Modeling the carbon dynamics of the La Grande hydroelectric complex in northern Quebec

A mechanistic semi-empirical carbon cycle model of the La Grande reservoir complex in northern Quebec, Canada was conceived in order to investigate the climate impact of such a large alteration of the continental water cycle. The model includes inputs from the drainage basin, organic matter release from flooded soils, CO₂ emissions across the water-atmosphere interface and sedimentation. Most input data stems from previous research by our group on those ecosystems. The model includes the seven reservoirs of the La Grande complex and was run for periods of 50 and 100 years. Terrigeneous dissolved, particulate and suspended soil carbon fluxes and concentrations were computed. Over 100 years, 31.3 × 10¹² g C are released from flooded soils, equivalent to 28-29% of inputs from the drainage basin. 40-74% of dissolved organic carbon is mineralized. CO₂ fluxes over 100 years are 50.5-79.8 × 10¹² g C, 46.4-67.9 × 10¹² g C more than in the absence of reservoirs. The increase in mineralization of organic matter and in CO₂ emissions is a result of the increase in cumulated water residence time due to the creation of the reservoirs. Changes in other carbon sinks and sources likely offset a part of this additional carbon flux to the atmosphere. In the first years following flooding of the reservoir, organic carbon release from flooded soils exceeds CO₂ emissions, implying the downstream export of large quantities of eroded soil organic carbon. After this initial period, CO₂ emissions are fuelled by organic carbon originating from the drainage basin.     

Modeling carbon sequestration under zero-tillage at the regional scale. II. The influence of crop rotation and soil type

For policy decisions with respect to CO₂-mitigation measures in the agricultural sector, national and regional estimations of the efficiency of such measures are required. The conversion of ploughed cropland to zero-tillage is discussed as an option to reduce CO₂ emissions and promises at the same time effective soil and water conservation. Based on the upscaling of simulation results with the soil and land resources information system SLISYS-BW, estimations of CO₂-mitigation rates in relation to crop rotations and soil type have been made for the state of Baden-Württemberg (Germany). The results indicate considerable differences in the CO₂-mitigation rates between crop rotations ranging from 0.48 to 0.03 Mg C ha⁻¹ a⁻¹ for winter cereals–spring cereals–rape rotations and winter cereals–spring cereals–corn silage rotations, respectively. The efficiency of the crop rotations is strongly related to the total carbon input and in particular the amount of crop residues. Among the considered soil types, highest CO₂-mitigation rates are associated with Cumulic Anthrosols (0.62 Mg C ha⁻¹ a⁻¹) and the lowest with Gleysols (−0.01 Mg C ha⁻¹ a⁻¹). An agricultural extensification scenario with conventional plowing but conversion of the presently applied intensive crop rotations to a clover–clover–winter cereals rotation indicated a CO₂-mitigation potential of 466 Gg C a⁻¹. However, the present high market prices for cereals and increasing demand for energy production from biomass encourages an intensification of the agricultural production and an excessive removal of biomass which in future will seriously reduce the potential for carbon sequestration on cropland.     

Reduction of CO2 emission by optimally tracking a pre-defined target

The recent global financial crisis has highlighted the need for balanced and efficient investments in the reduction of the greenhouse effect caused by emissions of CO₂ on a global scale. In a previous paper, the authors proposed a mathematical model describing the dynamic relation of CO₂ emission with investment in reforestation and clean technology. An efficient allocation of resources to reduce the greenhouse effect has also been proposed. Here, this model is used to provide estimates of the investments needed in land reforestation and in the adoption of clean technologies for an optimum emission and abatement of CO₂, for the period of 1996–2014. The required investments are computed to minimize deviations with respect to the emission targets proposed in the Kyoto Protocol for European Countries. The emission target can be achieved by 2014 with investments in reforestation peaking in 2004, and a reduction of the expected GDP of 42%, relative to 2006. Investments in clean technology should increase between 2008 and 2010 with maximum transfer figures around 70 million American dollars. Total (cumulative) costs are, however, relatively high depending on the price of carbon abatement and the rate at which the expected CO₂ concentration in the atmosphere should be reduced. Results highlight the advantages for policy makers to be able to manage investments in climate policy more efficiently, controlling optimum transfers based on a portfolio of actions that tracks a pre-defined CO₂ concentration target.     

The sensitivity of carbon sequestration to harvesting and climate conditions in a temperate cypress forest: Observations and modeling

The role of disturbance and climate factors in determining the forest carbon balance was investigated at a Japanese cypress forest in central Japan with eddy flux measurements, tree-ring analyses, and a terrestrial biosphere model. The forest was established as a plantation after intermittent harvesting and replanting between 1959 and 1977, and acted as a strong carbon sink of approximately 500 g C m⁻² year⁻¹ for the measurement years between 2001 and 2007. A terrestrial biosphere model, BIOME-BGC, was validated using the eddy flux data at daily to interannual timescales, and the tree-ring width data at interannual to decadal timescales. According to the model simulation, during the observation period 270 ± 55 g C m⁻² year⁻¹ was additionally sequestered due to the indirect effects of the harvesting and planting, whereas the increase of CO₂ concentration and the change in climate increased the sink of 110 ± 40 and 30 ± 80 g C m⁻² year⁻¹, respectively. The model simulation shows that the forest is now recovering from harvesting, and that harvesting is a more important determinant of the current carbon sink than either interannual climate anomalies or increased atmospheric CO₂ concentration. We found that harvesting with long rotation length could be effective management activity in order to increase carbon sequestration, if the harvested timber is converted into products with long lifecycles.     

Response of tree growth to a changing climate in boreal central Canada: A comparison of empirical,process-based,and hybrid modelling approaches

The impact of 2 × CO₂ driven climate change on radial growth of boreal tree species Pinus banksiana Lamb., Populus tremuloides Michx. and Picea mariana (Mill.) BSP growing in the Duck Mountain Provincial Forest of Manitoba (DMPF), Canada, is simulated using empirical and process-based model approaches. First, empirical relationships between growth and climate are developed. Stepwise multiple-regression models are conducted between tree-ring growth increments (TRGI) and monthly drought, precipitation and temperature series. Predictive skills are tested using a calibration–verification scheme. The established relationships are then transferred to climates driven by 1× and 2 × CO₂ scenarios using outputs from the Canadian second-generation coupled global climate model. Second, empirical results are contrasted with process-based projections of net primary productivity allocated to stem development (NPP_s). At the finest scale, a leaf-level model of photosynthesis is used to simulate canopy properties per species and their interaction with the variability in radiation, temperature and vapour pressure deficit. Then, a top-down plot-level model of forest productivity is used to simulate landscape-level productivity by capturing the between-stand variability in forest cover. Results show that the predicted TRGI from the empirical models account for up to 56.3% of the variance in the observed TRGI over the period 1912–1999. Under a 2 × CO₂ scenario, the predicted impact of climate change is a radial growth decline for all three species under study. However, projections obtained from the process-based model suggest that an increasing growing season length in a changing climate could counteract and potentially overwhelm the negative influence of increased drought stress. The divergence between TRGI and NPP_s simulations likely resulted, among others, from assumptions about soil water holding capacity and from calibration of variables affecting gross primary productivity. An attempt was therefore made to bridge the gap between the two modelling approaches by using physiological variables as TRGI predictors. Results obtained in this manner are similar to those obtained using climate variables, and suggest that the positive effect of increasing growing season length would be counteracted by increasing summer temperatures. Notwithstanding uncertainties in these simulations (CO₂ fertilization effect, feedback from disturbance regimes, phenology of species, and uncertainties in future CO₂ emissions), a decrease in forest productivity with climate change should be considered as a plausible scenario in sustainable forest management planning of the DMPF.     

Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia

Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km² area of European Russia were estimated based on ¹⁴C data. The rates are corrected for bomb radiocarbon which strongly affects the topsoil ¹⁴C balance. The approach is applied for carbon stored in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year⁻¹), intermediate for tundra soils (0.25% year⁻¹), and low for grassland soils (0.02–0.08% year⁻¹) with the exception of southern Chernozems (0.32% year⁻¹). In the soil layer of 20–100 cm depth, the turnover rates were much lower for all soil types (0.01–0.06% year⁻¹) except for peat bog soils of the southern taiga (0.14% year⁻¹). Combined with a map of soil type distribution and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m² year)), intermediate for tundra soils (33 gC/(m² year)), and lowest for grassland soils (1–26 gC/(m² year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from ¹⁴C measurements that most soil organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO₂ in 21st century and beyond.     

Estimating California ecosystem carbon change using process model and land cover disturbance data: 1951-2000

Land use change, natural disturbance, and climate change directly alter ecosystem productivity and carbon stock level. The estimation of ecosystem carbon dynamics depends on the quality of land cover change data and the effectiveness of the ecosystem models that represent the vegetation growth processes and disturbance effects. We used the Integrated Biosphere Simulator (IBIS) and a set of 30- to 60-m resolution fire and land cover change data to examine the carbon changes of California's forests, shrublands, and grasslands. Simulation results indicate that during 1951-2000, the net primary productivity (NPP) increased by 7%, from 72.2 to 77.1 Tg C yr⁻¹ (1 teragram = 10¹² g), mainly due to CO₂ fertilization, since the climate hardly changed during this period. Similarly, heterotrophic respiration increased by 5%, from 69.4 to 73.1 Tg C yr⁻¹, mainly due to increased forest soil carbon and temperature. Net ecosystem production (NEP) was highly variable in the 50-year period but on average equalled 3.0 Tg C yr⁻¹ (total of 149 Tg C). As with NEP, the net biome production (NBP) was also highly variable but averaged −0.55 Tg C yr⁻¹ (total of -27.3 Tg C) because NBP in the 1980s was very low (-5.34 Tg C yr⁻¹). During the study period, a total of 126 Tg carbon were removed by logging and land use change, and 50 Tg carbon were directly removed by wildland fires. For carbon pools, the estimated total living upper canopy (tree) biomass decreased from 928 to 834 Tg C, and the understory (including shrub and grass) biomass increased from 59 to 63 Tg C. Soil carbon and dead biomass carbon increased from 1136 to 1197 Tg C.Our analyses suggest that both natural and human processes have significant influence on the carbon change in California. During 1951-2000, climate interannual variability was the key driving force for the large interannual changes of ecosystem carbon source and sink at the state level, while logging and fire were the dominant driving forces for carbon balances in several specific ecoregions. From a long-term perspective, CO₂ fertilization plays a key role in maintaining higher NPP. However, our study shows that the increase in C sequestration by CO₂ fertilization is largely offset by logging/land use change and wildland fires.     

Modeling carbon sequestration under zero tillage at the regional scale. I. The effect of soil erosion

Zero tillage is recognized as a potential measure to sequester carbon dioxide in soils and to reduce CO₂ emissions from arable lands. An up-scaling approach of the output of the Environmental Policy Integrated Climate (EPIC) model with the information system SLISYS-BW has been used to estimate the CO₂-mitigation potential in the state of Baden-Württemberg (SW-Germany). The state territory of 35,742 km² is subdivided into eight agro-ecological zones (AEZ), which have been further subdivided into a total of 3976 spatial response units. Annual CO₂-mitigation rates where estimated from the changes in soil organic carbon content comparing 30 years simulations under conventional and zero tillage. Special attention was given to the influence of tillage practices on the losses of organic carbon through soil erosion, and consequently on the calculation of CO₂-mitigation rates. Under conventional tillage, mean carbon losses through erosion in the AEZ were estimated to be up to 0.45 Mg C ha⁻¹ a⁻¹. The apparent CO₂-mitigation rate for the conversion from conventional to zero tillage ranges from 0.08 to 1.82 Mg C ha⁻¹ a⁻¹ in the eight AEZ, if the carbon losses through soil erosion are included in the calculations. However, the higher carbon losses under conventional tillage compared to zero tillage are composed of both, losses through enhanced CO₂ emissions, and losses through intensified soil erosion. The adjusted net CO₂-mitigation rates of zero tillage, subtracting the reduced carbon losses through soil erosion, are between 0.07 and 1.27 Mg C ha⁻¹ a⁻¹ and the estimated net mitigation rate for the entire state amounts to 285 Gg C a⁻¹. This equals to 1045 Gg CO₂-equivalents per year with the cropping patterns in the reference year 2000. The results call attention to the necessity to revise those estimation methods for CO₂-mitigation which are exclusively or predominantly based on the measurements of differential changes in total soil organic carbon without taking into account the tillage effects on carbon losses through soil erosion.     

Optimal resource management control for CO2 emission and reduction of the greenhouse effect

In recent years, the world has witnessed an ever-growing concern towards global warming caused by greenhouse gases, such as carbon dioxide (CO₂). In order to reduce the emissions of CO₂ without limiting economic growth, substantial investments should target the development of clean technology and the expansion of forested areas. Considering the limited availability of resources, investments must be used in the most effective way. The present work proposes a method to efficiently manage these resources by applying the optimal control theory to a new mathematical model that describes the dynamics of the atmospheric CO₂. The contributions of this work are twofold: (1) present a model that describes the dynamic relation of CO₂ emission with investment in reforestation and clean technology and (2) present a method to efficiently manage the available resources by casting an optimal control problem. The mathematical model uses ordinary differential equations to relate the production of CO₂ with forest area and Gross Domestic Product (GDP). The model parameters are adjusted to fit the actual published data. Given an appropriate performance index, the optimal solution is found by numerically solving the Two-Point Boundary Value Problem (TPBVP) that arises from the application of Pontriagyn's Maximum Principle. The sensitivity of the obtained numerical solution is evaluated with respect to the uncertainties in the model parameters. The main objective of this work is to provide a quantitative tool for the efficient allocation of resources to reduce the greenhouse effect caused CO₂ emissions.     

Economic and environmental performance of electricity production in Finland: A multicriteria assessment framework

Meeting environmental, economic, and societal targets in energy policy is complex and requires a multicriteria assessment framework capable of exploring trade-offs among alternative energy options. In this study, we integrated economic analysis and biophysical accounting methods to investigate the performance of electricity production in Finland at plant and national level. Economic and environmental costs of electricity generation technologies were assessed by evaluating economic features (direct monetary production cost), direct and indirect use of fossil fuels (GER cost), environmental impact (CO₂ emissions), and global environmental support (emergy cost). Three scenarios for Finland's energy future in 2025 and 2050 were also drawn and compared with the reference year 2008. Accounting for an emission permit of 25 €/t CO₂, the production costs calculated for CHP, gas, coal, and peat power plants resulted in 42, 67, 68, and 74 €/MWh, respectively. For wind and nuclear power a production cost of 63 and 35 €/MWh were calculated. The sensitivity analysis confirmed wind power's competitiveness when the price of emission permits overcomes 20 €/t CO₂. Hydro, wind, and nuclear power were characterized by a minor dependence on fossil fuels, showing a GER cost of 0.04, 0.13, and 0.26 J/J_e, and a value of direct and indirect CO₂ emissions of 0.01, 0.04, and 0.07 t CO₂/MWh. Instead, peat, coal, gas, and CHP plants showed a GER cost of 4.18, 4.00, 2.78, and 2.33 J/J_e. At national level, a major economic and environmental load was given by CHP and nuclear power while hydro power showed a minor load in spite of its large production. The scenario analysis raised technological and environmental concerns due to the massive increase of nuclear power and wood biomass exploitation. In conclusion, we addressed the need to further develop an energy policy for Finland's energy future based on a diversified energy mix oriented to the sustainable exploitation of local, renewable, and environmentally friendly energy sources.     

Carbon sequestration in Taiwan harvested wood products

This study, with FAOSTAT and Taiwan data sources, estimates Taiwan carbon dioxide (CO₂) emissions in harvested wood products (HWP) by applying the three accounting methods suggested by the 2006 IPCC Guidelines. The investigation also explores impulse responses of CO₂ emissions to economic factors. Results from FAOSTAT and Taiwan data demonstrate an inconsistent production approach (PA) in the signs of the estimated CO₂ emissions. Average contributions of HWP from 1990 to 2008 for the stock change approach (SCA), PA and atmospheric flow approach (AFA) in Taiwan are ?3.195 Tg, 0.412 Tg and 10.632 Tg CO₂ emissions, respectively. SCA has determined the Taiwan HWP as a carbon reservoir; in contrast, PA and AFA have determined Taiwan HWP as a CO₂ emission. The net forest products imports into Taiwan induce the inconsistent signs of HWP carbon sequestration among SCA, PA and AFA. The vector autoregressive model (VAR) results also indicate that real GDP per capita is crucial for SCA CO₂ emissions, followed by exchange rate.     

Application of a three-dimensional model for assessing effects of small clear-cuttings on radiation and soil temperature

A three-dimensional model Mixfor-3D of soil–vegetation–atmosphere transfer (SVAT) was developed and applied to estimate possible effects of tree clear-cutting on radiation and soil temperature regimes of a forest ecosystem. The Mixfor-3D model consists of several closely coupled 3D sub-models describing: forest stand structure; radiative transfer in a forest canopy; turbulent transfer of sensible heat, H₂O and CO₂ between ground surface and the atmospheric surface layer; evapotranspiration of ground surface vegetation and soil; heat and moisture transfer in soil. The model operates with the horizontal grid resolution, 2 m × 2 m; vertical resolution, 1 m and primary time step, 1 h.     

Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July-September (T_amax) in all process models (R² = 0.4-0.6), indicating that these correlations were robust. The negative correlation between T_amax and NEP was attributed to different processes in different models, which were tested by comparing CO₂ fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (T_a). The general agreement in sensitivity of annual NPP to T_amax among the process models led to the development of a generalized algorithm for weather effects on NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3: NPP′ = NPP − 57.1 (T_amax − 18.6), where NPP and NPP′ are the current and temperature-adjusted annual NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July-September, and T_amax is the mean value for the current year. Our analysis indicated that the sensitivity of NPP to T_amax was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on NPP and NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.     

Inter-annual variability of ecosystem production in boreal jack pine forests (1975-2004) estimated from tree-ring data using CBM-CFS3

We describe and apply a method of using tree-ring data and an ecosystem model to reconstruct past annual rates of ecosystem production. Annual data on merchantable wood volume increment and mortality obtained by dendrochronological stand reconstruction were used as input to the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to estimate net ecosystem production (NEP), net primary production (NPP), and heterotrophic respiration (Rh) annually from 1975 to 2004 at 10 boreal jack pine (Pinus banksiana Lamb.) stands in Saskatchewan and Manitoba, Canada. From 1975 (when sites aged 41-60 years) to 2004 (when they aged 70-89 years), all sites were moderate C sinks except during some warmer than average years where estimated Rh increased. Across all sites and years, estimated annual NEP averaged 57 g Cm⁻² yr⁻¹ (range −31 to 176 g Cm⁻² yr⁻¹), NPP 244 g Cm⁻² yr⁻¹ (147-376 g Cm⁻² yr⁻¹), and Rh 187 g Cm⁻² yr⁻¹ (124-270 g Cm⁻² yr⁻¹). Across all sites, NPP was related to stand age and density, which are proxies for successional changes in leaf area. Regionally, warm spring temperature increased NPP and defoliation by jack pine budworm 1 year previously reduced NPP. Our estimates of NPP, Rh, and NEP were plausible when compared to regional eddy covariance and carbon stock measurements. Inter-annual variability in ecosystem productivity contributes uncertainty to inventory-based assessments of regional forest C budgets that use yield curves predicting averaged growth over time. Our method could expand the spatial and temporal coverage of annual forest productivity estimates, providing additional data for the development of empirical models accounting for factors not presently considered by these models.     

Effects of boreal forest management practices on the climate impact of CO2 emissions from bioenergy

In Life Cycle Assessment (LCA), carbon dioxide (CO₂) emissions from biomass combustion are traditionally assumed climate neutral if the bioenergy system is CO₂ flux neutral, i.e. the quantity of CO₂ released approximately equals the amount of CO₂ sequestered in biomass. This convention is a plausible assumption for fast growing biomass species, but is inappropriate for slower growing biomass, like forests. In this case, the climate impact from biomass combustion can be potentially underestimated if CO₂ emissions are ignored, or overestimated, if biogenic CO₂ is considered equal to anthropogenic CO₂. The estimation of the effective climate impact should take into account how the CO₂ fluxes are distributed over time: the emission of CO₂ from bioenergy approximately occurs at a single point in time, while the absorption by the new trees is spread over several decades. Our research target is to include this dynamic time dimension in unit-based impact analysis, using a boreal forest stand as case study. The boreal forest growth is modelled with an appropriate function, and is investigated under different forestry regimes (affecting the growth rate and the year of harvest). Specific atmospheric decay functions for biomass-derived CO₂ are then elaborated for selected combinations of forest management options. The contribution to global warming is finally quantified using the GWP_bio index as climate metric. Results estimates the effects of these practices on the characterization factor used for the global warming potential of CO₂ from bioenergy, and point out the key role played by the selected time horizon.     

Bewertung der Umwelteffizienz moderner Autoantriebe – auf dem Weg vom Diesel-Pkw-Boom zu Elektroautos

Motorized traffic is among the biggest CO₂-emitting sources and is additionally dominating NO_x emission. Engine technology shifts are approaching, while automobiles developed in Germany and Europe are exported worldwide together with the European emission thresholds for cars. The Diesel car boom induced by EU commission, national EU governments and car industry is accordingly analyzed for sustainability and its effects on environment. German CO₂ emission reduction numbers by motorized traffic, as claimed by the government, are questioned. Radiative forcing by soot (black carbon) Diesel car emissions is added on the CO₂ emissions by fuel combustion. Diesel cars without particle filters are found to cause an atmospheric warming. Modelled and measured NO_x emission data are assessed to mismatch considerably. In spite of an ambitious national NO_x reduction plan there is excess NO_x emission by the German and European Diesel car boom. In this context environmental sustainability of battery electric vehicles (BEV) is investigated. Direct (by car) und indirect (by power plant) emissions (CO₂, NO_x, PM₁₀, SO₂) of cars with internal combustion engines (ICE) and BEVs, respectively, are calculated and compared. CO₂-ecoanalysis revealed advantages for BEVs even operated with current German electricity mix based on around 15?% renewable sources.     

The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain,with emphasis on the role of wetlands

Historically, management strategies in Canada's boreal forest have focused on forest polygons and terrestrial biodiversity to address ecological considerations in forest management. The Forest Watershed and Riparian Disturbance (FORWARD) project examines the problem from a watershed perspective rather than a forest polygon viewpoint. The main objective of this study was to devise an artificial neural network (ANN) modeling tool that can predict flow and total phosphorus (TP) concentration for ungauged watersheds (where daily flow is not monitored). This dictates that all inputs should be easily accessed via a public domain database, like the Environment Canada weather database, without the need to install flow gauges in each modeled watershed. Daily flow and TP concentration for two of the project watersheds were modeled using ANNs. The two watersheds (1A Creek, 5.1 km² and Willow Creek, 15.6 km²) were chosen to reflect variations in wetland area and composition in the study area. Flow was modeled with a feed-forward multilayer perceptron ANN trained with the error back-propagation algorithm. Simulated values for flow were then used, as inputs, to model TP concentration using the same neural networks algorithm. One hidden layer with three slabs; each operating with a different activation function was utilized to simulate the conceptual differences between base flow, snowmelt, and storm events. Time domain analysis was conducted to identify possible model time-lagged inputs reflecting the time dependency of the modeled variables. Spectral analysis was used to address data hystereses. Our results highlight the capabilities of ANN in modeling complex ecosystems and highly correlated variables. Results also indicated that more research towards the phosphorus dynamics in wetlands is required to better represent the impact of wetland area and composition on the water-phase phosphorus in ANN modeling.     

Trends of SO2emission in the European union, 1985–1997

The trends of yearly emission of sulphur dioxide are analysed for the European Union during a period of time from 1985 to 1997. To achieve the above matter the method of the least squares model has been used. Major SO₂emissions were found in Germany, the United Kingdom, Spain, Italy and France. However, high SO₂emissions by km²were found in Germany, the United Kingdom and Belgium. The most remarkable results of the trend analysis appears as follows: 12 countries with significant downward trends, 2 countries with significant upward trends and 1 country with no significant trend. A decreasing trend is evident for the most part of the E.U., although Portugal and Greece generated significant increasing trends of SO₂emission for the mentioned year period.     

Decomposition analysis of energy-related carbon dioxide emissions in the iron and steel industry in China

This work aims to identify the main factors influencing the energy-related carbon dioxide (CO₂) emissions from the iron and steel industry in China during the period of 1995–2007. The logarithmic mean divisia index (LMDI) technique was applied with period-wise analysis and time-series analysis. Changes in energyrelated CO₂ emissions were decomposed into four factors: emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO₂ emissions during the sampling period; on the other hand the energy consumption is the largest contributor to the decrease in CO₂ emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to CO₂ emissions, respectively. Policy implications are provided regarding the reduction of CO₂ emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.     

Modelling forest management within a global vegetation model—Part 1: Model structure and general behaviour

This article describes a new forest management module (FMM) that explicitly simulates forest stand growth and management within a process-based global vegetation model (GVM) called ORCHIDEE. The net primary productivity simulated by ORCHIDEE is used as an input to the FMM. The FMM then calculates stand and management characteristics such as stand density, tree size distribution, tree growth, the timing and intensity of thinnings and clear-cuts, wood extraction and litter generated after thinning. Some of these variables are then fed back to ORCHIDEE. These computations are made possible with a distribution-based modelling of individual tree size. The model derives natural mortality from the relative density index (rdi), a competition index based on tree size and stand density. Based on the common forestry management principle of avoiding natural mortality, a set of rules is defined to calculate the recurrent intensity and frequency of forestry operations during the stand lifetime. The new-coupled model is called ORCHIDEE-FM (forest management).The general behaviour of ORCHIDEE-FM is analysed for a broadleaf forest in north-eastern France. Flux simulation throughout a forest rotation compare well with the literature values, both in absolute values and dynamics.Results from ORCHIDEE-FM highlight the impact of forest management on ecosystem C-cycling, both in terms of carbon fluxes and stocks. In particular, the average net ecosystem productivity (NEP) of 225 gC m⁻² year⁻¹ is close to the biome average of 311 gC m⁻² year⁻¹. The NEP of the “unmanaged” case is 40% lower, leading us to conclude that management explains 40% of the cumulated carbon sink over 150 years. A sensitivity analysis reveals 4 major avenues for improvement: a better determination of initial conditions, an improved allocation scheme to explain age-related decline in productivity, and an increased specificity of both the self-thinning curve and the biomass-diameter allometry.