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Afforestation projects for mitigating CO2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance (gsmax) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m−2 y−1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change. Afforestation projects for mitigating CO2 emissions require reliable monitoring of carbon storage and plant growth as key indicators of carbon fixation by afforestation. We developed the monitoring method for predicting carbon fixation in plantations, combining a process‐based ecosystem model and field data. We demonstrated how to estimate plant carbon pools and understand the parameter changes with plant growth stages and the uncertainty of predicted carbon fixation in a plantation by an optimization scheme using field survey data.

Current terrestrial ecosystem models are usually driven with global average annual atmospheric carbon dioxide (CO2) concentration data at the global scale. However, high‐precision CO2 measurement from eddy flux towers showed that seasonal, spatial surface atmospheric CO2 concentration differences were as large as 35 ppmv and the site‐level tests indicated that the CO2 variation exhibited different effects on plant photosynthesis. Here we used a process‐based ecosystem model driven with two spatially and temporally explicit CO2 data sets to analyze the atmospheric CO2 fertilization effects on the global carbon dynamics of terrestrial ecosystems from 2003 to 2010. Our results demonstrated that CO2 seasonal variation had a negative effect on plant carbon assimilation, while CO2 spatial variation exhibited a positive impact. When both CO2 seasonal and spatial effects were considered, global gross primary production and net ecosystem production were 1.7 Pg C·yr−1 and 0.08 Pg C·yr−1 higher than the simulation using uniformly distributed CO2 data set and the difference was significant in tropical and temperate evergreen broadleaf forest regions. This study suggests that the CO2 observation network should be expanded so that the realistic CO2 variation can be incorporated into the land surface models to adequately account for CO2 fertilization effects on global terrestrial ecosystem carbon dynamics.

Land use and land cover change (LUCC) plays an important role in determining the spatial distribution, magnitude, and temporal change of terrestrial carbon sources and sinks. However, the impacts of LUCC are not well understood and quantified over large areas. The goal of this study was to quantify the spatial and temporal patterns of carbon dynamics in various terrestrial ecosystems in the southeastern United States from 1992 to 2050 using a process-based modeling system and then to investigate the impacts of LUCC. Spatial LUCC information was reconstructed and projected using the FOREcasting SCEnarios of future land cover (FORE-SCE) model according to information derived from Landsat observations and other sources. Results indicated that urban expansion (from 3.7% in 1992 to 9.2% in 2050) was expected to be the primary driver for other land cover changes in the region, leading to various declines in forest, cropland, and hay/pasture. The region was projected to be a carbon sink of 60.4 gC m−2 yr−1 on average during the study period, primarily due to the legacy impacts of large-scale conversion of cropland to forest that happened since the 1950s. Nevertheless, the regional carbon sequestration rate was expected to decline because of the slowing down of carbon accumulation in aging forests and the decline of forest area.

The mid-latitude ecotone (MLE)—a transition zone between boreal and temperate forests, which includes the regions of Northeast Asia around 30°–60° N latitudes—delivers different ecosystem functions depending on different management activities. In this study, we assessed forest volume and net primary productivity changes in the MLE of Northeast Asia under different ecological characteristics, as well as various current management activities, using the BioGeoChemistry Management Model (BGC-MAN). We selected five pilot sites for pine (Scots pine and Korean red pine; Pinus sylvestris and P. densiflora), oak (Quercus spp.), and larch forests (Dahurian larch and Siberian larch; Larix gmelinii and L. sibirica), respectively, which covered the transition zone across the MLE from Lake Baikal, Russia to Kyushu, Japan, including Mongolia, Northeast China, and the Korean Peninsula. With site-specific information, soil characteristics, and management descriptions by forest species, we established their management characteristics as natural preserved forests, degraded forests, sandy and cold forest stands, and forests exposed to fires. We simulated forest volume (m3) and net primary productivity (Mg C ha−1) during 1960–2005 and compared the results with published literature. They were in the range of those specified in previous studies, with some site-levels under or over estimation, but unbiased estimates in their mean values for pine, oak, and larch forests. Annual rates of change in volume and net primary productivity differed by latitude, site conditions, and climatic characteristics. For larch forests, we identified a high mountain ecotype which warrants a separate model parameterization. We detected changes in forest ecosystems, explaining ecological transition in the Northeast Asian MLE. Under the transition, we need to resolve expected problems through appropriate forest management and social efforts.

Context Global temperatures are projected to increase and affect forests and wildlife populations. Forest management can potentially mitigate climate-induced changes through promoting carbon sequestration, forest resilience, and facilitated change. Objectives We modeled direct and indirect effects of climate change on avian abundance through changes in forest landscapes and assessed impacts on bird abundances of forest management strategies designed to mitigate climate change effects. Methods We coupled a Bayesian hierarchical model with a spatially explicit landscape simulation model (LANDIS PRO) to predict avian relative abundance. We considered multiple climate scenarios and forest management scenarios focused on carbon sequestration, forest resilience, and facilitated change over 100 years. Results Management had a greater impact on avian abundance (almost 50% change under some scenarios) than climate (<3% change) and only early successional and coniferous forest showed significant change in percent cover across time. The northern bobwhite was the only species that changed in abundance due to climate-induced changes in vegetation. Northern bobwhite, prairie warbler, and blue-winged warbler generally increased in response to warming temperatures but prairie warbler exhibited a non-linear response and began to decline as summer maximum temperatures exceeded 36 °C at the end of the century. Conclusion Linking empirical models with process-based landscape change models can be an effective way to predict climate change and management impacts on wildlife, but time frames greater than 100 years may be required to see climate related effects. We suggest that future research carefully consider species-specific effects and interactions between management and climate.

Assessing potential impacts of 1.5 °C and 2 °C global warming and identifying the risks of further 0.5 °C warming are crucial for climate adaptation and disaster risk management. Four earth system models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a process-based ecosystem model are used in this study to assess the impacts and potential risks of the two warming targets on the carbon cycle of China’s terrestrial ecosystems. Results show that warming generally stimulates the increase of net primary productivity (NPP) and net ecosystem productivity (NEP) under both representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios. The projected increments of NPP are higher at 2 °C warming than that at 1.5 °C warming for both RCP4.5 and RCP8.5 scenarios; approximately 13% and 19% under RCP4.5, and 12.5% and 20% under RCP8.5 at 1.5 °C and 2 °C warming, respectively. However, the increasing rate of NPP was projected to decline at 2 °C warming under the RCP4.5 scenario, and the further 0.5 °C temperature rising induces the decreased NPP linear slopes in more than 81% areas of China’s ecosystems. The total NEP is projected to be increased by 53% at 1.5 °C, and by 81% at 2 °C warming. NEP was projected to increase approximately by 28% with the additional 0.5 °C warming. Furthermore, the increasing rate of NEP weakens at 2 °C warming, especially under the RCP8.5 scenario. In summary, China’s total NPP and NEP were projected to increase under both 1.5 °C and 2 °C warming scenarios, although adverse effects (i.e., the drop of NPP growth and the reduction of carbon sequestration capacity) would occur in some regions such as northern China in the process of global warming.

Evaluating the responses of net primary productivity (NPP) to climate change is essential for regional ecosystem management and adaptations to climate change. The Yangtze River Economic Belt (YREB) is a key ecological functional area and hotspot of carbon sequestration in China due to the high degree of forest coverage. We used a process-based ecosystem model to project terrestrial NPP and analyzed the response to climate change over the 21st century in the YREB under two representative concentration pathway (RCP) scenarios using the regional climate model. The results show that the projection of NPP generally increased by 13.5% under RCP4.5 and 16.4% under RCP8.5 in the middle of the century, by 23% under RCP4.5, and by 35% under RCP8.5 in the late term of the century compared with that from a reference period of current climate conditions (1985–2006). The rate of NPP change under the RCP8.5 scenario is higher than that under the RCP4.5 scenario. Similarly, the NPP is also projected to increase both with 1.5 and 2 °C global warming targets in the YREB. The magnitudes of NPP increment are approximately 14.7% with 1.5 °C and 21% with 2 °C warming targets compared with the current climate, which are higher than the average increments of China. Although NPP is projected to increase under the two scenarios, the tendency of NPP increasingly exhibits a slowdown after the 2060 s under the RCP4.5 scenario, and the growth rate of NPP is projected to drop in more than 31% of regional areas with the additional 0.5 °C warming. In contrast, under the RCP8.5 scenario, the trend in NPP keeps rising substantially, even above 2 °C global warming. However, the NPP in some provinces, including Jiangxi and Hunan, is projected to reduce at the end of the 21st century, probably because of temperature rises, precipitation decreases, and water demand increases. Generally, the NPP is projected to increase due to climate change, particularly temperature increase. However, temperature rising does not always show a positive effect on NPP increasing; the growth rate of NPP will slow down under the RCP4.5 scenario in the mid-late 21st century, and NPP will also reduce by the end of this century under the RCP8.5 scenario in some places, probably presenting some risks to terrestrial ecosystems in these areas, in terms of reduced functions and service decline, a weakened capacity of carbon sequestration, and reduced agricultural production.

Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model–data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models.

CO2 exchange processes in forest ecosystems are of profound ecological and economic importance, meaning there is a need for generally applicable simulation tools. However, process-based ecosystem models, which are in principal suitable for the task, are commonly evaluated at only a few sites and for a limited number of plant species. It is thus often unclear if the processes and parameters involved are suitable for model application at a regional scale. We tested the LandscapeDNDC forest growth module PnET (derived from the Photosynthetic / EvapoTranspiration model) with site-specific as well as multi-site calibrated parameters using independent data sets of eddy covariance measurements across a European transect. Although site-specific parametrization is superior (r2 for pooled Gross Primary Production (GPP) during calibration period: site-specific = 0.93, multi-site = 0.88; r2 for pooled Net Ecosystem Exchange (NEE) during calibration period: site-specific = 0.81, multi-site = 0.73), we show that general parameters are able to represent carbon uptake over periods of several years. The procedure has been applied for the three most dominant European tree species i.e., Scots pine, Norway spruce and European beech. In addition, we discuss potential model improvements with regard to the sensitivity of parameters to site conditions differentiated into climate, nutrient and drought influences.

Current terrestrial ecosystem models are usually driven with global average annual atmospheric carbon dioxide ( CO 2 ) concentration data at the global scale. However, high‐precision CO 2 measurement from eddy flux towers showed that seasonal, spatial surface atmospheric CO 2 concentration differences were as large as 35 ppmv and the site‐level tests indicated that the CO 2 variation exhibited different effects on plant photosynthesis. Here we used a process‐based ecosystem model driven with two spatially and temporally explicit CO 2 data sets to analyze the atmospheric CO 2 fertilization effects on the global carbon dynamics of terrestrial ecosystems from 2003 to 2010. Our results demonstrated that CO 2 seasonal variation had a negative effect on plant carbon assimilation, while CO 2 spatial variation exhibited a positive impact. When both CO 2 seasonal and spatial effects were considered, global gross primary production and net ecosystem production were 1.7 Pg C·yr −1 and 0.08 Pg C·yr −1 higher than the simulation using uniformly distributed CO 2 data set and the difference was significant in tropical and temperate evergreen broadleaf forest regions. This study suggests that the CO 2 observation network should be expanded so that the realistic CO 2 variation can be incorporated into the land surface models to adequately account for CO 2 fertilization effects on global terrestrial ecosystem carbon dynamics.