Changed thinning regimes may increase carbon stock under climate change: A case study from a Finnish boreal forest

A physiological growth and yield model was applied for assessing the effects of forest management and climate change on the carbon (C) stocks in a forest management unit located in Finland. The aim was to outline an appropriate management strategy with regard to C stock in the ecosystem (C in trees and C in soil) and C in harvested timber. Simulations covered 100 years using three climate scenarios (current climate, ECHAM4 and HadCM2), five thinning regimes (based on current forest management recommendations for Finland) and one unthinned. Simulations were undertaken with ground true stand inventory data (1451 hectares) representing Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and silver birch (Betula pendula) stands. Regardless of the climate scenario, it was found that shifting from current practices to thinning regimes that allowed higher stocking of trees resulted in an increase of up to 11% in C in the forest ecosystem. It also increased the C in the timber yield by up to 14%. Compared to current climatic conditions, the mean increase over the thinning regimes in the total C stock in the forest ecosystem due to the climate change was a maximum of 1%; but the mean increase in total C in timber yield over thinning regimes was a maximum of 12%.     

Drag coefficients and turbulence spectra within three boreal forest canopies

Atmospheric turbulence was measured within a black spruce forest, a jack pine forest, and a trembling aspen forest, located in southeastern Manitoba, Canada. Drag coefficients (C _d ) varied little with height within the pine and aspen canopies, but showed some height dependence within the dense spruce canopy. A constant C _d of 0.15, with the measured momentum flux and velocity profiles, gave good estimates of leaf-area-index (LAI) profiles for the pine and aspen canopies, but underestimated LAI for the spruce canopy.Velocity spectra were scaled using the Eulerian integral time scales and showed a substantial inertial subrange above the canopies. In the bottom part of the canopies, the streamwise and cross-stream spectra showed rapid energy loss whereas the vertical spectra showed an apparent energy gain, in the region where the inertial subrange is expected. The temperature spectra showed an inertial subrange with the expected -2/3 slope at all heights. Cospectra of momentum and heat flux had slopes of about -1 in much of the inertial subrange. Possible mechanisms to explain some of the spectral features are discussed.     

Modelling Carbon Dynamics of Boreal Forest Ecosystems Using the Canadian Land Surface Scheme

The ecosystem carbon (C) and nitrogen (N) simulations recently implemented in the Canadian Land Surface Scheme (CLASS) are presented. The main calculations include plant photosynthesis, autotrophic respiration, root N uptake, litterfall, plant growth, and soil heterotrophic respiration. Model experiments are made on two boreal forest ecosystems, deciduous (aspen) and conifers (black spruce). Simulated plant, soil, and ecosystem CO₂ exchanges are analysed on half-hourly, daily, and annual time scales and compared with tower eddy correlation flux measurements and estimates from various authors. Modeled daily ecosystem CO₂ exchange explained86% and 54%, respectively, of the observed variance of eddy correlationflux at the aspen site and at the black spruce site. Annual results show that the aspen ecosystem was simulated as a C sink in both 1994 (+164 g Cm^–2) and 1996 (+142 g C m^–2), and the black spruce ecosystem wassimulated as a C sink in 1994 (+39 g C m^–2) and 1995 (+25 g Cm^–2), but as a C source in 1996 (–27 g C m^–2).     

Overestimated Biomass Carbon Pools of the Northern mid- and High Latitude Forests

The biomass carbon (C) stock of forests is one of key parameters for the study of regional and global carbon cycles. Literature reviews shows that inventory-based forest C stocks documented for major countries in the middle and high northern latitudes fall within a narrow range of 36–56 Mg C ha⁻¹ with an overall area-weighted mean of 43.6 Mg C ha⁻¹. These estimates are 0.40 to 0.71 times smaller than those (61–108 Mg C ha⁻¹) used in previous analysis of balancing the global carbon budget. A statistical analysis, using the global forest biomass database, implies that aboveground biomass per hectare is proportional to forest mean height [biomass in Mg/ha = 10.63 (height in m)] in closed-canopy forests in the study regions, indicating that forest height can be a proxy of regional biomass C stocks. The narrow range of C stocks is likely a result of similar forest height across the northern regions. The lower biomass C stock obtained in this study strongly suggests that the role of the northern forests in the global carbon cycle needs to be re-evaluated. Our findings also suggest that regional estimates of biomass could be readily made from the use of satellite methods such as lidar that can measure forest canopy height over large regions.     

A global model of changing N2O emissions from natural and perturbed soils

A high resolution global model of the terrestrial biosphere is developed to estimate changes in nitrous oxide (N₂O) emissions from 1860–1990. The model is driven by four anthropogenic perturbations, including land use change and nitrogen inputs from fertilizer, livestock manure, and atmospheric deposition of fossil fuel NO _x . Global soil nitrogen mineralization, volatilization, and leaching fluxes are estimated by the model and converted to N₂O emissions based on broad assumptions about their associated N₂O yields. From 1860–1990, global N₂O emissions associated with soil nitrogen mineralization are estimated to have decreased slightly from 5.9 to 5.7 Tg N/yr, due mainly to land clearing, while N₂O emissions associated with volatilization and leaching of excess mineral nitrogen are estimated to have increased sharply from 0.45 to 3.3 Tg N/yr, due to all four anthropogenic perturbations. Taking into account the impact of each perturbation on soil nitrogen mineralization and on volatilization and leaching of excess mineral nitrogen, global 1990 N₂O emissions of 1.4, 0.7, 0.4 and 0.08 Tg N/yr are attributed to fertilizer, livestock manure, land clearing and atmospheric deposition of fossil fuel NO _x , respectively. Consideration of both the short and long-term fates of fertilizer nitrogen indicates that the N₂O/fertilizer-N yield may be 2% or more.C. NBM Definitions AET _mon (cm H₂O) = monthly actual evapotranspiration - AET _ann (cm H₂O) = annual actual evapotranspiration - age _h (years) = stand age of herbaceous biomass - age _w (years) = stand age of woody biomass - atmblc (gC/m²/month) = net flux of CO₂ from grid - biotoc (gC/g biomass) = 0.50 = convert g biomass to g C - beff _h = 0.8 = fraction of cleared herbaceous litter that is burned - beff _w = 0.4 = fraction of cleared woody litter that is burned - bfmin = 0.5 = fraction of burned N litter that is mineralized or converted to reactive gases which rapidly redeposit. Remainder assumed pyrodenitrified to N₂. + N₂O - bprob = probability that burned litter will be burned - burn _h (gC/m²/month) = herbaceous litter burned after land clearing - burn _w (gC/m²/month) = woody litter burned after land clearing - cbiomsh (gC/m²) = C herbaceous biomass pool - cbiomsw (gC/m²) = C woody biomass pool - clear (gC/m²/month) = woody litter C removed by land clearing - clearn (gN/m²/month) = woody litter N removed by land clearing - cldh (month^–1) = herbaceous litter decomposition coefficient - cldw (month^–1) = woody litter decomposition coefficient - clittrh (gC/m²) = C herbaceous litter pool - clittrw (gC/m²) = C woody litter pool - clph (month^–1) = herbaceous litter production coefficient - clpw (month^–1) = woody litter production coefficient - cnrath (gC/gN) = C/N ratio in herbaceous phytomass - cnrats (gC/gN) = C/N ratio in soil organic matter - cnratt (gC/gN) = average C/N ratio in total phytomass - cnratw (gC/gN) = C/N ratio in woody phytomass - crod (month^–1) = forest clearing coefficient - csocd (month^–1) = actual soil organic matter decompostion coefficient - decmult decomposition coefficient multiplier; natural =1.0; agricultural =1.0 (1.2 in sensitivity test) - fertmin (gN/m²/month) = inorganic fertilizer input - fleach fraction of excess inorganic N that is leached - fligh (g Lignin/ g C) = lignin fraction of herbaceous litter C - fligw (g Lignin/ g C) = 0.3 = lignin fraction of woody litter C - fln2o = .01–.02 = fraction of leached N emitted as N₂O - fnav = 0.95 = fraction of mineral N available to plants - fosdep (gN/m²/month) = wet and dry atmospheric deposition of fossil fuel NO _x - fresph = 0.5 = fraction of herbaceous litter decomposition that goes to CO₂ respiration - fresps = 0.51 + .068 ^* sand = fraction of soil organic matter decomposition that goes to CO₂ respiration - frespw = 0.3 ^* (^* see comments in Section 2.3 under decomposition) = fraction of woody litter decomposition that goes to CO₂ respiration - fsoil = ratio of NPP measured on given FAO soil type to NPF_miami - fstruct = 0.15 + 0.018 ^* ligton = fraction of herbaceous litter going to structural/woody pool - fvn2o = .05–.10 = fraction of excess volatilized mineral N emitted as N₂O - fvol = .02 = fraction of gross mineralization flux and excess mineral N volatilized - fyield ratio of total agricultural NPP in a given country in 1980 to total NPP_miami of all displaced natural grids in that country - gimmob _h (gN/m²/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of herbaceous litter - gimmob _s (gN/m²/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of soil organic matter - gimmob _w (gN/m²/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of woody litter - graze (gC/m²/month) = C herbaceous biomass grazed by livestock - grazen (gN/m²/month) = N herbaceous biomass grazed by livestock - growth _h (gC/m²/month) = herbaceous litter incorporated into microbial biomass - growth _w (gC/m²/month) = woody litter incorporated into microbial biomass - gromin _h (gN/m²/month) = gross N mineralization due to decomposition and burning of herbaceous litter - gromin _s (gN/m²/month) = gross N mineralization due to decomposition of soil organic matter - gromin _w (gN/m²/month) = gross N mineralization due to decomposition and burning of woody litter - herb herbaceous fraction by weight of total biomass - leach (gN/m²/month) = leaching (& volatilization) losses of excess inorganic N - ligton (g lignin-C/gN) = lignin/N ratio in fresh herbaceous litter - LP _h (gC/m²/month)= C herbaceous litter production - LP (gC/m²/month) = C woody litter production - LPN _h (gN/m²/month) = N herbaceous litter production - LPN _W (gN/m²/month) = N woody litter production - manco2 (gC/m²/month) = grazed C respired by livestock - manlit (gC/m²/month) = C manure input (feces + urine) - n2oint (gN/m²/month) = intercept of N₂O flux vs gromin regression - n2oleach (gN/m²/month) = N₂O flux associated with leaching and volatilization of excess inorganic N - n2onat (gN/m²/month) = natural N₂O flux from soils - n2oslope slope of N₂O flux vs gromin regression - nbiomsh (gN/m²) = N herbaceous biomass pool - nbiomsw (gN/m²) = N woody biomass pool - nfix (gN/m²/month) = N₂ fixation + natural atmospheric deposition - nlittrh (gN/m²) = N herbaceous litter pool - nlittrw (gN/m²) = N woody litter pool - nmanlit (gN/m²/month) = organic N manure input (feces) - nmanmin (gN/m²/month) = inorganic N manure input (urine) - nmin (gN/m²) = inorganic N pool - NPP _acth (gC/m²/month)= actual herbaceous net primary productivity - NPP _actw (gC/m²/month) = actual woody net primary productivity - nvol (gN/m²/month) = volatilization losses from inorganic N pool - plntnav (gN/m²/month)= mineral N available to plants - plntup _h (gN/m²/month) = inorganic N incorporated into herbaceous biomass - plntup _w (gN/m²/month) = inorganic N incorporated into woody biomass - precip _ann (mm) = mean annual precipitation - precip _mon (mm) = mean monthly precipitation - pyroden _h (gN/m²/month) = burned herbaceous litter N that is pyrodenitrified to N₂ - pyroden _w (gN/m²/month) = burned woody litter N that is pyrodenitrified to N₂ - recyc fraction of N that is retranslocated before senescence - resp _h (gC/m²/month) = herbaceous litter CO₂ respiration - resp _s (gC/m²/month) = soil organic carbon CO₂ respiration - resp _w (gC/m²/month) = woody litter CO₂ respiration - sand sand fraction of soil - satrat ratio of maximum NPP to N-limited NPP - soiloc (gC/m²) = soil organic C pool - soilon (gN/m²) = soil organic N pool - temp _ann (°C) = mean annual temperature - temp _mon (°C) = mean monthly temperature Now at the NOAA Aeronomy Laboratory, Boulder, Colorado.     

Temporal Assessment of Growing Stock, Biomass and Carbon Stock of Indian Forests

The dynamics of terrestrial ecosystems depends on interactions between carbon, nutrient and hydrological cycles. Terrestrial ecosystems retain carbon in live biomass (aboveground and belowground), decomposing organic matter, and soil. Carbon is exchanged naturally between these systems and the atmosphere through photosynthesis, respiration, decomposition, and combustion. Human activities change carbon stock in these pools and exchanges between them and the atmosphere through land-use, land-use change, and forestry.In the present study we estimated the wood (stem) biomass, growing stock (GS) and carbon stock of Indian forests for 1984 and 1994. The forest area, wood biomass, GS, and carbon stock were 63.86 Mha, 4327.99 Mm³, 2398.19 Mt and 1085.06 Mt respectively in 1984 and with the reduction in forest area, 63.34 Mha, in 1994, wood biomass (2395.12 Mt) and carbon stock (1083.69 Mt) also reduced subsequently. The Conifers, of temperate region, stocked maximum carbon in their woods, 28.88 to 65.21 t C ha⁻¹, followed by Mangrove forests, 28.24 t C ha⁻¹, Dipterocarp forests, 28.00 t C ha⁻¹, and Shorea robusta forests, 24.07 t C ha⁻¹. Boswellia serrata, with 0.22 Mha forest area, stocked only 3.91 t C ha⁻¹. To have an idea of rate of carbon loss the negative changes (loss of forest area) in forest area occurred during 1984–1994 (10yrs) and 1991–1994 (4yrs) were also estimated. In India, land-use changes and fuelwood requirements are the main cause of negative change. Total 24.75 Mt C was lost during 1984–1994 and 21.35 Mt C during 1991–94 at a rate of 2.48 Mt C yr⁻¹ and 5.35 Mt C yr⁻¹ respectively. While in other parts of India negative change is due to multiple reasons like fuelwood, extraction of non-wood forest products (NWFPs), illicit felling etc., but in the northeastern region of the country shifting cultivation is the only reason for deforestation. Decrease in forest area due to shifting cultivation accounts for 23.0% of the total deforestation in India, with an annual loss of 0.93 Mt C yr⁻¹.     

Major wet interval in white mountains medieval warm period evidenced inδ 13C of bristlecone pine tree rings

A long ¹³C chronology was developed from bristlecone pine (Pinus longaeva) at the Methuselah Walk site in the White Mountains of California. The chronology represents cellulose from five-year ring groups pooled from multiple radii of multiple trees. The most dramatic isotopic event in the chronology appears from A.D. 1080–1129, when ¹³C values are depressed to levels ~ 2 below the mean for the period A.D. 925–1654. This isotopic excursion appears to represent a real event and is not an artifact of sampling circumstances; in fact, a similar excursion occurs in a previously-reported, independent ¹³C chronology from bristlecone pine. By carbon isotope fractionation models, the shift to low ¹³C values is consistent with abundant soil moisture, permitting leaf stomata to remain open, and allowing ready access of CO₂ from which carbon fixation may discriminate more effectively against¹³C in favor of¹²C. According to this model, the¹³C-depleted 50-yr isotopic excursion represents the wettest period in the White Mountains in the past 1000 yr, during which isotope-reconstructed July Palmer Drought Severity Indices averaged ~ +2.2.     

模拟氮沉降对温带阔叶红松林地氮素净矿化量的影响

采用埋置PVC管的树脂芯方法原位测定了不同氮形态及其剂量作用下长白山阔叶红松林地0～7 cm和0～15 cm土壤氮素净氨化、净硝化和净矿化量的季节和年际变化规律.近3年的观测结果表明,对照处理不同土层氮素年净矿化量中以净氨化占主导地位,约占净矿化量的53％～72％,高剂量NO3-N的输入使该比例减少至37％～66％,而NH4-N的输入却使该比例增至86％～92％.随着模拟氮沉降量增加,土壤氮素年净矿化量也随之增加,尤其外源NH+4-N输入对净矿化量的促进作用更为明显,但随着施肥年限的延长,这种促进作用逐渐减弱.与林地0～15 cm土壤相比,氮沉降量增加对0～7 cm土壤氮素净氨化和净矿化量的促进作用更为明显,尤其是NH4Cl处理的促进作用更大.通过将实验结果与前人报道的野外原位观测整合,逐步回归分析后发现土壤氮素年净矿化量随着氮素年沉降量的增加而增大,氮沉降量对不同区域森林土壤氮素年净矿化量的贡献率约为38％;大气氮沉降量、森林有机层pH及其碳/氮比值可解释不同区域森林表层土壤氮素年净矿化量一半的变化.研究结果将利于有效预测区域林地氮素净矿化量特征及其对环境变化的响应.     

A stable oxygen, but not carbon, isotope chronology of Callitris columellaris reflects recent climate change in north-western Australia

We investigated the dendroclimatic potential of stable carbon (δ¹³C) and oxygen (δ¹⁸O) abundances in tree rings of Callitris columellaris F. Muell. Tree-ring chronologies were constructed from the central Pilbara, north-western Australia and span 1919–1999. Variation in δ¹⁸O was more strongly related to climate than δ¹³C; ecological and physiological factors may have dampened the climate signal in the δ¹³C chronology. Tree-ring δ¹⁸O was most strongly correlated with relative humidity (RH) and rainfall (r = −0.36 and −0.39) of the wettest months of the summer period, January and February. The correlation with RH reflects its effect on evaporative enrichment of leaf water. However, tree-ring δ¹⁸O may also partly reflect the variability in ¹⁸O signatures of rainfall, which are influenced by the amount of rainfall and atmospheric humidity. From the δ¹⁸O chronology, we inferred that from 1919 to 1955 summers were relatively dry and warm, but since 1955, summers in the Pilbara region have become increasingly cooler and more humid. Since 1980, conditions have been the wettest and coolest of the last 80 years. These inferred changes in climate correspond to a measured increase in rainfall since 1980 in north-western Australia associated with a greater intensity of tropical cyclones. We conclude that δ¹⁸O abundances in tree rings of C. columellaris have significant potential for reconstructing the climate of semi-arid Australia, a region for which observational climate records are sparse.     

Carbon Stores,Sinks, and Sources in Forests of Northwestern Russia: Can We Reconcile Forest Inventories with Remote Sensing Results?

Forest inventories and remote sensing are the two principal data sources used to estimate carbon (C) stocks and fluxes for large forest regions. National governments have historically relied on forest inventories for assessments but developments in remote sensing technology provide additional opportunities for operational C monitoring. The estimate of total C stock in live forest biomass modeled from Landsat imagery for the St. Petersburg region was consistent with estimates derived from forest inventory data for the early 1990s (272 and 269 TgC, respectively). The estimates of mean C sink in live forest biomass also agreed well (0.36 and 0.34 Mg C ha^–1 yr^–1). Virtually all forest lands were accumulating C in live biomass, however when the net change in total ecosystem C stock was considered, 19% of the forest area were a net source of C. The average net C sink in total ecosystem biomass is quite weak (0.08 MgC ha^–1 yr^–1 and could be reversed by minor increases in harvest rates or a small decline in biomass growth rates.     

Carbon Stores, Sinks, and Sources in Forests of Northwestern Russia: Can We Reconcile Forest Inventories with Remote Sensing Results?

Forest inventories and remote sensing are the two principal data sources used to estimate carbon (C) stocks and fluxes for large forest regions. National governments have historically relied on forest inventories for assessments but developments in remote sensing technology provide additional opportunities for operational C monitoring. The estimate of total C stock in live forest biomass modeled from Landsat imagery for the St. Petersburg region was consistent with estimates derived from forest inventory data for the early 1990s (272 and 269 TgC, respectively). The estimates of mean C sink in live forest biomass also agreed well (0.36 and 0.34 Mg C ha^–1 yr^–1). Virtually all forest lands were accumulating C in live biomass, however when the net change in total ecosystem C stock was considered, 19% of the forest area were a net source of C. The average net C sink in total ecosystem biomass is quite weak (0.08 MgC ha^–1 yr^–1 and could be reversed by minor increases in harvest rates or a small decline in biomass growth rates.     

A statistical power analysis of woody carbon flux from forest inventory data

At a national scale, the carbon (C) balance of numerous forest ecosystem C pools can be monitored using a stock change approach based on national forest inventory data. Given the potential influence of disturbance events and/or climate change processes, the statistical detection of changes in forest C stocks is paramount to maintaining the net sequestration status of these stocks. To inform the monitoring of forest C balances across large areas, a power analysis of a forest inventory of live/dead standing trees and downed dead wood C stocks (and components thereof) was performed in states of the Great Lakes region, U.S. Using data from the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service, it was found that a decrease in downed wood C stocks (?1.87 Mg/ha) was nearly offset by an increase in standing C stocks (1.77 Mg/ha) across the study region over a 5-year period. Carbon stock change estimates for downed dead wood and standing pools were statistically different from zero (α?=?0.10), while the net change in total woody C (?0.10 Mg/ha) was not statistically different from zero. To obtain a statistical power to detect change of 0.80 (α?=?0.10), standing live C stocks must change by at least 0.7 %. Similarly, standing dead C stocks would need to change by 3.8 %; while downed dead C stocks require a change of 6.9 %. While the U.S.’s current forest inventory design and sample intensity may not be able to statistically detect slight changes (<1 %) in forest woody C stocks at sub-national scales, large disturbance events (>3 % stock change) would almost surely be detected. Understanding these relationships among change detection thresholds, sampling effort, and Type I (α) error rates allows analysts to evaluate the efficacy of forest inventory data for C pool change detection at various spatial scales and levels of risk for drawing erroneous conclusions.     

Bioenergy potentials from forestry in 2050

The purpose of this study was to evaluate the global energy production potential of woody biomass from forestry for the year 2050 using a bottom-up analysis of key factors. Woody biomass from forestry was defined as all of the aboveground woody biomass of trees, including all products made from woody biomass. This includes the harvesting, processing and use of woody biomass. The projection was performed by comparing the future demand with the future supply of wood, based on existing databases, scenarios, and outlook studies. Specific attention was paid to the impact of the underlying factors that determine this potential and to the gaps and uncertainties in our current knowledge. Key variables included the demand for industrial roundwood and woodfuel, the plantation establishment rates, and the various theoretical, technical, economical, and ecological limitations related to the supply of wood from forests. Forests, as defined in this study, exclude forest plantations. Key uncertainties were the supply of wood from trees outside forests, the future rates of deforestation, the consumption of woodfuel, and the theoretical, technical, economical, or ecological wood production potentials of the forests. Based on a medium demand and medium plantation scenario, the global theoretical potential of the surplus wood supply (i.e., after the demand for woodfuel and industrial roundwood is met) in 2050 was calculated to be 6.1 Gm³ (71 EJ) and the technical potential to be 5.5 Gm³ (64 EJ). In practice, economical considerations further reduced the surplus wood supply from forests to 1.3 Gm³ year⁻¹ (15 EJ year⁻¹). When ecological criteria were also included, the demand for woodfuel and industrial roundwood exceeded the supply by 0.7 Gm³ year⁻¹ (8 EJ year⁻¹). The bioenergy potential from logging and processing residues and waste was estimated to be equivalent to 2.4 Gm³ year⁻¹ (28 EJ year⁻¹) wood, based on a medium demand scenario. These results indicate that forests can, in theory, become a major source of bioenergy, and that the use of this bioenergy can, in theory, be realized without endangering the supply of industrial roundwood and woodfuel and without further deforestation. Regional shortages in the supply of industrial roundwood and woodfuel can, however, occur in some regions, e.g., South Asia and the Middle East and North Africa.     

Impacts of climate change on the risk of snow-induced forest damage in Finland

This work studied the temporal and spatial variability of the risk of snow-induced forest damage in Finland under current and changing climatic conditions until the end of this century. The study was based on a snow accumulation model in which cumulative precipitation, air temperature and wind speed were used as input variables. The risk was analyzed in terms of the number of days per year when the accumulated amount of snow exceeded 20 kg m^???2. Based on the risk, the forest area and mean carbon stock of seedling, young thinning and advanced thinning stands at risk were calculated. Furthermore, the number of 5-day periods, when the accumulated amount of snow exceeded a risk limit, was calculated for the current and changing climatic conditions in order to study the frequency of damaging snowfalls. Compared to the baseline period 1961–1990, the risk of snow-induced forest damage and the amount of damaging snowfalls were predicted to decrease from the first 30-year period (1991–2020) onwards. Over the whole country, the mean annual number of risk days decreased by 11%, 23% and 56% in the first, second and third 30-year period, respectively, compared to the baseline period. In the most hazardous areas in north-western and north-eastern Finland, the number of risk days decreased from the baseline period of over 30 days to about 8 days per year at the end of the century. Correspondingly, the shares of the forest area at risk were 1.9%, 2.0% and 1.0% in the first, second and third 30-year period, respectively. The highest mean annual carbon stocks of young stands at risk were found in central, north-eastern and north-western Finland in the first and second 30-year period, varying between 0.6 and 1.2 Mg C ha^???1 year^???1, meaning at highest 3% of the mean carbon stock (Mg C stem wood ha^???1) of those areas. This study showed that although the risk of snow-induced forest damage was mainly affected by changes in critical weather events, the development of growing stock under the changing climatic conditions also had an effect on the risk assessment. However, timely management of forest stands in the areas with a high risk of snow-induced damage contributes to the trees’ increased resistance to the damage.     

Potential impacts of climate change on a mixed broadleaved-Korean pine forest stand: A gap model approach

A gap-typed forest dynamic model KOPIDE was used to assess the dynamic responses of a mixed broadleaved-Korean pine forest stand to climate change in northeastern China. The GFDL climate change scenario was applied to derive the changes in environmental variables, such as 10 °C based DEGD and PET/P, which were used to implement the model. The simulation result suggests that the climate change would cause important changes in stand structure. Korean pine, the dominant species in the area under current climate conditions, would disappear under the GFDL equilibrium scenario. Oak and elm would become the dominant species replacing Korean pine, ash and basswood. Such a potential change in forest structure would require different strategies for forest management in northeastern China.     

Leaf-level gas exchange and scaling-up of forest understory carbon fixation rates with a “patch-scale” canopy model

Summary During the Hartheim experiment (HartX) 1992, conducted in the Upper Rhine Valley, Germany, we estimated water vapor flux from the understory by several methods as reported in Wedler et al. (this issue). We also examined the photosynthetic gas exchange of the dominant understory speciesBrachypodium pinnatum, Carex alba, andCarex flacca at the leaf level with an CO₂/H₂O porometer. A mechanisticallybased leaf gas exchange model was parameterized for these understory species and validated via the measured diurnal courses of carbon dioxide exchange. Leaf CO₂ gas exchange was scaled-up to patch- and then to stand-level utilizing the leaf gas exchange model as a component of the canopy light interception/energy balance model GAS-FLUX, and by further considering variation in vegetation patch-type distribution, patch-specific spatial structure, patch-type leaf area index, and microclimate beneath the tree canopy.At patch-level,C. alba exhibited the lowest net CO₂ uptake of ca. 75 mmol m^–2 d^–1 due to a low leaf-level photosynthetic capacity, whereas net CO₂ fixation ofB. pinnatum- andC. flacca-patches was approx. 178 and 184 mmol m^–2 d^–1, respectively. Highest CO₂ uptake was estimated for mixed patches whereB. pinnatum grew together with the sedge speciesC. alba orC. flacca. Scaling-up of leaf gas exchange to stand level resulted in an estimated average rate of total CO₂ fixation by the graminoid understory patches of approximately 93 mmol m^–2 d^–1 during the HartX period. The conservative gas exchange behavior ofC. alba at Hartheim and its apparent success in space capture seems to affect overall functioning of this pine forest ecosystem by limiting understory CO₂ uptake. The CO₂ uptake by the understory is approximately 20% of stand total CO₂ uptake. CO₂ uptake fluxes mirror the relative differences in water loss from the understory and crown layer during the HartX period. Comparative measurements indicate that understory vegetation in spruce and pine forests is not greatly different from that of other low-statured natural ecosystems such as tundra or marshes under high light conditions, although CO₂ capture by the understory at Hartheim is at the low extreme of the estimates, apparently due to the success ofC. alba. With 6 Figures     

橡胶林的热量平衡

本文通过实测资料与理论计算,分析了我国主要橡胶产区(广东)不同类型橡胶园的热量平衡各分量的特征,并与热带次生杂木林、热带稀树草原及裸地作了对比。胶林的热量收入(净辐射)可观,晴天日总量最大可达15.95MJ/m~2,与次生林相仿,而大于空旷地。热量支出项中,蒸散耗热占60—70％,湍流热通量占25—35％,土壤热通量占5—10％。其比例随林型结构与林分而异,且胶林作用层与林地作用层之间、胶林(或纯茶林、次生林)与空旷地之间差异明显。     

Challenges in the Application of Existing Process-Based Models to Predict the Effect of Climate Change on C Pools in Forest Ecosystems

Process-based models used to investigate forest ecosystem response to climate change were not necessarily developed to include the effect of carbon dioxide (CO₂) and temperature increases on physiological processes. Simulation of the impacts of climate change with such models may lead to questionable predictions. It is generally believed that significant shifts in the performance of black spruce (Picea mariana [Mill] B.S.P.) will occur under climate change. This species, which accounts for 64% of Ontario's coniferous growing stock and 80% of the annual allowable cut, represents important economic activity throughout the boreal forest region. Forest management planning requires relatively accurate productivity estimates. Thus, it is imperative to ensure that process-based models realistically predict the effect of climate change. In this study, CENTURY and FOREST-BGC models were calibrated for a productive, upland black spruce stand in northwestern Ontario. Even though both models predicted similar relative outcomes after 100 years of climate change, they disagreed on the impacts of temperature in combination with an increase in CO₂. Also, absolute amounts of carbon sequestered varied with climate change scenarios. Comparison of both models indicated that the representation of critical processes in these two forest ecosystem models is incomplete. For instance, the interactive effects of CO₂ and temperature increases on physiological processes at stand and soil levels are not well documented nor are they easily identifiable in the models. Their incorporation into models is therefore problematic. Practitioners must consequently be wary of assumptions about the inclusion of critical processes in models.     

Global Warming and Tropical Land-Use Change: Greenhouse Gas Emissions from Biomass Burning, Decomposition and Soils in Forest Conversion, Shifting Cultivation and Secondary Vegetation

Tropical forest conversion, shiftingcultivation and clearing of secondary vegetation makesignificant contributions to global emissions ofgreenhouse gases today, and have the potential forlarge additional emissions in future decades. Globally, an estimated 3.1×10⁹ t of biomasscarbon of these types is exposed to burning annually,of which 1.1×10⁹ t is emitted to the atmospherethrough combustion and 49×10⁶ t is converted tocharcoal (including 26–31×10⁶ t C of blackcarbon). The amount of biomass exposed to burningincludes aboveground remains that failed to burn ordecompose from clearing in previous years, andtherefore exceeds the 1.9×10⁹ t of abovegroundbiomass carbon cleared on average each year. Above-and belowground carbon emitted annually throughdecomposition processes totals 2.1×10⁹ t C. Atotal gross emission (including decomposition ofunburned aboveground biomass and of belowgroundbiomass) of 3.41×10⁹ t C year^-1 resultsfrom clearing primary (nonfallow) and secondary(fallow) vegetation in the tropics. Adjustment fortrace gas emissions using IPCC Second AssessmentReport 100-year integration global warming potentialsmakes this equivalent to 3.39×10⁹ t ofCO₂-equivalent carbon under a low trace gasscenario and 3.83×10⁹ t under a high trace gasscenario. Of these totals, 1.06×10⁹ t (31%)is the result of biomass burning under the low tracegas scenario and 1.50×10⁹ t (39%) under thehigh trace gas scenario. The net emissions from allclearing of natural vegetation and of secondaryforests (including both biomass and soil fluxes) is2.0×10⁹ t C, equivalent to 2.0–2.4×10⁹ t of CO₂-equivalent carbon. Adding emissions of0.4×10⁹ t C from land-use category changesother than deforestation brings the total for land-usechange (not considering uptake of intact forest,recurrent burning of savannas or fires in intactforests) to 2.4×10⁹ t C, equivalent to 2.4–2.9×10⁹ t of CO₂-equivalent carbon. The totalnet emission of carbon from the tropical land usesconsidered here (2.4×10⁹ t C year^-1)calculated for the 1981–1990 period is 50% higherthan the 1.6×10⁹ t C year^-1 value used by the Intergovernmental Panel on Climate Change. The inferred (= `missing') sink in the global carbonbudget is larger than previously thought. However,about half of the additional source suggested here maybe offset by a possible sink in uptake by Amazonianforests. Both alterations indicate that continueddeforestation would produce greater impact on globalcarbon emissions. The total net emission of carboncalculated here indicates a major global warmingimpact from tropical land uses, equivalent toapproximately 29% of the total anthropogenic emissionfrom fossil fuels and land-use change.     

Monoterpene concentrations in and above a forest of scots pine

Diurnal and vertical ambient air measurements of the monoterpenes have been made in and above a Scots pine (Pinus sylvestris) forest of central Sweden, within the boreal northern coniferous biome. Sampling was done with Tenax TA, and analysis by GC and ion trap detection. Daytime mixing ratios were on the order of tenths of a ppbv from the forest floor to the top of the forest, and a factor of 2 or 3 lower above the forest. Mixing ratios at night were at the ppbv level, highest near the forest floor and the crown, and decreased with height above the forest. The highest total concentration observed was 8 ppbv inside the forest at 3 am (GMT). The average terpene composition was 3-carene 32%, -pinene 29%, limonene 18%, -pinene 10%, -phellandrene 7%, camphene 5%, and sabinene at less than 2%. The 3-carene/-pinene ratio varied with wind direction and speed, relative humidity, and wet/dry vegetation, but not with ozone or NO₂ concentration, solar radiation, or temperature. Variations in the observed terpene composition at the sampling site are mainly caused by the influence of other vegetation in the vicinity of the site. It would seem that wet Scots pine emits more 3-carene relative to -pinene than does dry pine.