Using a Bayesian approach to parameter estimation; comparison of the 
GLUE and MCMC methods

Makowski, David; Wallach, Daniel; Tremblay, Marie

doi:10.1051/agro:2002007

Cited by 136 publications

(56 citation statements)

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“…Acceptance rates across all models ranged between 21% and 48%, values considered acceptable (see e.g. Gilks et al, 1995;Makowski et al, 2002). Based on the visual inspection of the plotting series for all parameters and variables of concern (e.g.…”

Section: Validation Of the M-h Algorithm Resultsmentioning

confidence: 99%

“…For the suite of alternative conceptual models, input and parameter values are sampled using the MetropolisHastings (M-H) algorithm (Metropolis et al, 1953;Hastings, 1970;Chib and Greenberg, 1995;Gilks et al, 1995) to generate simulators of the system.…”

Section: Multi-model Approach To Account For Conceptual Model and Scementioning

confidence: 99%

“…Sampling of parameters from the prior ranges presented in Table 3 was performed using the Metropolis-Hastings (M-H) algorithm (Metropolis et al, 1953;Hastings, 1970;Chib and Greenberg, 1995;Gilks et al, 1995). From exploratory runs, 100 parallel Markov chains starting from randomly selected points defined in the prior parameter ranges (Table 3) were implemented to proceed with the M-H algorithm for models M1 and M2.…”

Section: Simulation Proceduresmentioning

confidence: 99%

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Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Rojas

Batelaan

Feyen

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) -North Chile using a novel and fully integrated multi-model approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, model weights used in BMA for multi-model aggregation are obtained from GLUE-based likelihood values. These model weights are based on model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUEbased cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding model weights used for multi-model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. ContriCorrespondence to: R. Rojas (rodrigo.rojas@jrc.ec.europa.eu) bution of conceptual model uncertainty to the predictive uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual model uncertainty.

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Section: Validation Of the M-h Algorithm Resultsmentioning

confidence: 99%

Section: Multi-model Approach To Account For Conceptual Model and Scementioning

confidence: 99%

Section: Simulation Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Rojas

Batelaan

Feyen

et al. 2010

Hydrol. Earth Syst. Sci.

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“…With such restricted calibration material, large uncertainties in estimates of impact variables such as yield can result (Makowski et al 2002, Palosuo et al 2011, Watson et al 2014. Aside from the limitations in quantity of data, there are also numerous choices available in the approaches used to calibrate models.…”

Section: Shortcomingsmentioning

confidence: 99%

Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

Pirttioja¹,

Carter²,

Fronzek³

et al. 2015

Clim. Res.

139

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This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to + 9 degrees C) and precipitation (-50 to + 50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model developmen

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“…MCMC is one of the most important numerical technique for creating a sample from the posterior distribution, which has been widely used in hydrological modelling to quantify parameter uncertainties (e.g. Kuczera & Parent, 1998;Campbell et al, 1999;Makowski et al, 2002;Vrugt et al, 2003). Its underlying rationale is to set up a Markov chain to simulate the true posterior distribution by generating samples from a random walk.…”

Section: Swat Model Uncertaintiesmentioning

confidence: 99%

A Review On Accuracy and Uncertainty of Spatial Data and Analyses with special reference to Urban and Hydrological Modelling

Devendran

Gnanappazham

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: Uncertainty, Spatial Data Quality, Modelling, Urban simulation models, CA, Hydrological models, SWAT, Error propagation ABSTRACT:Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.

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Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods

Cited by 136 publications

References 0 publications

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

A Review On Accuracy and Uncertainty of Spatial Data and Analyses with special reference to Urban and Hydrological Modelling

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