Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models

Yeluripati, Jagadeesh; Oijen, Marcel van; Wattenbach, Martin; Neftel, A.; Ammann, Andrea Brandes; Parton, William J.; Smith, Pete

doi:10.1016/j.soilbio.2009.08.021

Cited by 59 publications

(34 citation statements)

References 24 publications

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“…Models rely on assumptions on status (e.g., equilibrium state and pool size) of the system and/or model-fitting techniques to parameterize pool sizes and their transformation controlled by decay rates and microbial C use efficiency. This issue is partially overcome by new emerging approaches including replacing conceptual model C pools with measurable C fractions according to their physical/chemical properties (e.g., Zimmermann et al 2007), relating C pool size to local climate, soil, and/or management conditions (e.g., Basso et al 2011, Weihermüller et al 2013, tracing different C pools using C isotopes (e.g., von Lutzow et al 2007, Ogle andPendall 2015), and integrating available prior information to constrain parameter space based on Bayesian calibration (e.g., Van Oijen et al 2005, Yeluripati et al 2009, Ogle and Pendall 2015. Nevertheless, the equilibrium assumption underpinning the spin-up-run approach is widely debated in the literature because few soils are really at the equilibrium state due to environmental variability and various disturbances (Wutzler and Reichstein 2007).…”

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confidence: 99%

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

Luo

Wang

Sun

2017

Ecological Applications

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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 CO (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.

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confidence: 99%

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

Luo

Wang

Sun

2017

Ecological Applications

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“…Throughout this work we tried to challenge the assumption that total SOC and different SOC pools are in steady state. Dropping the steady-state assumption leads to the problem of initializing the different conceptual SOC pools (Yeluripati et al, 2009). The most common way to deal with initialization problems of conceptual and nonmeasurable SOC pools is to perform spin-up runs of the model in an undisturbed environment.…”

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confidence: 99%

“…The most common way to deal with initialization problems of conceptual and nonmeasurable SOC pools is to perform spin-up runs of the model in an undisturbed environment. Then estimates about initial SOC pools are retrieved based on a reconstructed disturbance history (Falloon and Smith, 2000;Wutzler and Reichstein, 2007;Yeluripati et al, 2009). Due to its simplicity the steady-state equations for the ICBM can still be derived relatively easily: (start − tlag (L,R) ) was taken as the initial 14 C signature of litter input, where start denotes the starting year of simulations.…”

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Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints

et al. 2014

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Abstract. Soils of temperate forests store significant amounts of organic matter and are considered to be net sinks of atmospheric CO 2 . Soil organic carbon (SOC) turnover has been studied using the 14 C values of bulk SOC or different SOC fractions as observational constraints in SOC models. Further, the 14 C values of CO 2 that evolved during the incubation of soil and roots have been widely used together with 14 C of total soil respiration to partition soil respiration into heterotrophic respiration (HR) and rhizosphere respiration. However, these data have not been used as joint observational constraints to determine SOC turnover times. Thus, we focus on (1) how different combinations of observational constraints help to narrow estimates of turnover times and other parameters of a simple two-pool model, the Introductory Carbon Balance Model (ICBM); (2) whether relaxing the steady-state assumption in a multiple constraints approach allows the source/sink strength of the soil to be determined while estimating turnover times at the same time. To this end ICBM was adapted to model SOC and SO 14 C in parallel with litterfall and the 14 C of litterfall as driving variables. The 14 C of the atmosphere with its prominent bomb peak was used as a proxy for the 14 C of litterfall. Data from three spruce-dominated temperate forests in Germany and the USA (Coulissenhieb II, Solling D0 and Howland Tower site) were used to estimate the parameters of ICBM via Bayesian calibration. Key findings are as follows: (1) the joint use of all four observational constraints (SOC stock and its 14 C, HR flux and its 14 C) helped to considerably narrow turnover times of the young pool (primarily by 14 C of HR) and the old pool (primarily by 14 C of SOC). Furthermore, the joint use of all observational constraints made it possible to constrain the humification factor in ICBM, which describes the fraction of the annual outflux from the young pool that enters the old pool. The Bayesian parameter estimation yielded the following turnover times (mean ± standard deviation) for SOC in the young pool: Coulissenhieb II 1.1 ± 0.5 years, Solling D0 5.7 ± 0.8 years and Howland Tower 0.8 ± 0.4 years. Turnover times for the old pool were 377 ± 61 years (Coulissenhieb II), 313 ± 66 years (Solling D0) and 184 ± 42 years (Howland Tower), respectively. (2) At all three sites the multiple constraints approach was not able to determine if the soil has been losing or storing carbon. Nevertheless, the relaxed steady-state assumption hardly introduced any additional uncertainty for the other parameter estimates. Overall the results suggest that using 14 C data from more than one carbon pool or flux helps to better constrain SOC models.

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“…Yeluripati et al [40] performed Bayesian calibration using soil respiration measurements to initialize the SOC pools in the DAYCENT model [41,42]. They found that the model pools could be effectively initialized using soil respiration data, which is especially important when trying to model at sites (or large-scale spatial model runs) where land-use history is unknown.…”

Section: Numerical Methods In Systems Sciencementioning

confidence: 99%

Systems approaches in global change and biogeochemistry research

Smith

Albanito

Bell

et al. 2012

Phil. Trans. R. Soc. B

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Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.

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Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models

Cited by 59 publications

References 24 publications

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

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

Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints

Systems approaches in global change and biogeochemistry research

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Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models

Cited by 59 publications

References 24 publications

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

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

Bayesian calibration of a soil organic carbon model using Δ&lt;sup&gt;14&lt;/sup&gt;C measurements of soil organic carbon and heterotrophic respiration as joint constraints

Systems approaches in global change and biogeochemistry research

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Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints