The presence of an Incentive Gap (IG) continues to justify reform of the LULUCF carbon accounting framework. Frequently neglected in the climate change mitigation and adaptation literature, carbon accounting practices ultimately define the nuts and bolts of what counts and which resources (forest, forest‐based or other) are favored and utilized. For Annex I countries in the Kyoto Mechanism, the Incentive Gap under forest management (FM) is significantly large: some 75% or more of potential forestry‐based carbon sequestration is not effectively incentivized or mobilized for climate change mitigation and adaptation (Ellison et al. 2011a). In this paper, we expand our analysis of the Incentive Gap to incorporate the changes agreed in Durban and encompass both a wider set of countries and a larger set of omitted carbon pools. For Annex I countries, based on the first 2 years of experience in the first Commitment Period (CP1) we estimate the IG in FM at approximately 88%. Though significantly reduced in CP2, the IG remains a problem. Thus our measure of missed opportunities under the Kyoto and UNFCCC framework – despite the changes in Durban – remains important. With the exception perhaps of increased energy efficiency, few sinks or sources of reduced emissions can be mobilized as effectively and efficiently as forests. Thus, we wonder at the sheer magnitude of this underutilized resource.

Enhanced incentives for mitigation efforts in the Land Use. : Land Use Change and Forestry sector in the next global climate change agreement

The Nordic Council of Ministers has set up the Nordic COP 15 Group to help achieve a successful outcome in the climate change negotiations at COP 15 in Copenhagen in December 2009. The Nordic COP 15 Group have identified key elements in the negotiations where efforts are needed to ensure a good outcome, i.e. adaptation to climate change, technolo-gy transfer, legal issues, sinks and deforestation. This report explore ways to adjust the current accounting rules on sinks into rules that would create better incen-tives for actively managing lands, in order to decrease the emission of greenhouse gases to the atmosphere and/ or to remove greenhouse gases from the atmosphere. Different proposals for the treatment of the LULUCF-sector (Land Use, Land Use Change and Forestry sector) are compared and analysed.

Assessing uncertainty: Sample size trade-offs in the development and application of carbon stock models

Many parties to the United Nation’s Framework Convention on Climate Change (UNFCCC) base their reporting of change in Land Use, Land-Use Change and Forestry (LULUCF) sector carbon pools on national forest inventories. A strong feature of sample-based inventories is that very detailed measurements can be made at the level of plots. Uncertainty regarding the results stems primarily from the fact that only a sample, and not the entire population, is measured. However, tree biomass on sample plots is not directly measured but rather estimated using regression models based on allometric features such as tree diameter and height. Estimators of model parameters are random variables that exhibit different values depending on which sample is used for estimating model parameters. Although sampling error is strongly influenced by the sample size when the model is applied, modeling error is strongly influenced by the sample size when the model is under development. Thus, there is a trade-off between which sample sizes to use when applying and developing models. This trade-off has not been studied before and is of specific interest for countries developing new national forest inventories and biomass models in the REDD context. This study considers a specific sample design and population. This fact should be considered when extrapolating results to other locations and populations.