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.     

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.     

Simulation of NO and N2O emissions from a spruce forest during a freeze/thaw event using an N-flux submodel from the PnET-N-DNDC model integrated to CoupModel

The amount of nitrogen gases (N₂O, NO and N₂) emitted from forest soils depends on interactions between soil properties, climatic factors and soil management. To increase the understanding of nitrogen processes in soil ecosystems, two dynamic models, CoupModel (coupled heat and mass transfer model for soil–plant–atmosphere systems) and the denitrification–decomposition (DNDC) model were selected. Both are dynamic models with different submodels for soil, vegetation, hydrology and climate system. CoupModel has a higher degree of detail on soil physical and abiotic components, whereas the DNDC model contains details of microbiological processes involved in production of nitrogen gases. To improve the previous simple submodel of nitrogen emission in CoupModel, we included a submodel corresponding to the forest version of DNDC containing photosynthesis/evapotranspiration-nitrogen (PnET-N-DNDC model).     

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.     

Modelling and mapping topographic variations in forest soils at high resolution: A case study

This article examines the utility of a digitally derived cartographic depth-to-water (DTW) index to model and map variations in drainage, vegetation and soil type and select soil properties within a forested area (40 ha) of the Swan Hills, Alberta, Canada. This index was derived from a LiDAR (Light Detection and Ranging) derived digital elevation model (DEM), with at least 1 ground return per m². The resulting DTW pattern was set to be zero along all DEM-derived flow channels, each with a 4 ha flow-initiation threshold. Soil type (luvisol, gleysol, mesisol), drainage type (very poor to excessive), vegetation type (hydric to xeric) and forest floor depth were determined along hillslope transects. These determinations conformed more closely to the DEM-derived log₁₀(DTW) variations (R² > 60%) than to the corresponding variations of the widely used topographic wetness index (TWI) (R² < 25%). Setting log₁₀(DTW) thresholds to represent the wet to moist to dry transitions between vegetation, drainage and soil type enabled a high-resolution mapping of these types across the study area. Also determined were soil moisture content, coarse fragment and soil particle composition (sand, silt, clay), pH, total C, N, S, P, Ca, Mg, K, Fe, Al, Mn, Zn, and available Ca, Mg, K, P and NH₄, by soil layer type and depth. Most of these variables were also more correlated with log₁₀(DTW) than with TWI, with and without soil layer depth as an additional regression variable. These variables are, therefore, subject to topographic controls to at least some extent, and can be modelled and mapped accordingly, as illustrated for soil moisture, forest floor depth and pH across the study area, from ridge tops to depressions. Further examinations revealed that the DEM-produced DTW and TWI patterns complemented one another, with DTW delineating soils in relation to local water-table influences, and with TWI delineating where the water would flow and accumulate.     

Surface modelling of global terrestrial ecosystems under three climate change scenarios

A high accuracy and speed method (HASM) of surface modelling is developed to find a solution for error problem and to improve computation speed. A digital elevation model (DEM) is established on spatial resolution of 13.5 km × 13.5 km. Regression formulations among temperature, elevation and latitude are simulated in terms of data from 2766 weather observation stations scattered over the world by using the 13.5 km × 13.5 km DEM as auxiliary data. Three climate scenarios of HadCM3 are refined from spatial resolution of 405 km × 270 km to 13.5 km × 13.5 km in terms of the regression formulations. HASM is employed to simulate surfaces of mean annual bio-temperature, mean annual precipitation and potential evapotranspiration ratio during the periods from 1961 to 1990 (T₁), from 2010 to 2039 (T₂), from 2040 to 2069 (T₃), and from 2070 to 2099 (T₄) on spatial resolution of 13.5 km × 13.5 km. Three scenarios of terrestrial ecosystems on global level are finally developed on the basis of the simulated climate surfaces. The scenarios show that all polar/nival, subpolar/alpine and cold ecosystem types would continuously shrink and all tropical types, except tropical rain forest in scenario A1Fi, would expand because of the climate warming. Especially at least 80% of moist tundra and 22% of nival area might disappear in period T₄ comparing with the ones in the period T₁. Tropical thorn woodland might increase by more than 97%. Subpolar/alpine moist tundra would be the most sensitive ecosystem type because its area would have the rapidest decreasing rate and its mean center would shift the longest distance towards west. Subpolar/alpine moist tundra might be able to serve as an indicator of climatic change. In general, climate change would lead to a continuous reduction of ecological diversity.     

GIS-based modeling of the geographic distribution of Quercus emoryi Torr. (Fagaceae) in México and identification of significant environmental factors influencing the species’ distribution

The greatest concentration of oak species in the world is believed to be found in Mexico. These species are potentially useful for reforestation because of their capacity to adapt to diverse environments. Knowledge of their geographic distribution and of species–environment relations is essential for decision-making in the management and conservation of natural resources. The objectives of this study were to develop a model of the distribution of Quercus emoryi Torr. in Mexico, using geographic information systems and data layers of climatic and other variables, and to determine the variables that significantly influence the distribution of the species. The study consisted of the following steps: (A) selection of the target species from a botanical scientific collection, (B) characterization of the collecting sites using images with values or categories of the variables, (C) model building with the overlay of images that meet the habitat conditions determined from the characterization of sites, (D) model validation with independent data in order to determine the precision of the model, (E) model calibration through adjustment of the intervals of some variables, and (F) sensitivity analysis using precision and concordance non-parametric statistics applied to pairs of images. Results show that the intervals of the variables that best describe the species’ habitat are the following: altitude from 1650 to 2750 amsl, slope from 0 to 66°; average minimum temperature of January from −12 to −3 °C; mean temperature of June from 11 to 25 °C; mean annual precipitation from 218 to 1225 mm; soil units: lithosol, eutric cambisol, haplic phaeozem, chromic luvisol, rendzina, luvic xerosol, mollic planosol, pellic vertisol, eutric regosol; type of vegetation: oak forest, oak–pine forest, pine forest, pine–oak forest, juniperus forest, low open forest, natural grassland and chaparral. The resulting model of the geographic distribution of Quercus emoryi in Mexico had the following values for non-parametric statistics of precision and agreement: Kappa index of 0.613 and 0.788, overall accuracy of 0.806 and 0.894, sensitivity of 0.650 and 0.825, specificity of 0.963, positive predictive value of 0.945 and 0.957 and negative predictive value of 0.733 and 0.846. Results indicate that the variable average minimum temperature of January, with a maximum value of −3 °C, is an important factor in limiting the species’ distribution.     

Influence of grain size on species-habitat models

High resolution remote sensing data facilitate the use of small-scale habitat features such as trees or hedges in the analysis of species-habitat relationships. Such data potentially enable more accurate species-habitat mapping than lower resolution data. Here, for the first time, we systematically investigated this hypothesis by altering the spatial resolution from 1 m up to 1000 m grain size in species-habitat models of 13 bird species. The study area covered the Nidda river catchment in central Germany, a large heterogeneous landscape of 1620 km². A high resolution habitat map of the area was converted to coarser spatial and thematic resolutions in seven steps. We investigated how model performance responded to grain size, and we compared the differential effects of spatial resolution and thematic resolution on model performance. Explained deviance (D²) of the bird models generally decreased with coarser spatial resolution of the data, although it did not decrease monotonically in all species. On average across all species, model D² decreased from 41.5 at 1 m grain size to 15.9 at 1000 m grain size. Ten species were best modelled at 1 m, two species at 3 m and one species at 32 m grain size. Model performance degraded continuously with increasing grain size, both in habitat generalist and habitat specialist bird species, and was systematically lower in habitat generalists. The higher model performance observed at finer grain sizes was most likely caused by the combination of three factors: (1) high spatial accuracy of bird records and (2) a more precise location and delineation of habitat features and, (3) to a lesser degree, by more habitat types differentiated in maps of finer resolution. We conclude that higher spatial and thematic resolution data can be essential for deriving accurate predictions on bird distribution patterns from species-habitat models. Especially for bird species that are sensitive to specific land-use types or to small-scaled habitat features, a grain size of 1-3 m seems most promising.     

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.     

The derivation of species response curves with Gaussian logistic regression is sensitive to sampling intensity and curve characteristics

We investigated quantitatively the sensitivity of plant species response curves to sampling characteristics (number of plots, occurrence and frequency of species), along a simulated pH gradient. We defined 54 theoretical unimodal response curves, issued from combinations of six values for optimum (opt = 3, 4, …, 8), three values for tolerance (tol = 0.5, 1.0, and 1.5, sensu ter Braak and Looman [ter Braak, C.J.F., Looman, C.W.N., 1986. Weighted averaging, logistic regression and the Gaussian response model. Vegetatio 65, 3–11]), and three values for maximum probability of presence (p_max = 0.05, 0.20, and 0.50). For each of these 54 theoretical response curves, we built artificial binary data sets (presence/absence) to test the influence of species occurrence, frequency, or number of available plots. With real data extracted from EcoPlant, a phytoecological database for French forests [Gégout, J.-C., Coudun, Ch., Bailly, G., Jabiol, B., 2005. EcoPlant: a forest sites database linking floristic data with soil characteristics and climatic conditions. J. Veg. Sci. 16, 257–260], we compared the ecological response of 50 plant species to soil pH, based first on a small data set (100 randomly sampled plots), and then based on the whole data set available (3810 plots).     

Spatio-temporal modelling of ground vegetation development in mountain spruce forests

More complex models of forest ecosystems are required to understand how land-cover changes can impact vegetation dynamics and spatial pattern. In order to document spatio-temporal modelling abilities, the observations conducted in the declined climax mountain Norway spruce forest during the recovery period (1995-2006) are used for simulation and spatial analysis in the GIS environment. The developed spatio-temporal model is used for simulation of forest vegetation dynamics in a mountain spruce forest in the framework of regeneration processes after stress from air pollution. In order to explore the spatial and temporal phenomena of regeneration processes, the spatio-temporal model is based on a large set of ordinary differential equations that solve dynamic processes in sets of microsites arranged in grids for each ground vegetation species and each age group of Norway spruce seedlings. The spatial extent of the explored site is composed of a set of 50 × 50 microsites. Each microsite is represented by a square with dimensions of 1 m × 1 m. The presented simulation studies are mainly focused on seedlings from the seed year 1992, in order to explore the longest monitored time series of survival. It is based on exponential growth models that are related to the environmental conditions for each microsite. The canopy gaps based on estimates of the local crown projected area, the soil type layer, and the dominant grass density are used to provide case simulation studies. The first case study simulates the influence of microsite positions in relation to the local tree crown projections on the survival of spruce seedlings. It is assumed that the density of the trees is the main factor that determines the light and heat supply to the ground level of the Norway spruce seedlings. The second case study extends the previous study to include terms that determine the growth ratio in dependence on the crown projection area. The third case study provides further extensions in order to simulate growth ratio relations to the local soil type. The fourth case study demonstrates the local influence of the dominant grasses, such as Avenella flexuosa and Calamagrostis villosa, on the natural regeneration of Norway spruce. Starting from the conditions at the sites before the recovery period, the case simulation studies are able to project the short-term succession for a regeneration decade and the approximate long-term development. In addition to the standard simulation procedures based on solution of ordinary differential equations, spatio-temporal modelling in the GIS environment is able to provide spatial data management, analysis and visualization of the data.     

Optimization of ecosystem model parameters using spatio-temporal soil moisture information

Parameters in process-based terrestrial ecosystem models are often nonlinearly related to the water flux to the atmosphere, and they also change temporally and spatially. Therefore, for estimating soil moisture, process-based terrestrial ecosystem models inevitably need to specify spatially and temporally variant model parameters. This study presents a two-stage data assimilation scheme (TSDA) to spatially and temporally optimize some key parameters of an ecosystem model which are closely related to soil moisture. At the first stage, a simplified ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS), is used to obtain the prior estimation of daily soil moisture. After the spatial distribution of 0–10 cm surface soil moisture is derived from remote sensing, an Ensemble Kalman Filter is used to minimize the difference between the remote sensing model results, through optimizing some model parameters spatially. At the second stage, BEPS is reinitialized using the optimized parameters to provide the updated model predictions of daily soil moisture. TSDA has been applied to an arid and semi-arid area of northwest China, and the performance of the model for estimating daily 0–10 cm soil moisture after parameter optimization was validated using field measurements. Results indicate that the TSDA developed in this study is robust and efficient in both temporal and spatial model parameter optimization. After performing the optimization, the correlation (r²) between model-predicted 0–10 cm soil moisture and field measurement increased from 0.66 to 0.75. It is demonstrated that spatial and temporal optimization of ecosystem model parameters can not only improve the model prediction of daily soil moisture but also help to understand the spatial and temporal variation of some key parameters in an ecosystem model and the corresponding ecological mechanisms controlling the variation.     

Modeling net primary production of a fast-growing forest using a light use efficiency model

As interest grows in the quantification of global carbon cycles, Light Use Efficiency (LUE) model predictions of the forest net primary production (NPP) are being developed at an accelerating rate. Such models can provide useful predictions at large scales, but evaluating their performance has been difficult. In this study, a remote sensing-based LUE model was established to estimate forest NPP. Using the forest inventory data (FID) from the regional forest inventory survey in China and established allometric biomass equations, we calculated the biomass, the biomass increment, and the NPP of Eucalyptus urophylla (E. urophylla) plantation plots in the forestry jurisdiction of the Leizhou Forestry Bureau, Southern China. The FID-based NPP and the NPP from LUE model predictions were then compared to each other. Results show that the NPP from model predictions at a spatial resolution of 30 m × 30 m varied from 0 to 265 gC/(m² month) and showed regional differences. In addition, the stand age had variable effects on the average individual biomass of the E. urophylla plantation plots. The average individual biomass of the young and mid-age forests increased exponentially and logarithmically with the stand age (R² = 0.9178 and R² = 0.8683), respectively. For young and mid-age E. urophylla plantation plots, the LUE model-predicted NPP was fairly consistent with the FID-based NPP, but the model predictions of the NPP were higher than the estimates from FID. Through the analysis of the causes of uncertainty and the possible reasons for the discrepancy between the model-based NPP and FID-based NPP, the FID-derived estimates provided a foundation for model evaluation.     

Dissolved organic carbon concentrations and fluxes in forest catchments and streams: DOC-3 model

Dissolved organic carbon (DOC) concentrations in south-western Nova Scotia streams, sampled at weekly to biweekly intervals, varied across streams from about 3 to 40 mg L⁻¹, being highest mid-summer to fall, and lowest during winter to spring. A 3-parameter model (DOC-3) was proposed to project daily stream DOC concentrations and fluxes from modelled estimates for daily soil temperature and moisture, year-round, and in relation to basin size and wetness. The parameters of this model refer to (i) a basin-specific DOC release parameter “k_DOC^”, related to the wet- and open-water area percentages per basin, (ii) the lag time “τ” between DOC production and subsequent stream DOC emergence, related to the catchment area above the stream sampling location; and (iii) the activation energy “Ea”, to deal with the temperature effect on DOC production. This model was calibrated with the 1988-2006 DOC concentration data from three streams (Pine Marten, Moosepit Brook, and the Mersey River sampled at or near Kejimkujik National Park, or KNP), and was used to interpret the biweekly 1999-2003 DOC concentrations data (stream, ground and lake water, soil lysimeters) of the Pockwock-Bowater Watershed Project near Halifax, Nova Scotia. The data and the model revealed that the DOC concentrations within the streams were not correlated to the DOC concentrations within the soil- and groundwater, but were predictable based on (i) the hydrologically inferred weather-induced changes in soil moisture and temperature next to each stream, and (ii) the topographically inferred basin area and wet- and open-water area percentages associated with each stream (R² = 0.53; RMSE = 3.5 mg L⁻¹). Model-predicted fluxes accounted 74% of the hydrometrically determined DOC exports at KNP.     

Assessing spawning ground hydraulic suitability for Chinese sturgeon (Acipenser sinensis) from horizontal mean vorticity in Yangtze River

The Chinese sturgeon, Acipenser sinensis, is an anadromous protected species. Its migration pattern has been blocked since the construction of Gezhouba dam and the length of the natural spawning site reduced to less than 7 km. However, the fish eventually established an alternative spawning ground in the narrow downstream reach of Gezhouba dam. In this article, we applied Delft3D-Flow model to simulate the hydraulic suitability of the spawning ground downstream Gezhouba dam. Horizontal mean vorticity was used to assess the hydraulic environment of spawning ground. The flow field state was determined through model simulation and field-measured data used to validate the model. The computational method was improved by calculating absolute horizontal mean vorticity from estimates the literature. The final vorticity was determined from the simulation output and its distribution pattern retrieved. The horizontal mean vorticity range was 0.71–4.61 10⁻³ s⁻¹ for the entire spawning grounds, with egg mass field upper limit of 1.0 × 10⁻³ s⁻¹. Vorticity strength selection of Chinese sturgeon spawning can enhance our understanding of egg fertilization rate, hence the protection of fertilized eggs. Furthermore, the results of the study would add to existing scientific database for spawning ground hydraulic environment protection, especially in the ecological regulation drive of the Three Gorges Reservoir.     

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.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

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.     

Mechanisms of moisture stress in a mid-latitude temperate forest: Implications for feedforward and feedback controls from an irrigation experiment

While it is well established that stomata close during moisture stress, strong correlations among environmental (e.g., vapor pressure deficit, soil moisture, air temperature, radiation) and internal (e.g., leaf water potential, sap flow, root-shoot signaling) variables obscure the identification of causal mechanisms from field experiments. Models of stomatal control fitted to field data therefore suffer from ambiguous parameter identification, with multiple acceptable (i.e., nearly optimal) model structures emphasizing different moisture status indicators and different processes. In an effort to minimize these correlations and improve parameter and process identification, we conducted an irrigation experiment on red maples (Acer rubrum L.) at Harvard Forest (summers of 2005 and 2006). Control and irrigated trees experienced similar radiative and boundary layer forcings, but different soil moisture status, and thus presumably different diurnal cycles of internal leaf water potential. Measured soil moisture and atmospheric forcing were used to drive a transient tree hydraulic model that incorporated a Jarvis-type leaf conductance in a Penman–Monteith framework with a Cowan-type (resistance and capacitance) tree hydraulic representation. The leaf conductance model included dependence on both leaf matric potential, Ψ_L (so-called feedback control) and on vapor pressure deficit, D (so-called feedforward control). Model parameters were estimated by minimizing the error between predicted and measured sap flow. The whole-tree irrigation treatment had the effect of elevating measured transpiration during summer dry-downs, demonstrating the limiting effect that subsurface resistance may have on transpiration during these times of moisture stress. From the best fitted model, we infer that during dry downs, moisture stress manifests itself in an increase of soil resistance with a resulting decrease in Ψ_L, leading to both feedforward and feedback controls in the control trees, but only feedforward control for the irrigated set. Increases in the sum-of-squares error when individual model components were disabled allow us to reject the following three null hypotheses: (1) the f(D) stress is statistically insignificant (p = 0.01); (2) the f(Ψ_L) stress is statistically insignificant (p = 0.07); and (3) plant storage capacitance is independent of moisture status (p = 0.07).     

An optimized policy for the reduction of CO2 emission in the Brazilian Legal Amazon

The Brazilian government has already acknowledged the importance of investing in the development and application of technologies to reduce or prevent CO₂ emissions resulting from human activities in the Legal Brazilian Amazon (BA). The BA corresponds to a total area of 5 × 10⁶ km² from which 4 × 10⁶ km² was originally covered by the rain forest. One way to interfere with the net balance of greenhouse gases (GHG) emissions is to increase the forest area to sequester CO₂ from the atmosphere. The single most important cause of depletion of the rain forest is cattle ranching. In this work, we present an effective policy to reduce the net balance of CO₂ emissions using optimal control theory to obtain a compromising partition of investments in reforestation and promotion of clear technology to achieve a CO₂ emission target for 2020. The simulation indicates that a CO₂ emission target for 2020 of 376 million tonnes requires an estimated forest area by 2020 of 3,708,000 km², demanding a reforestation of 454,037 km². Even though the regional economic growth can foster the necessary political environment for the commitment with optimal emission targets, the reduction of 38.9% of carbon emissions until 2020 proposed by Brazilian government seems too ambitious.