Integration of Process-based Soil Respiration Models with Whole-Ecosystem CO2 Measurements

Effects of plastic mulching on soil CO2 efflux in a cotton field in northwestern China

In Northwestern China, more and more traditional cultivation system (TC) with no mulching and flood irrigation have been replaced by modern cultivation technology (MC) combining plastic film mulching with drip irrigation. Does plastic film mulching increase or reduce soil CO₂ emission in arid areas? In order to study the effects of plastic mulching on soil CO₂ efflux, a field study was conducted to compare soil CO₂ concentration, soil CO₂ efflux, soil temperature and moisture between the TC treatment and the MC treatment during a cotton growing season in Northwestern China. The seasonal patterns of soil profile temperature and soil moisture in the TC treatment were similar to that in the MC treatment. The mean value of soil profile temperature in the MC treatment was higher than that in the TC treatment. Except for soil moisture at 15 cm depth, the mean value of soil moisture at 5 cm and 10 cm depths in the MC treatment was higher than that in the TC treatment. The variation patterns of soil CO₂ concentration and soil CO₂ efflux in MC treatment were different to that in the TC treatment. Although the peak of soil CO₂ concentration in the TC treatment was earlier than that in the MC treatment, the duration of soil CO₂ concentration with high values in TC treatment was shorter than that in the MC treatment. Based on the model of Fick’s first diffusion law, soil surface CO₂ efflux in the MC and TC treatments were determined. The surface CO₂ efflux in the TC treatment calculated by Fick’s first diffusion law model was in good agreement with the value measured by chamber method. The seasonal curve of soil surface CO₂ efflux in the MC treatment indicate the similar pattern with that in the TC treatment, and the rate of CO₂ efflux was lower in the MC system. In the MC treatment, the seasonal variation of soil surface efflux was explained more by soil moisture than by soil temperature. However, in the TC treatment, the seasonal variation of soil surface efflux was explained more by soil temperature than by soil moisture. Over the completely experimental period, accumulated rates of soil CO₂ efflux were 361 g C m⁻² and 474 g C m⁻² for the MC and TC system, respectively. We concluded that converting agricultural practices from traditional cultivation to the plastic mulching cultivation could reduce soil CO₂ efflux by approximately 110 g C m⁻² year⁻¹ in agricultural land in arid areas of Northwestern China.

A framework for incorporating ecology into Earth System Models is urgently needed

This commentary considers the benefits of a new framework to incorporate ecological processes in Earth System Models (ESMs) to both Earth system science and to ecology. Adding ecological processes to ESMs skillfully will likely improve the long-term performance of these models. The rigor required to achieve this will prompt ecologists to test complex ecological hypotheses on regional and global scales. Some candidate processes are suggested.

Dynamics of Soil Respiration in Alpine Wetland Meadows Exposed to Different Levels of Degradation in the Qinghai-Tibet Plateau, China

The effects of degradation of alpine wetland meadow on soil respiration (Rs) and the sensitivity of Rs to temperature (Q10) were measured in the Napa Lake region of Shangri-La on the southeastern edge of the Qinghai-Tibet Plateau. Rs was measured for 24 h during each of three different stages of the growing season on four different degraded levels. The results showed: (1) peak Rs occurred at around 5:00 p.m., regardless of the degree of degradation and growing season stage, with the maximum Rs reaching 10.05 μmol·m-2·s-1 in non-degraded meadows rather than other meadows; (2) the daily mean Rs value was 7.14-7.86 μmol·m-2·s-1 during the mid growing season in non-degraded meadows, and declined by 48.4-62.6% when degradation increased to the severely degraded level; (3) Q10 ranged from 7.1-11.3 in non-degraded meadows during the mid growing season, 5.5-8.0 and 6.2-8.2 during the early and late growing seasons, respectively, and show a decline of about 50% from the non-degraded meadows to severely degraded meadows; (4) Rs was correlated significantly with soil temperature at a depth of 0-5 cm (p < 0.05) on the diurnal scale, but not at the seasonal scale; (5) significant correlations were found between Rs and soil organic carbon (SOC), between biomass and SOC, and between Q10 and Rs (p < 0.05), which indicates that biomass and SOC potentially impact Q10. The results suggest that vegetation degradation impact both Rs and Q10 significantly. Also, we speculated that Q10 of alpine wetland meadow is probable greater at the boundary region than inner region of the Qinghai-Tibet Plateau, and shoule be a more sensitive indicator in the studying of climate change in this zone.

Uncertain future soil carbon dynamics under global change predicted by models constrained by total carbon measurements

Pool-based carbon (C) models are widely applied to predict soil C dynamics under global change and infer underlying mechanisms. However, it is unclear about the credibility of model-predicted C pool size, decay rate (k), and/or microbial C use efficiency (e) as only data on bulked total C is usually available for model constraining. Using observing system simulation experiments (OSSE), we constrained a two-pool model using simulated data sets of total soil C dynamics under topical hypotheses on responses of soil C dynamics to warming and elevated CO₂ (i.e., global change scenarios). The results indicated that the model predicted great uncertainties in C pool size, k, and e under all global change scenarios, resulting in the difficulty to correctly infer the presupposed "real" values of those parameters that are used to generate the simulated total soil C for constraining the model. Furthermore, the model using the constrained parameters generated divergent future soil C dynamics. Compared with the predictions using the presupposed real parameters (i.e., the real future C dynamics), the percentage uncertainty in 100-yr predictions using the constrained parameters was up to 45% depending on global change scenarios and data availability for model-constraining. Such great uncertainty was mainly due to the high collinearity among the model parameters. Using pool-based models, we argue that soil C pool size, k, and/or e and their responses to global change have to be estimated explicitly and empirically, rather than through model-fitting, in order to accurately predict C dynamics and infer underlying mechanisms. The OSSE approach provides a powerful way to identify data requirement for the new generation of model development and test model performance.

On improving the communication between models and data

The potential for model-data synthesis is growing in importance as we enter an era of 'big data', greater connectivity and faster computation. Realizing this potential requires that the research community broaden its perspective about how and why they interact with models. Models can be viewed as scaffolds that allow data at different scales to inform each other through our understanding of underlying processes. Perceptions of relevance, accessibility and informatics are presented as the primary barriers to broader adoption of models by the community, while an inability to fully utilize the breadth of expertise and data from the community is a primary barrier to model improvement. Overall, we promote a community-based paradigm to model-data synthesis and highlight some of the tools and techniques that facilitate this approach. Scientific workflows address critical informatics issues in transparency, repeatability and automation, while intuitive, flexible web-based interfaces make running and visualizing models more accessible. Bayesian statistics provides powerful tools for assimilating a diversity of data types and for the analysis of uncertainty. Uncertainty analyses enable new measurements to target those processes most limiting our predictive ability. Moving forward, tools for information management and data assimilation need to be improved and made more accessible.

Bayesian learning and predictability in a stochastic nonlinear dynamical model

Bayesian inference methods are applied within a Bayesian hierarchical modeling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations.

Persistent reduced ecosystem respiration after insect disturbance in high elevation forests

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no increase in ecosystem respiration from scales of several square metres up to an 84 km² valley. Rather, we found comparable declines in both gross primary productivity and respiration suggesting little change in net flux, with a transitory recovery of respiration 6–7 years after mortality associated with increased incorporation of leaf litter C into soil organic matter, followed by further decline in years 8–10. The mechanism of the impact of tree mortality caused by these biotic disturbances is consistent with reduced input rather than increased output of carbon.

Timing and magnitude of C partitioning through a young loblolly pine (Pinus taeda L.) stand using 13C labeling and shade treatments

The dynamics of rapid changes in carbon (C) partitioning within forest ecosystems are not well understood, which limits improvement of mechanistic models of C cycling. Our objective was to inform model processes by describing relationships between C partitioning and accessible environmental or physiological measurements, with a special emphasis on short-term C flux through a forest ecosystem. We exposed eight 7-year-old loblolly pine (Pinus taeda L.) trees to air enriched with (13)CO(2) and then implemented adjacent light shade (LS) and heavy shade (HS) treatments in order to manipulate C uptake and flux. The impacts of shading on photosynthesis, plant water potential, sap flow, basal area growth, root growth and soil CO(2) efflux rate (CER) were assessed for each tree over a 3-week period. The progression of the (13)C label was concurrently tracked from the atmosphere through foliage, phloem, roots and surface soil CO(2) efflux. The HS treatment significantly reduced C uptake, sap flow, stem growth and fine root standing crop, and resulted in greater residual soil water content to 1 m depth. Soil CER was strongly correlated with sap flow on the previous day, but not the current day, with no apparent treatment effect on the relationship. Although there were apparent reductions in new C flux belowground, the HS treatment did not noticeably reduce the magnitude of belowground autotrophic and heterotrophic respiration based on surface soil CER, which was overwhelmingly driven by soil temperature and moisture. The (13)C label was immediately detected in foliage on label day (half-life = 0.5 day), progressed through phloem by Day 2 (half-life = 4.7 days), roots by Days 2-4, and subsequently was evident as respiratory release from soil which peaked between Days 3 and 6. The δ(13)C of soil CO(2) efflux was strongly correlated with phloem δ(13)C on the previous day, or 2 days earlier. While the (13)C label was readily tracked through the ecosystem, the fate of root C through respiratory, mycorrhizal or exudative release pathways was not assessed. These data detail the timing and relative magnitude of C flux through various components of a young pine stand in relation to environmental conditions.

The model-data fusion pitfall: assuming certainty in an uncertain world

Model-data fusion is a powerful framework by which to combine models with various data streams (including observations at different spatial or temporal scales), and account for associated uncertainties. The approach can be used to constrain estimates of model states, rate constants, and driver sensitivities. The number of applications of model-data fusion in environmental biology and ecology has been rising steadily, offering insights into both model and data strengths and limitations. For reliable model-data fusion-based results, however, the approach taken must fully account for both model and data uncertainties in a statistically rigorous and transparent manner. Here we review and outline the cornerstones of a rigorous model-data fusion approach, highlighting the importance of properly accounting for uncertainty. We conclude by suggesting a code of best practices, which should serve to guide future efforts.