Do we necessarily need to increase model complexity to forecast soil carbon dynamics?

Potential future changes in soil carbon dynamics in the Ziwuling Forest, China under different climate change scenarios

Kohlenstoffumsatz in aggregierten Böden bestimmt mit Hilfe der natürlichen 13C Abundanz

Microbial processes are known to substantially influence carbon dynamics in soil, which can be altered by changes in community structure. However, the explicit representation of soil microbial diversity in ecosystem models is still in need of improvement. Model-based estimates are crucial to quantify links between functional diversity of soil microbes and ecosystem functioning, in particular soil carbon turnover and storage.Incorporating functional diversity is well established in vegetation models and is the methodological basis for the presented approach. Trait-based modelling is used here to directly connect community structure to corresponding carbon fluxes and thereby enable implications for the marsh soils of the Elbe estuary, which represent an important carbon sink. Modelled microbial diversity is based on multiple functional types that vary in key traits related to carbon cycling. The simulated microbial community develops population dynamics based on the environmental conditions, leading to selection of certain functional types. This allows predictions of the abundances and potential shifts in the community structure resulting in altered soil carbon dynamics. Parameter values for the microbial model are derived from empirical data and a specifically developed experimental approach that investigates microbial growth and uptake kinetics. We assess the impact of functional diversity on carbon dynamics in marsh soils by comparing soil carbon fluxes in the model with and without explicitly modelled microbial functional diversity. The findings of the study are expected to enhance projections of soil organic carbon storage in wetland ecosystems as well as emphasizing the role of microbial functional diversity for ecosystem carbon dynamics.

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

QUANTIFYING PEATLAND CARBON DYNAMICS USING MECHANISTICALLY-BASED BIOGEOCHEMISTRY MODELS

Effects of vegetation restoration on soil organic carbon sequestration at multiple scales in semi-arid Loess Plateau, China

Soil carbon dynamics under young tropical secondary forests on former pastures—A case study from Panama

Biomass carbon accumulation in forest ecosystems is a widespread phenomenon at both regional and global scales. However, as coupled carbon-climate models predicted, a positive feedback could be triggered if accelerated soil carbon decomposition offsets enhanced vegetation growth under a warming climate. It is thus crucial to reveal whether and how soil carbon stock in forest ecosystems has changed over recent decades. However, large-scale changes in soil carbon stock across forest ecosystems have not yet been carefully examined at both regional and global scales, which have been widely perceived as a big bottleneck in untangling carbon-climate feedback. Using newly developed database and sophisticated data mining approach, here we evaluated temporal changes in topsoil carbon stock across major forest ecosystem in China and analysed potential drivers in soil carbon dynamics over broad geographical scale. Our results indicated that topsoil carbon stock increased significantly within all of five major forest types during the period of 1980s-2000s, with an overall rate of 20.0 g C m(-2) yr(-1) (95% confidence interval, 14.1-25.5). The magnitude of soil carbon accumulation across coniferous forests and coniferous/broadleaved mixed forests exhibited meaningful increases with both mean annual temperature and precipitation. Moreover, soil carbon dynamics across these forest ecosystems were positively associated with clay content, with a larger amount of SOC accumulation occurring in fine-textured soils. In contrast, changes in soil carbon stock across broadleaved forests were insensitive to either climatic or edaphic variables. Overall, these results suggest that soil carbon accumulation does not counteract vegetation carbon sequestration across China's forest ecosystems. The combination of soil carbon accumulation and vegetation carbon sequestration triggers a negative feedback to climate warming, rather than a positive feedback predicted by coupled carbon-climate models.

In this study, the dynamics of soil thermal, hydrologic, and ecosystem processes were coupled to project how the carbon budgets of boreal forests will respond to changes in atmospheric CO2, climate, and fire disturbance. The ability of the model to simulate gross primary production and ecosystem respiration was verified for a mature black spruce ecosystem in Canada, the age‐dependent pattern of the simulated vegetation carbon was verified with inventory data on aboveground growth of Alaskan black spruce forests, and the model was applied to a postfire chronosequence in interior Alaska. The comparison between the simulated soil temperature and field‐based estimates during the growing season (May to September) of 1997 revealed that the model was able to accurately simulate monthly temperatures at 10 cm (R> 0.93) for control and burned stands of the fire chronosequence. Similarly, the simulated and field‐based estimates of soil respiration for control and burned stands were correlated (R= 0.84 and 0.74 for control and burned stands, respectively). The simulated and observed decadal to century‐scale dynamics of soil temperature and carbon dynamics, which are represented by mean monthly values of these variables during the growing season, were correlated among stands (R= 0.93 and 0.71 for soil temperature at 20‐ and 10‐cm depths,R= 0.95 and 0.91 for soil respiration and soil carbon, respectively). Sensitivity analyses indicate that along with differences in fire and climate history a number of other factors influence the response of carbon dynamics to fire disturbance. These factors include nitrogen fixation, the growth of moss, changes in the depth of the organic layer, soil drainage, and fire severity.

Using present and past climosequences to estimate soil organic carbon and related physical quality indicators under future climatic conditions

<p>Soil organic carbon (SOC) is essential for soil fertility and further represents a global carbon stock with potential to control atmospheric CO<sub>2</sub> concentrations. Due to intense agricultural management, SOC is decreasing in many parts of the world, meaning that the soils act as CO<sub>2</sub> sources rather than CO<sub>2</sub> sinks, which they could have the capacity to be. Therefore, it is important to identify pertinent agricultural management practices that allow for high productivity, but at the same time allow for carbon sequestration and increase in SOC.</p><p>In order to document changes in SOC, it is necessary to monitor SOC over decadal time scales, since changes occur slowly and are small as compared with existing stocks. The SOC content in Danish agricultural soils has been monitored at approx. 10-yr intervals (1986, 1997, 2009) since the first systematic national observations in 1986, where soils were sampled from a national 7 km x 7 km grid.</p><p>In 2018, a new sampling campaign was conducted from the national 7 km x 7 km grid and soils were analysed for SOC to 1 m depth. The procedures applied in 2018 allowed for more precise relocation of the sampling points from 2009 as compared to precision obtained during the period from 1986-2009. Further, measurements in 2018 included assessment of soil bulk density and stone content in the upper 0-50 cm, which was not measured previously. Thus, one of the aims of the study was to evaluate how more precise point-specific information on bulk density and stone fractions affected the calculated SOC stocks across different soil types and management practices.</p><p>The point-specific bulk density measured in 2018 were on average lower than the bulk densities used previously, which were retrieved from a database of texture-based soil classes. The volumetric stone fraction in the upper 0-50 cm was found to be <5% for roughly 90% of the soils, whereas <3% of the soils had stone fractions of >10%. On average, the inclusion of point-specific bulk density and stone fractions lead to approx. 5% lower SOC estimation, with equal approximmately contribution from the two variables.</p>

Organic carbon mineralization in soils of a natural forest and a forest plantation of southeastern China

Soil carbon dynamics in perennial biomass crops on marginally productive cropland in southern Canada

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model’s soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane Grus grus in a spatio‐temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio‐temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity. Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact‐focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability but can be a useful approach to improving insight gained from model outputs.