Recent developments and challenges in crop growth modelling: uncertainty analysis, global sensitivity analysis and data assimilation

2017 ◽  
pp. 129-136
Author(s):  
I.L. López-Cruz
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Uncertainty Analysis ◽  
Global Sensitivity Analysis ◽  
Crop Growth ◽  
Growth Modelling ◽  
Global Sensitivity ◽  
Modelling Uncertainty ◽  
Recent Developments

scholarly journals Global sensitivity analysis, probabilistic calibration, and predictive assessment for the Data Assimilation Linked Ecosystem Carbon model

2014 ◽  
Vol 7 (5) ◽  
pp. 6893-6948
Author(s):  
C. Safta ◽  
D. Ricciuto ◽  
K. Sargsyan ◽  
B. Debusschere ◽  
H. N. Najm ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Steady State ◽  
Data Assimilation ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Forest Site ◽  
Carbon Cycle Model ◽  
Global Sensitivity ◽  
Predictive Skill ◽  
Ecosystem Carbon

Abstract. In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model. A Global Sensitivity Analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for Quantities of Interest obtained with the Data Assimilation Linked Ecosystem Carbon (DALEC) model. We then employ a Bayesian approach to calibrate the parameters of DALEC using net ecosystem exchange observations at the Harvard Forest site. The calibration exercise is guided by GSA and by Fisher information matrix results that quantify the amount of information carried by the experimental data about specific model parameters. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks. These checks show a better performance for the non-steady state model during the growing season compared to the one employing steady state assumptions. Overall, this study leads to a 40% improvement in the predictive skill of DALEC and highlights the importance of considering correlations in the model parameters as informed by the data.

scholarly journals Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

2015 ◽  
Vol 8 (7) ◽  
pp. 1899-1918 ◽  
Author(s):  
C. Safta ◽  
D. M. Ricciuto ◽  
K. Sargsyan ◽  
B. Debusschere ◽  
H. N. Najm ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Global Sensitivity Analysis ◽  
Net Ecosystem Exchange ◽  
Forest Site ◽  
Carbon Cycle Model ◽  
Global Sensitivity ◽  
Predictive Skill ◽  
Ecosystem Carbon ◽  
Posterior Predictive Checks

Abstract. In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.

scholarly journals Risk Assessment Method for Offshore Structure Based on Global Sensitivity Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Zou Tao ◽  
Li Huajun ◽  
Liu Defu
Keyword(s):  
Risk Assessment ◽  
Sensitivity Analysis ◽  
Uncertainty Analysis ◽  
Global Sensitivity Analysis ◽  
Assessment Method ◽  
Design Stage ◽  
Offshore Structure ◽  
Global Sensitivity ◽  
Risk Assessment Method ◽  
Global Uncertainty

Based on global sensitivity analysis (GSA), this paper proposes a new risk assessment method for an offshore structure design. This method quantifies all the significances among random variables and their parameters at first. And by comparing the degree of importance, all minor factors would be negligible. Then, the global uncertainty analysis work would be simplified. Global uncertainty analysis (GUA) is an effective way to study the complexity and randomness of natural events. Since field measured data and statistical results often have inevitable errors and uncertainties which lead to inaccurate prediction and analysis, the risk in the design stage of offshore structures caused by uncertainties in environmental loads, sea level, and marine corrosion must be taken into account. In this paper, the multivariate compound extreme value distribution model (MCEVD) is applied to predict the extreme sea state of wave, current, and wind. The maximum structural stress and deformation of a Jacket platform are analyzed and compared with different design standards. The calculation result sufficiently demonstrates the new risk assessment method’s rationality and security.

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