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Ageing and competition reduce treesâ ability to capture resources, which predisposes them to death. In this study, the effect of senescence on the survival probability of Norway spruce ( (L.) Karst.) was analysed by fitting alternative survival probability models. Different model formulations were compared in the dataset, which comprised managed and unmanaged plots in long-term forest experiments in Finland and Norway, as well as old-growth stands in Finland. Stand total age ranged from 19 to 290 years. Two models were formulated without an age variable, such that the negative coefficient for the squared stem diameter described a decreasing survival probability for the largest trees. One of the models included stand age as a separate independent variable, and three models included an interaction term between stem diameter and stand age. According to the model including stand age and its interaction with stem diameter, the survival probability curves could intersect each other in stands with a similar structure but a different mean age. Models that did not include stand age underestimated the survival rate of the largest trees in the managed stands and overestimated their survival rate in the old-growth stands. Models that included stand age produced more plausible predictions, especially for the largest trees. The results supported the hypothesis that the stand age and senescence of trees decreases the survival probability of trees, and that the ageing effect improves survival probability models for Norway spruce.Picea abies

Monitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes and large spatial variation. Stratification of soil sampling allows the reduction of sample size without reducing precision. If there are no previous measurements, the stratification can be made with model-predictions of target variable. Our aim was to present a simulation-based stratification method, and to estimate how much stratification of inventory plots could improve the efficiency of the sampling. The effect of large uncertainties related to soil C change measurements and simulated predictions was targeted since they may considerably decrease the efficiency of stratification. According to our simulations, stratification can be useful with a feasible soil sample number if other uncertainties (simulated predictions and forecasted forest management) can be controlled. For example, the optimal (Neyman) allocation of plots to 4 strata with 10 soil samples from each plot (unpaired repeated sampling) reduced the standard error (SE) of the stratified mean by 9â34% from that of simple random sampling, depending on the assumptions of uncertainties. When the uncertainties of measurements and simulations were not accounted for in the division to strata, the decreases of SEs were 2â9 units less. Stratified sampling scheme that accounts for the uncertainties in measured material and in the correlates (simulated predictions) is recommended for the sampling design of soil C stock changes.

Countries need to assess changes in the carbon stocks of forest soils as a part of national greenhouse gas (GHG) inventories under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol (KP). Since measuring these changes is expensive, it is likely that many countries will use alternative methods to prepare these estimates. We reviewed seven well-known soil carbon models from the point of view of preparing country-scale soil C change estimates. We first introduced the models and explained how they incorporated the most important input variables. Second, we evaluated their applicability at regional scale considering commonly available data sources. Third, we compiled references to data that exist for evaluation of model performance in forest soils. A range of process-based soil carbon models differing in input data requirements exist, allowing some flexibility to forest soil C accounting. Simple models may be the only reasonable option to estimate soil C changes if available resources are limited. More complex models may be used as integral parts of sophisticated inventories assimilating several data sources. Currently, measurement data for model evaluation are common for agricultural soils, but less data have been collected in forest soils. Definitions of model and measured soil pools often differ, ancillary model inputs require scaling of data, and soil C measurements are uncertain. These issues complicate the preparation of model estimates and their evaluation with empirical data, at large scale. Assessment of uncertainties that accounts for the effect of model choice is important part of inventories estimating large-scale soil C changes. Joint development of models and large-scale soil measurement campaigns could reduce the inconsistencies between models and empirical data, and eventually also the uncertainties of model predictions.

As soil microbial respiration is the major component of land CO2 emissions, differences in the functional dependence of respiration on soil moisture among Earth system models (ESMs) contributes significantly to the uncertainties in their projections. Using soil organic C (SOC) stocks and CO2 data from a boreal forest–mire ecotone in Finland and Bayesian data assimilation, we revised the original precipitation-based monotonic saturation dependency of the Yasso07 soil carbon model using the non-monotonic Ricker function based on soil volumetric water content. We fit the revised functional dependency of moisture to the observed microbial respiration and SOC stocks and compared its performance against the original Yasso07 model and the version used in the JSBACH land surface model with a reduction constant for decomposition rates in wetlands. The Yasso07 soil C model coupled with the calibrated unimodal Ricker moisture function with an optimum in well-drained soils accurately reconstructed observed SOC stocks and soil CO2 emissions and clearly outperformed previous model versions on paludified organo-mineral soils in forested peatlands and water-saturated organic soils in mires. The best estimate of the posterior moisture response of decomposition used both measurements of SOC stocks and CO2 data from the full range of moisture conditions (from dry and xeric to wet and water-saturated soils). We observed unbiased residuals of SOC and CO2 data modelled with the moisture optimum in well-drained soils, suggesting that this modified function accounts more precisely for the long-term SOC change dependency according to ecosystem properties as well as the contribution of short-term CO2 responses including extreme events. The optimum moisture for decomposition in boreal forests was found in well-drained soils instead of the mid-range between dry and water-saturated conditions as is commonly assumed among soil C and ESMs. Although the unimodal moisture modifier with an optimum in well-drained soils implicitly incorporates robust biogeochemical mechanisms of SOC accumulation and CO2 emissions, it needs further evaluation with large-scale data to determine if its use in land surface models will decrease the uncertainty in projections. [ABSTRACT FROM AUTHOR]

Peatland management practices, such as drainage and restoration, have a strong effect on boreal peatland methane (CH4) fluxes. Furthermore, CH4 fluxes are strongly controlled by local environmental conditions, such as soil hydrology, temperature and vegetation, which are all experiencing considerable changes due to climate change. Both management practices and climate change are expected to influence peatland CH4 fluxes during this century but the magnitude and net impact of these changes is still insufficiently understood. In this study, we simulated the impacts of two forest management practices, rotational forestry and continuous cover forestry, as well as peatland restoration on hypothetical forestry-drained peatlands across Finland using the land surface model JSBACH coupled with the soil carbon model YASSO and peatland methane model HIMMELI. We further simulated the impacts of climatic warming using two RCP (Representative Concentration Pathway) emission scenarios, RCP 2.6 and RCP 4.5. We investigated the response of CH4 fluxes, soil water-table level (WTL), soil temperatures, and soil carbon dynamics to changes in management practices and climate. Our results show that management practices have a strong impact on peatland WTLs and CH4 emissions continuing for several decades, with emissions increasing after restoration and clearcutting. Towards the end of the century, WTLs increase slightly likely due to increasing precipitation. CH4 fluxes have opposing trends in restored and drained peatlands. In restored peatlands, CH4 emissions decrease towards the end of the century following the decomposition harvest residue in the top peat layers, while in drained peatland forests sinks get weaker and occasional emissions become more common, likely due to rising WTL and soil temperatures. The strength of these trends vary across the country, with CH4 emissions from restored peatlands decreasing more strongly in southern Finland and forest soil CH4 sinks weakening most in northern Finland. [ABSTRACT FROM AUTHOR]

Continuous cover forestry (CCF) has gained interest as an alternative to even-aged management particularly on drained peatland forests. However, relatively little is known about the physiological response of suppressed trees when larger trees are removed as a part of CCF practices. Consequently, studies concentrating on process-level modeling of the response of trees to selection harvesting are also rare. Here, we compared, modeled and measured harvest response of previously suppressed Norway spruce (Picea abies) trees to a selection harvest. We quantified the harvest response by collecting Norway spruce tree-ring samples in a drained peatland forest site and measuring the change in stable carbon and oxygen isotopic ratios of wood formed during 2010–20, including five post-harvest years. The measured isotopic ratios were compared with ecosystem-level process model predictions for |${\kern0em }^{13}$| C discrimination and |${\kern0em }^{18}$| O leaf water enrichment. We found that the model predicted similar but lower harvest response than the measurements. Furthermore, accounting for mesophyll conductance was important for capturing the variation in |${\kern0em }^{13}$| C discrimination. In addition, we performed sensitivity analysis on the model, which suggests that the modeled |${\kern0em }^{13}$| C discrimination is sensitive to parameters related to CO2 transport through stomata to the mesophyll. [ABSTRACT FROM AUTHOR]

In Finland, wind-induced forest damage is expected to increase in the future. Demand exists for timely and precise first-hand information about the main impact area of windstorms. Locating potential damage areas quickly is essential for effective operational planning of salvage loggings, aiming to reduce monetary losses of timber and risk for secondary damage caused by insects. This study presents an approach for mapping the potential damage areas immediately after a windstorm, by using high-resolution forest vulnerability data and information on the spatial distribution of maximum wind gust speed derived from weather station observations using kriging with external drift interpolation. The new method is evaluated by analyzing damage caused by nine major windstorms of the 2010s in Finland. Our results show that including roughness length information as an auxiliary variable in the interpolation improved the results and produced wind maps with more plausible structure and better separation between forested and non-forested land areas. The forest vulnerability data were most strongly linked to damage, whilst wind gust speed had weaker results. However, for future storms with unknown damage areas, we consider maximum wind gust speed still essential for defining the main impact area, whereas forest vulnerability data could then be used for more detailed damage predictions. Further advancements of wind interpolation approaches, preferably towards higher resolution and, if possible, based on a denser and more diverse observation network, is needed to fully exploit the potential of combined wind and forest vulnerability data. Albeit we recognize multiple uncertainties, room for improvements and benefits that additional data sources would bring, our study demonstrates a simple approach for rapid mapping of potential forest wind damage areas, which could be further developed into an operational tool. [ABSTRACT FROM AUTHOR]

As soil microbial respiration is the major component of land CO2 emissions, differences in the functional dependence of respiration on soil moisture among the Earth system models (ESM) contributes significantly to the uncertainties in their projections. Using soil organic C (SOC) stocks and CO2 data from a boreal forest – mire ecotone in Finland and Bayesian data assimilation, we revised the precipitation-based environmental function of the Yasso07 soil carbon model. We fit this function to the observed microbial respiration response to moisture and compared its performance against the original Yasso07 model and the version used in the JSBACH land surface model with a reduction constant for decomposition rates in wetlands. The Yasso07 soil C model coupled with the calibrated unimodal moisture function with an optimum in dry soils accurately reconstructed observed SOC stocks and soil CO2 emissions and clearly outperformed previous model versions on paludified organo-mineral soils in forested peatlands and water-saturated organic soils in mires. The best estimate of the posterior moisture response of decomposition used both measurements of SOC stocks and CO2 data from the full range of moisture conditions (from dry/xeric to wet/water-saturated soils). We observed unbiased residuals of SOC and CO2 data modelled with the moisture optimum in well-drained soils, suggesting that this modified function accounts more precisely for the long-term SOC change dependency according to ecosystem properties as well as the contribution of short term CO2 responses including extreme events. The optimum moisture for decomposition in boreal forests was in dry well-drained soils instead of the mid-range between dry and water-saturated conditions as is commonly assumed among many soil C and ESM models. Although the unimodal moisture modifier with an optimum in well-drained soils implicitly incorporates robust biogeochemical mechanisms of SOC accumulation and CO2 emissions, it needs further evaluation with large scale data to determine if its use in land surface models will decrease the uncertainty of projections. [ABSTRACT FROM AUTHOR]