Parameter sensitivity study of the biogeochemical model in the China coastal seas

Ji, Xuanliang; Liu, Guimei; Gao, Shan; Wang, Hui

doi:10.1007/s13131-015-0762-0

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The lattice type of 𝛼-Zr is in the form of HCP and the space group of its primary cell is P6 3 /mmc (No.194). The Zr(0 0 0 1) plane, which is also the most stable surface, [29][30][31] is considered as the dominant Zr surface orientation. The first-principles DFT calculations were conducted with the Cambridge Sequential Total Energy Package (CASTEP) module to optimize the 𝛼-Zr primary cell.…”

Section: Computational Methods and Modelsmentioning

confidence: 99%

Atomic‐Scale Investigations of H₃BO₃ and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Ren

Suo

et al. 2023

Advcd Theory and Sims

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The boric acid (H 3 BO 3 ) and lithium hydroxide (LiOH) are routinely added to primary water as soluble neutron absorbers and pH regulating agents in the pressured water reactor nuclear power plant, respectively. Previous studies mostly focus on the service behavior of nuclear materials using experimental methods, whereas the microscopic mechanism of H 3 BO 3 and LiOH on Zr-based fuel cladding remains largely unclear. With the first principle approach of density functional theory (DFT), the geometric structure, Mulliken population, partial densities of states, and electron density difference are adopted to study the adsorption characteristics and binding strength of H 3 BO 3 and LiOH on Zr matrix. The results show that when H 3 BO 3 and LiOH react on the Zr matrix, there is formation of hybrid peaks and new bonds between surface molecules and the matrix. Meanwhile, the vertical molecule has a substantial impact on electron movement between adsorbs and the matrix. The interaction energy between two molecules and the Zr surface in turn is LiOH > H 3 BO 3 , demonstrating that LiOH has the most influence on the fuel cladding in the primary circuit.

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Section: Computational Methods and Modelsmentioning

confidence: 99%

Atomic‐Scale Investigations of H₃BO₃ and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Ren

Suo

et al. 2023

Advcd Theory and Sims

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“…Firstly, we used a sensitivity formulation to perform a screening sensitivity analysis. The following sensitivity function is employed to measure the local sensitivity of the biological parameters to model outputs [45,48]:…”

Section: Sensitivity Analysismentioning

confidence: 99%

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

et al. 2022

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Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.

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“…The most common approach in BGC modeling is to conduct a local analysis with a few experiments by varying the parameter values OAT or simultaneously (e.g., Baklouti et al, 2006;Kriest et al, 2012;Kvale & Meissner, 2017). Some studies assessed the adjoint sensitivity (e.g., Fennel et al, 2001;Tjiputra & Winguth, 2008;Ji et al, 2015) of model output to its inputs by computing gradients. However, it is important to understand that gradient-based SA is a local method because the gradient is a local notion computed in the vicinity of the parameter's current value.…”

Section: Introductionmentioning

confidence: 99%

Global sensitivity analysis of a one-dimensional ocean biogeochemical model

Mamnun,

Völker,

Krumscheid

et al. 2023

SESMO

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Ocean biogeochemical (BGC) models are a powerful tool for investigating ocean biogeochemistry and the global carbon cycle. The potential benefits emanating from BGC simulations and predictions are broad, with significant societal impacts from fisheries management to carbon dioxide removal and policy-making. These models contain numerous parameters, each coupled with large uncertainties, leading to significant uncertainty in the model outputs. This study performs a global sensitivity analysis (GSA) of an ocean BGC model to identify the uncertain parameters that impact the variability of model outputs most. The BGC model Regulated Ecosystem Model 2 is used in a one-dimensional configuration at two ocean sites in the North Atlantic (BATS) and the Mediterranean Sea (DYFAMED). Variance-based Sobol' indices are computed to identify the most influential parameters for each site for the quantities of interest (QoIs) commonly considered for the calibration and validation of BGC models. The most sensitive parameters are the chlorophyll to nitrogen ratio, chlorophyll degradation rate, zooplankton grazing and excretion parameters, photosynthesis parameters, and nitrogen and carbon remineralization rate. Overall, the sensitivities of most QoIs were similar across the two sites; however, some differences emerged because of different mixed layer depths. The results suggest that implementing multiple zooplankton function types in BGC models can improve BGC predictions. Further, explicitly implementing heterotrophic bacteria in the model can better simulate the carbon export production and CO2 fluxes. The study offers a comprehensive list of the most important BGC parameters that need to be quantified for future modeling applications and insights for BGC model developments.

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Parameter sensitivity study of the biogeochemical model in the China coastal seas

Cited by 6 publications

References 30 publications

Atomic‐Scale Investigations of H₃BO₃ and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Atomic‐Scale Investigations of H₃BO₃ and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Global sensitivity analysis of a one-dimensional ocean biogeochemical model

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Parameter sensitivity study of the biogeochemical model in the China coastal seas

Cited by 6 publications

References 30 publications

Atomic‐Scale Investigations of H3BO3 and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Atomic‐Scale Investigations of H3BO3 and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Global sensitivity analysis of a one-dimensional ocean biogeochemical model

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Product

Resources

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Atomic‐Scale Investigations of H₃BO₃ and LiOH on Zr(0 0 0 1) Surface: A DFT Study

Atomic‐Scale Investigations of H₃BO₃ and LiOH on Zr(0 0 0 1) Surface: A DFT Study