Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach

Iizumi, Toshichika; Yokozawa, Masayuki; Nishimori, Motoki

doi:10.1016/j.agrformet.2008.08.015

Cited by 169 publications

(129 citation statements)

References 27 publications

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“…There have also been a few parameter uncertainty estimates based on a Bayesian approach to model calibration (Iizumi et al 2009;Wallach et al 2012)). A simpler alternative that has been applied to crop models is the GLUE algorithm, which explores the space of possible parameter values, calculates a likelihood and eliminates parameter vectors whose likelihood is below a threshold (Wang et al 2005).…”

Section: Creating Ensembles Based On a Single Model With Multiple Parmentioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

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Section: Creating Ensembles Based On a Single Model With Multiple Parmentioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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“…The Metropolis-Hastings algorithm (Metropolis et al, 1953;Hastings, 1970) was computationally implemented to carry out the MCMC by Iizumi et al (2009). The algorithm of the MCMC used for this study was the same as that in Iizumi et al (2009).…”

Section: Metropolis-hastings Algorithmmentioning

confidence: 99%

“…The algorithm of the MCMC used for this study was the same as that in Iizumi et al (2009). Briefly, the algorithm consisted of the following 10 steps:…”

Section: Metropolis-hastings Algorithmmentioning

confidence: 99%

Statistical downscaling with Bayesian inference: Estimating global solar radiation from reanalysis and limited observed data

Iizumi¹,

Nishimori²,

Yokozawa³

et al. 2011

Intl Journal of Climatology

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Daily global solar radiation (SR) is one of essential weather inputs for crop, hydrological, and other simulation models to calculate biomass production and potential evapotranspiration. The availability of long-term observed SR data is, however, limited, especially in developing countries. This hinders climate applications in various sectors in these countries. To overcome this difficulty, we here propose a method to infer the reasonable daily SR condition for past decades from global reanalysis and limited observed SR data. The method consists of the regression-based statistical downscaling method and two empirical models for estimating the SR condition (i.e. the S-model and the DTR/RH-model). These empirical models were independent in terms of the variables explaining the SR condition. The regression models were trained on the basis of the SR conditions estimated by the S-model and the DTR/RH-model instead of the observed SR data. The Markov Chain Monte Carlo (MCMC) technique was applied to determine the parameter values of these models that guide the models to provide SR conditions that are close in value to each other at both the site and domain-mean scales. After that, we computed the SR condition over the 30 years from 1978 through 2007 at 17 sites in the Vietnam Mekong Delta area using the determined parameter values. The inferred SR condition was close in value to the corresponding observations available from the literature. This suggests that the proposed method yielded a reasonable inference of the SR condition at the sites despite the limited availability of observed SR data. The provided estimates of the daily SR condition over the past 30 years are useful for climate applications in agricultural, hydrological, and other sectors in this area.

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“…In a number of studies (e.g. Pathak et al, 2012;Wang et al, 2005;Iizumi et al, 2009) the hydrological model has been calibrated as a first step and the plant growth model as a second step in order to reduce the number of parameters varied in one calibration step. However, in such a setup, feedbacks between biomass production and hydrology are not considered (Pauwels et al, 2007).…”

Section: T Houska Et Al: Monte Carlo-based Calibration and Uncertaimentioning

confidence: 99%

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

et al. 2014

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Abstract.Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures -for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface.We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation.The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 10 6 model runs randomly drawn from a uniform distribution.The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

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Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach

Cited by 169 publications

References 27 publications

Lessons from climate modeling on the design and use of ensembles for crop modeling

Lessons from climate modeling on the design and use of ensembles for crop modeling

Statistical downscaling with Bayesian inference: Estimating global solar radiation from reanalysis and limited observed data

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

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