Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions

Wetlands are the largest natural source of methane (CH₄ ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH₄ , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH₄ production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH₄ emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH₄ production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH₄ emissions. Based on a sensitivity analysis, CH₄ emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH₄ transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH₄ production and CH₄ emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH₄ production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH₄ emissions across biomes. These processes and associated parameters for CH₄ emissions among and within the wetlands provide useful insights for interpreting observed net CH₄ fluxes, estimating sensitivities to biophysical variables, and modeling global CH₄ fluxes.

[Multi-disciplinary knowledge integration and its technical approaches in the integrated ecology of macroecosystem science]

The development of contemporary macroecosystem sciences requires to comprehensively understand the process mechanism and model mechanism of large-scale macroecosystem structure and function, spatial variation, and dynamic evolution, to realize quantitative simulation, scientific assessment, prediction and early warning of ecosystem change and its impacts on human well-being, and to serve the utilization, protection, regulation, and management of ecosystems. Therefore, a new research field of large-scale integrated ecology of macroecosystem science (IEMES) is emerging. Based on the systematic analysis of the basic theories, approaches and key technologies of integrated ecology of macroecosystem science, the following basic understandings have been formed: 1) IEMES takes macroecosystem at regional, continental, and global scales as the research object, and adopts the methods and technologies of multidisciplinary knowledge integration. It is aimed to solve the major resource and environmental problems during the development of human society, such as food security, resource security, ecological security, and environmental security. 2) The basic scientific and technological tasks of IEMES are to understand the basic structural and functional properties of macroecosystem, monitor the changes of ecosystem state, explain the spatiotemporal evolution of ecosystem, uncover the mechanism of ecosystem operation and maintenance process, quantitatively evaluate the functional state and service capacity of ecosystem, predict the dynamic evolution and geographical pattern of ecosystem, provide early warning of ecosystem changes and ecological environmental disasters. 3) It is needed to reconstruct the theory and methodology system of "multi-source data analysis, multi-model simulation, multi-disciplinary knowledge fusion" and develop key technology of multi-disciplinary knowledge fusion of "multi-scale observation, multi-method verification, multi-process fusion, and cross-scale simulation" for IEMES. 4) The continental scale multi-spatiotemporal ecosystem observation and experiment network is the basic scientific and technological facility to carry out deep integration of multi-disciplinary knowledge. It is necessary to develop key technology of multi-disciplinary ecological knowledge integration of multi-factor, multi-process, multi-interface, multi-medium, multi-scale, and multi-method around the regional, continental, and global scale macroecosystem science issues.

Improving plant allometry by fusing forest models and remote sensing

Allometry determines how tree shape and function scale with each other, related through size. Allometric relationships help scale processes from the individual to the global scale and constitute a core component of vegetation models. Allometric relationships have been expected to emerge from optimisation theory, yet this does not suitably predict empirical data. Here we argue that the fusion of high-resolution data, such as those derived from airborne laser scanning, with individual-based forest modelling offers insight into how plant size contributes to large-scale biogeochemical processes. We review the challenges in allometric scaling, how they can be tackled by advances in data-model fusion, and how individual-based models can serve as data integrators for dynamic global vegetation models.

Application of a two-pool model to soil carbon dynamics under elevated CO2

Elevated atmospheric CO2 concentrations increase plant productivity and affect soil microbial communities, with possible consequences for the turnover rate of soil carbon (C) pools and feedbacks to the atmosphere. In a previous analysis (Van Groenigen et al., 2014), we used experimental data to inform a one-pool model and showed that elevated CO2 increases the decomposition rate of soil organic C, negating the storage potential of soil. However, a two-pool soil model can potentially explain patterns of soil C dynamics without invoking effects of CO2 on decomposition rates. To address this issue, we refit our data to a two-pool soil C model. We found that CO2 enrichment increases decomposition rates of both fast and slow C pools. In addition, elevated CO2 decreased the carbon use efficiency of soil microbes (CUE), thereby further reducing soil C storage. These findings are consistent with numerous empirical studies and corroborate the results from our previous analysis. To facilitate understanding of C dynamics, we suggest that empirical and theoretical studies incorporate multiple soil C pools with potentially variable decomposition rates.

Predictability of the terrestrial carbon cycle

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool-dominant transfers, and the first-order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.