Global Sensitivity Analysis for CERES-Rice Model under Different Cultivars and Specific-Stage Variations of Climate Parameters

Global sensitivity analysis (SA) has become an efficient way to identify the most influential parameters on model results. However, the effects of cultivar variation and specific-stage variations of climate conditions on model outputs still remain unclear. In this study, 30 indica hybrid rice cultivars were simulated in the CERES-Rice model; then the Sobol’ method was used to perform a global SA on 16 investigated parameters for three model outputs (anthesis day, maturity day, and yield). In addition, we also compared the differences in the sensitivity results under four specific-stage variations (vegetative phase, panicle-formation phase, ripening phase, and the whole growth season) of climate conditions. The results indicated that (1) parameter Tavg, G4, and P2O are the most influential parameters for all model outputs across cultivars during the whole growth season; (2) under the vegetative-phase variation of climate parameters; the variability of model outputs is mainly controlled by parameter P2O and Tavg; (3) under the panicle-formation-phase or ripening-phase variation of climate parameters, parameter P2O was the dominant variable for all model outputs; (4) parameter PORM had a considerable effect (the total sensitivity index, STi; STi>0.05) on yield regardless of the various specific-stage variations of the climate parameters. Findings obtained from this study will contribute to understanding the comprehensive effects of crop parameters on model outputs under different cultivars and specific-stage variations of climate conditions.

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Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

Journal of Elasticity ◽

10.1007/s10659-021-09833-9 ◽

2021 ◽

Author(s):

Sebastian Brandstaeter ◽

Sebastian L. Fuchs ◽

Jonas Biehler ◽

Roland C. Aydin ◽

Wolfgang A. Wall ◽

...

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Model Parameters ◽

Computational Studies ◽

Growth And Remodeling ◽

Global Sensitivity ◽

Output Variability ◽

Constrained Mixture ◽

The One ◽

Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

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Influential parameters for estimating the environmental impacts of geothermal power: A global sensitivity analysis study

Cleaner Environmental Systems ◽

10.1016/j.cesys.2021.100054 ◽

2021 ◽

Vol 3 ◽

pp. 100054

Author(s):

Andrea Paulillo ◽

Aleksandra Kim ◽

Christopher Mutel ◽

Alberto Striolo ◽

Christian Bauer ◽

...

Keyword(s):

Sensitivity Analysis ◽

Environmental Impacts ◽

Global Sensitivity Analysis ◽

Analysis Study ◽

Global Sensitivity ◽

Geothermal Power ◽

Influential Parameters

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A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

Agronomy ◽

10.3390/agronomy10040607 ◽

2020 ◽

Vol 10 (4) ◽

pp. 607

Author(s):

Deepak Upreti ◽

Stefano Pignatti ◽

Simone Pascucci ◽

Massimo Tolomio ◽

Zhenhai Li ◽

...

Keyword(s):

Sensitivity Analysis ◽

Distribution Function ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Kolmogorov Smirnov ◽

Anderson Darling ◽

Parameter Approach ◽

Influential Parameters ◽

Smirnov Statistic ◽

Stress Parameters

The present work reports the global sensitivity analysis of the Aquacrop Open Source (AOS) model, which is the open-source version of the original Aquacrop model developed by the Food and Agriculture Organization (FAO). Analysis for identifying the most influential parameters was based on different strategies of global SA, density-based and variance-based, for the wheat crop in two different geographical locations and climates. The main objectives were to distinguish the model’s influential and non-influential parameters and to examine the yield output sensitivity. We compared two different methods of global sensitivity analysis: the most commonly used variance-based method, EFAST, and the moment independent density-based PAWN method developed in recent years. We have also identified non-influential parameters using Morris screening method, so to provide an idea of the use of non-influential parameters with a dummy parameter approach. For both the study areas (located in Italy and in China) and climates, a similar set of influential parameters was found, although with varying sensitivity. When compared with different probability distribution functions, the probability distribution function of yield was found to be best approximated by a Generalized Extreme Values distribution with Kolmogorov–Smirnov statistic of 0.030 and lowest Anderson–Darling statistic of 0.164, as compared to normal distribution function with Kolmogorov–Smirnov statistic of 0.122 and Anderson–Darling statistic of 4.099. This indicates that yield output is not normally distributed but has a rather skewed distribution function. In this case, a variance-based approach was not the best choice, and the density-based method performed better. The dummy parameter approach avoids to use a threshold as it is a subjective question; it advances the approach to setting up a threshold and gives an optimal way to set up a threshold and use it to distinguish between influential and non-influential parameters. The highly sensitive parameters to crop yield were specifically canopy and phenological development parameters, parameters that govern biomass/yield production and temperature stress parameters rather than root development and water stress parameters.

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A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand

Agronomy ◽

10.3390/agronomy10070984 ◽

2020 ◽

Vol 10 (7) ◽

pp. 984

Author(s):

W. B. M. A. C. Bandara ◽

Kazuhito Sakai ◽

Tamotsu Nakandakari ◽

Preecha Kapetch ◽

R. H. K. Rathnappriya

Keyword(s):

Sensitivity Analysis ◽

Spatial Variability ◽

Gaussian Process ◽

Global Sensitivity Analysis ◽

Management Strategies ◽

Global Sensitivity ◽

Temporal And Spatial Variability ◽

Sugarcane Yield ◽

Temporal And Spatial ◽

Influential Parameters

Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.

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Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models

The AAPS Journal ◽

10.1208/s12248-020-00480-x ◽

2020 ◽

Vol 22 (5) ◽

Author(s):

Dan Liu ◽

Linzhong Li ◽

Amin Rostami-Hodjegan ◽

Frederic Y. Bois ◽

Masoud Jamei

Keyword(s):

Sensitivity Analysis ◽

Plasma Concentration ◽

Global Sensitivity Analysis ◽

Model Parameters ◽

Model Parameter ◽

Global Sensitivity ◽

Physiologically Based ◽

The Impact ◽

Influential Parameters

Abstract Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (Cmax) time at which Cmax is reached (Tmax), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (Vss) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (Vliver) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.

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Global sensitivity analysis of influential parameters for excavation stability of metro tunnel

Automation in Construction ◽

10.1016/j.autcon.2020.103080 ◽

2020 ◽

Vol 113 ◽

pp. 103080 ◽

Cited By ~ 1

Author(s):

Wenli Liu ◽

Lieyun Ding

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Excavation Stability ◽

Metro Tunnel ◽

Influential Parameters

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Global Sensitivity Analysis of (a)Symmetric Energy Harvesters

10.21203/rs.3.rs-704560/v1 ◽

2021 ◽

Author(s):

João Pedro Norenberg ◽

Americo Cunha ◽

Samuel da Silva ◽

Paulo Varoto

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Critical Parameters ◽

Mean Power ◽

Small Perturbations ◽

Global Sensitivity ◽

Energy Harvesters ◽

Stable Conditions ◽

Piezoelectric Coupling ◽

Influential Parameters

Abstract Parametric variability is inevitable in actual energy harvesters and can define crucial aspects of the system performance, especially in susceptible systems to small perturbations. In this way, this work aims to identify the most critical parameters in the dynamics of (a)symmetric bistable energy harvesters with nonlinear piezoelectric coupling, considering the variability of their physical and excitation parameters. For this purpose, a global sensitivity analysis based on the Sobol' indices is performed by an orthogonal decomposition in terms of conditional variances to access the dependence of the recovered power concerning the harvester parameters. This technique quantifies the variance concerning each parameter individually and jointly regarding the total variation of the model. The results indicate that the frequency and amplitude of excitation, asymmetric bias angle, and piezoelectric coupling at the electrical domain are the most influential parameters that affect the mean power harvested. It has also been shown that the order of importance of the parameters can change from stable conditions. In possession of this, a better understanding of the system under analysis is obtained, identifying vital parameters that rule the change of dynamic behavior and constituting a powerful tool in the robust design and prediction of nonlinear harvesters.

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