Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites

Post, H.; Vrugt, Jasper A.; Fox, A. M.; Vereecken, Harry; Franssen, Harrie‐Jan Hendricks

doi:10.1002/2015jg003297

Cited by 40 publications

(70 citation statements)

References 104 publications

(179 reference statements)

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“…Furthermore, emulators make it possible to calibrate complex models hierarchically, which would not be computationally feasible otherwise as hierarchical Bayesian modeling involves calibrating models many times at multiple spatial/temporal/experimental settings. For example, it is a known issue that sitelevel calibrations are not easily transferable to new sites or larger scales (Post et al, 2017). In that sense, Hierarchical…”

Section: Bruteforce Vs Emulatormentioning

confidence: 99%

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Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Fer

Kelly²,

Andrews

et al. 2018

Preprint

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Abstract. Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov Chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally-demanding models a n d large data sets. We describe an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature, and introduced novel approaches to the specification of both model and data uncertainties, including bias and autocorrelation corrections on multiple data streams. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard "bruteforce" MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the bruteforce approach, but reduced computation time by more than two orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (bruteforce) Bayesian data assimilation uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data.Our study furthers efforts toward reducing model uncertainties showing that the emulator method makes it possible to efficiently calibrate complex models. This efficient data assimilation method allows us to conduct more calibration experiments in relatively much shorter times, enabling constraining of numerous models using the expanding amount and types of data.

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Section: Bruteforce Vs Emulatormentioning

confidence: 99%

“…For example, NEE is a result of both primary production and respiration processes, and the model outputs were sensitive to parameters involved in both of these processes. If we were to assimilate only NEE, estimated parameters contributing to NEE might have compensating errors (Post et al, 2017).…”

Section: Autocorrelation Correction and Multiple Data Constraintsmentioning

confidence: 99%

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Fer

Kelly²,

Andrews

et al. 2018

Preprint

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“…On the other hand, if the calibration data are sensitive to the parameters, even a complex model can sometimes be well constrained by using a single type of observations. For example, Post et al (2017) estimated eight CLM parameters using 1-year records of half-hourly NEE observations at four sites, and found that for most sites the CLM parameters can be well constrained with their 95 % confidence intervals close to the maximum a posteriori estimates. For the only site where the parameter uncertainties were relatively large, they concluded that the simulated NEE was less sensitive to these parameters.…”

Section: Synthetic Study With Pseudo-datamentioning

confidence: 99%

“…Thus, the DREAM scheme is particularly applicable to complex and multimodal optimization problems. Recently, Post et al (2017) reported a successful application of DREAM in estimation of the complex Community Land Model (CLM) using 1-year records of NEE observations. They found that the posterior parameter estimates were superior to their default values in the ability to track and explain the measured NEE data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Ricciuto

Walker

et al. 2017

Biogeosciences

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Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

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“…The model used was CLM4.5 and compared with EC measurements and LAI from the RapidEye satellite. We applied successfully validated parameter estimates from Post et al (2016) for C 3 grass, evergreen coniferous forest and broadleaf deciduous forest. For C 3 crops, we estimated a new set of parameters as the estimated parameters by Post et al (2016) did not improve the model-data fit in verification experiments, using the DiffeRential Evolution Adaptive Metropolis (DREAM; Vrugt, 2015).…”

mentioning

confidence: 99%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites

Cited by 40 publications

References 104 publications

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

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