Model structure uncertainty of SOC dynamics studied in a single modeling framework

2020 ◽  
Author(s):  
Nadezda Vasilyeva ◽  
Artem Vladimirov ◽  
Taras Vasiliev
Keyword(s):  
Reaction Rates ◽  
Model Structure ◽  
Modeling Framework ◽  
Calculated Density ◽  
Density Fractions ◽  
C Pools ◽  
C Cycle ◽  
Study Results ◽  
Microbial Turnover ◽  
Bare Fallow

<p>The aim of our study is the source of uncertainty in soil organic carbon (SOC) models which comes from the model structure. For that we have developed a family of mathematical models for SOC dynamics with switchable biological and physical mechanisms. Studies mechanisms include microbial activity with constant or dynamic carbon use efficiency (CUE) and constant or dynamic microbial turnover rate; priming effect: decay of stable SOC pool in the presence of labile SOC pool; temperature and moisture dependencies of SOC decomposition rates; dynamic adsorption strength and occlusion. Model SOC cycle includes measurable C pools in soil size and density fractions, each comprised of two estimated theoretical C pools (labile and stable - biochemical C cycle). Reaction rates of the biochemical cycle are modified according to its physical state: decay accelerates with size, accelerates with the amount of adsorbed C (density: heavy to light) and decelerates with soil microaggregation (occluded state). The models family was tested on C and 13C dynamics detailed data of a long-term bare fallow chronosequence.</p><p>Analysis of SOC models family with different combinations of mechanisms showed that the best (estimated by BIC) description of SOC dynamics in physical fractions was with microbially-explicit models only in case of a feedback via dynamics of microbial turnover and CUE. First, we estimated uncertainty of all mechanism-specific parameters for every model in the family. We calculated density distributions for parameters characterizing functional properties and stability of soil components (such as energy of activation, adsorption capacity, CUE, 13C distillation coefficient) for the models family weighted with models likelihoods. These parameter values were then compared with common experimental values.</p><p>We discuss the use of the study results to estimate relevance of observed parameter and structural uncertainties for global SOC projections obtained using different model structures.</p>

DIF Detection With Zero-Inflation Under the Factor Mixture Modeling Framework

2021 ◽  
pp. 001316442110289
Author(s):  
Sooyong Lee ◽  
Suhwa Han ◽  
Seung W. Choi
Keyword(s):  
Total Sample ◽  
Mixture Modeling ◽  
Zero Inflation ◽  
Modeling Framework ◽  
Detection Power ◽  
Suggested Procedure ◽  
Response Data ◽  
Number Of Zeros ◽  
Factor Mixture Modeling ◽  
Study Results

Response data containing an excessive number of zeros are referred to as zero-inflated data. When differential item functioning (DIF) detection is of interest, zero-inflation can attenuate DIF effects in the total sample and lead to underdetection of DIF items. The current study presents a DIF detection procedure for response data with excess zeros due to the existence of unobserved heterogeneous subgroups. The suggested procedure utilizes the factor mixture modeling (FMM) with MIMIC (multiple-indicator multiple-cause) to address the compromised DIF detection power via the estimation of latent classes. A Monte Carlo simulation was conducted to evaluate the suggested procedure in comparison to the well-known likelihood ratio (LR) DIF test. Our simulation study results indicated the superiority of FMM over the LR DIF test in terms of detection power and illustrated the importance of accounting for latent heterogeneity in zero-inflated data. The empirical data analysis results further supported the use of FMM by flagging additional DIF items over and above the LR test.

Temperature sensitivity of simulated soils with biochars produced at different temperatures

Soil Research ◽  
2019 ◽  
Vol 57 (3) ◽  
pp. 294 ◽  
Author(s):  
Xiaojie Wang ◽  
Guanhong Chen ◽  
Renduo Zhang
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Temperature Sensitivity ◽  
Pyrolysis Temperature ◽  
Soil C ◽  
Ambient Temperatures ◽  
C Pools ◽  
C Cycle ◽  
Microbial Enzyme ◽  
Different Temperatures

The temperature sensitivity of multiple carbon (C) pools in the soil plays an important role in the C cycle and potential feedback to climate change. The aim of this study was to investigate the temperature sensitivity of different biochars in soil to better understand the temperature sensitivity of different soil C pools. Biochars were prepared using sugarcane residue at temperatures of 300, 500 and 800°C (representing different C pools) and C skeletons (representing the refractory C pool in biochar) were obtained from each biochar. The sugarcane residue, biochars and C skeletons were used as amendments in a simulated soil with microbes but without organic matter. The temperature sensitivity of the amended soils was characterised by their mineralisation rate changes in response to ambient temperatures. The temperature sensitivity of treatments with relatively refractory biochars was higher than that with labile biochars. The temperature sensitivity of treatments with biochars was lower than for their corresponding C skeletons. The different temperature sensitivity of treatments was attributable to the different internal C structures (i.e. the functional groups of C=C and aromatic structure) of amendments, determining the biodegradability of substrates. Dissolved organic matter and microbial enzyme activity of biochars were lower than those of corresponding C skeletons, and decreased with increasing pyrolysis temperature. The temperature sensitivities of treatments with biochars, C skeletons and sugarcane residue were negatively correlated with the properties of dissolved organic matter and microbial enzyme activities (especially dehydrogenase) in soil.

scholarly journals Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition

2015 ◽  
Vol 12 (9) ◽  
pp. 2655-2694 ◽  
Author(s):  
E. S. Weng ◽  
S. Malyshev ◽  
J. W. Lichstein ◽  
C. E. Farrior ◽  
R. Dybzinski ◽  
...  
Keyword(s):  
Atmospheric Co2 ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
Modeling Framework ◽  
Light Water ◽  
System Models ◽  
C Cycle ◽  
Competition For Light ◽  
Individual Trees ◽  
Earth System Models

Abstract. The long-term and large-scale dynamics of ecosystems are in large part determined by the performances of individual plants in competition with one another for light, water, and nutrients. Woody biomass, a pool of carbon (C) larger than 50% of atmospheric CO2, exists because of height-structured competition for light. However, most of the current Earth system models that predict climate change and C cycle feedbacks lack both a mechanistic formulation for height-structured competition for light and an explicit scaling from individual plants to the globe. In this study, we incorporate height-structured competition for light, competition for water, and explicit scaling from individuals to ecosystems into the land model version 3 (LM3) currently used in the Earth system models developed by the Geophysical Fluid Dynamics Laboratory (GFDL). The height-structured formulation is based on the perfect plasticity approximation (PPA), which has been shown to accurately scale from individual-level plant competition for light, water, and nutrients to the dynamics of whole communities. Because of the tractability of the PPA, the coupled LM3-PPA model is able to include a large number of phenomena across a range of spatial and temporal scales and still retain computational tractability, as well as close linkages to mathematically tractable forms of the model. We test a range of predictions against data from temperate broadleaved forests in the northern USA. The results show the model predictions agree with diurnal and annual C fluxes, growth rates of individual trees in the canopy and understory, tree size distributions, and species-level population dynamics during succession. We also show how the competitively optimal allocation strategy – the strategy that can competitively exclude all others – shifts as a function of the atmospheric CO2 concentration. This strategy is referred to as an evolutionarily stable strategy (ESS) in the ecological literature and is typically not the same as a productivity- or growth-maximizing strategy. Model simulations predict that C sinks caused by CO2 fertilization in forests limited by light and water will be down-regulated if allocation tracks changes in the competitive optimum. The implementation of the model in this paper is for temperate broadleaved forest trees, but the formulation of the model is general. It can be expanded to include other growth forms and physiologies simply by altering parameter values.

Carbon dynamics in the Brazilian Cerrado: stocks and fluxes estimated by a model data fusion framework (CARDAMOM)

2020 ◽  
Author(s):  
Eráclito Sousa-Neto ◽  
Luke Smallman ◽  
Jean Ometto ◽  
Mathew Williams
Keyword(s):  
Land Surface ◽  
Balance Model ◽  
Brazilian Cerrado ◽  
Model Framework ◽  
Area Index ◽  
Soil C ◽  
C Pools ◽  
C Cycling ◽  
C Cycle ◽  
Different Types

<p>Savannas are a major component of the world’s vegetation and cover a land surface of about 15 Mkm<sup>2</sup>, accounting for about 30% of the terrestrial primary production. In the South America, the Brazilian Savanna (Cerrado) is the second largest biome (2 Mkm<sup>2</sup>), after the Amazon biome, and a hotspot of biodiversity. The Cerrado region is heterogeneous, with savanna vegetation ranging from open grassland, through a gradient of increasing tree density to nearly closed-canopy woodland. The cerrado vegetation is markedly seasonal in phenology and is often burned, either naturally or as part of a management cycle. Due its large occupation, Cerrado have the potential to influence the regional and possibly the global energy, water and carbon (C) balances. The allocation of the net primary productivity (NPP) of an ecosystem between canopy, woody tissue and fine roots is an important descriptor of the functioning of an ecosystem, and an important feature to correctly represent in terrestrial ecosystem models for carbon rates estimation, as well as their residence time, variation with climate and disturbance, and in order to make better forecasts. Such estimation in Cerrado regions remains still difficult given the lack of important soil and vegetation data. Previous studies have showed that the fluxes of water and C are closely related to each other, and to the diurnal cycle of solar radiation. However, there is no study clearly assessing the allocation of C through the different types of vegetation, either in the different types of physiognomies. To help estimating the C flows across the different C pools and types of vegetation, we are using Carbon Data Model Framework (CARDAMOM) which is a computer programme that retrieves terrestrial carbon (C) cycle variables by combining C cycle observations with a mass balance model. CARDAMOM produces global dynamic estimates of plant and soil C pools, their exchanges with each other and with the atmosphere, and C cycling variables for processes driving change. It also produces a C cycle analysis consistent with C measurements and climate, and it is suited for using with global-scale satellite observations such as aboveground biomass (ABG) or leaf area index (LAI). For that, we count on field data available (AGB, BGB) and satellite data (LAI, AGB, soil C), which will help to present robust analyses of C cycling across gradients of biomass in the Brazilian Cerrado.</p>

scholarly journals Lignin reinforcement of urea-crosslinked starch films for reduction of starch biodegradability to improve slow nitrogen release properties under natural aerobic soil condition

e-Polymers ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Zahid Majeed ◽  
Nurlidia Mansor ◽  
Zakaria Man ◽  
Samsuri Abd Wahid
Keyword(s):  
Diffusion Mechanism ◽  
Composite Films ◽  
Soil Condition ◽  
Reaction Rates ◽  
Order Reaction ◽  
Fickian Diffusion ◽  
Major Drawback ◽  
Study Results ◽  
Zero Order Reaction ◽  
Aerobic Soil

AbstractThe urea-crosslinked starch (UcS) film has a major drawback of very rapid biodegradability when applied as slow release fertilizer in soil. Lignin reinforcement of the UcS was used to prepare composite films, aimed to reduce the starch biodegradability and slow the release of nitrogen in aerobic soil condition. Study results revealed that mineralization of the composite films was delayed from 6.40 to 13.58% more than UcS film. Inhibition of composite films mixing with soil, the Michaelis-Menten reaction rates for α-amylase were inhibited ~1.72–2.03 times whereas the Michaelis-Menten reaction rates for manganese peroxidase were increased ~1.07–1.41 times compared to UcS film. Saccharides–glucose, maltose and maltotriose demonstrated that their rates of formation (zero-order reaction) and depletion (first-order reaction); both were slowed more in aerobic soil which received the composite films. Increasing of lignin in composite films, the acid to aldehyde ratios of vanillyl and syringyl phenols of the lignin declined from 1.18 to 1.17 (~0.76%) and 1.59–1.56 (~1.78%), respectively. The diffusivity of nitrogen was effectively slowed 0.66–0.94 times by the lignin in composite films and showed a “Fickian diffusion” mechanism (release exponent n=0.095–0.143).

scholarly journals Aggregation and Controlled Interaction: Automated Mechanisms for Managing Design Complexity

1998 ◽  
Author(s):  
Timothy M. Jacobs ◽  
Elaine Cohen
Keyword(s):  
Product Management ◽  
Model Structure ◽  
Modeling Framework ◽  
Design Models ◽  
Integrate Model ◽  
Design Complexity ◽  
Large Numbers ◽  
Management Controls ◽  
Representational Power ◽  
Synergistic Relationships

Abstract Complexity in modern product design is manifest through large numbers of diverse parts, functions, and design disciplines that require an intricate web of synergistic relationships to link them together. It is extremely difficult for designers to assimilate or represent such complex designs in their totality. In this research, we present a framework that utilizes the intricate relationships between design components to enhance the representational power of design models and to provide focal points for automating the management of design complexity. We introduce automated mechanisms, based on aggregation and interaction relationships between design components, that integrate model structure, a variety of conceptual and detailed design information, and product management controls into a single modeling framework. These mechanisms are easily incorporated into design models and they facilitate re-use and cooperative design by ensuring that related entities can be modified independently.

scholarly journals Studying Human Design Teams through Computational Teams of Simulated Annealing Agents

2018 ◽  
Author(s):  
Christopher McComb ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Empirical Studies ◽  
Individual Characteristics ◽  
Personnel Cost ◽  
Design Teams ◽  
Modeling Framework ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Study Results ◽  
Cognitive Study

Novel design methodologies are often evaluated through empirical studies involving human designers. However, such empirical studies can incur a high personnel cost. Further, it can be difficult to isolate the effects of specific team or individual characteristics. These limitations could be bypassed by employing a computational model of design teams. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, an agent-based platform that provides a means for efficiently simulating human design teams. A number of empirically demonstrated cognitive phenomena are modeled within the platform, striking a balance between model simplicity and direct applicability to engineering design problems. This paper discusses the composition of the CISAT modeling framework and demonstrates how it can be used to simulate the performance of human design teams in a cognitive study. Results simulated with CISAT are compared directly to the results derived from human designers. Finally, the CISAT model is also used to investigate the characteristics that were most and least helpful to teams during the cognitive study.

scholarly journals Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system

2021 ◽  
Vol 25 (10) ◽  
pp. 5603-5621
Author(s):  
Andrew J. Newman ◽  
Amanda G. Stone ◽  
Manabendra Saharia ◽  
Kathleen D. Holman ◽  
Nans Addor ◽  
...  
Keyword(s):  
Hydrologic Modeling ◽  
Initial Conditions ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Model Parameters ◽  
Model Structure ◽  
Structure Model ◽  
Return Periods ◽  
Modeling Framework ◽  
Flood Generation

Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

scholarly journals A Novel Model for Simulating COVID-19 Dynamics Through Layered Infection States that Integrate Concepts from Epidemiology, Biophysics and Medicine: SEI3R2S-Nrec

2020 ◽  
Author(s):  
Jack M Winters
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Disease Process ◽  
Data Access ◽  
Population Diversity ◽  
Optimal Feedback Control ◽  
Model Structure ◽  
Modeling Framework ◽  
Physiological Models ◽  
The Usa

Introduction. Effectively modeling SARS-CoV-2/COVID-19 dynamics requires careful integration of population health (public health motivation) and recovery dynamics (medical interventions motivation). This manuscript proposes a minimal pandemic model, which conceptually separates "complex adaptive systems" (CAS) associated with social behavior and infrastructure (e.g., tractable input events modulating exposure) from idealized bio-CAS (e.g., the immune system). The proposed model structure extends the classic simple SEIR (susceptible, exposed, infected, resistant/recovered) uni-causal compartmental model, widely used in epidemiology, into an 8th-order functional network SEI3R2S-Nrec model structure, with infection partitioned into three severity states (e.g., starts in I1 [mostly asymptomatic], then I2 if notable symptoms, then I3 if ideally hospitalized) that connect via a lattice of fluxes to two "resistant" (R) states. Here Nrec ("not recovered") represents a placeholder for better tying emerging COVID-19 medical research findings with those from epidemiology. Methods. Borrowing from fuzzy logic, a given model represents a "Universe of Discourse" (UoD) that is based on assumptions. Nonlinear flux rates are implemented using the classic Hill function, widely used in the biochemical and pharmaceutical fields and intuitive for inclusion within differential equations. There is support for "encounter" input events that modulate ongoing E (exposures) fluxes via S↔I1 and other I1/2/3 encounters, partitioned into a "social/group" (uSG(t)) behavioral subgroup (e.g., ideally informed by evolving science best-practices), and a smaller uTB(t) subgroup with added "spreader" lifestyle and event support. In addition to signal and flux trajectories (e.g., plotted over 300 days), key cumulative output metrics include fluxes such as I3→D deaths, I2→I3 hospital admittances, I1→I2 related to "cases" and R1+R2 resistant. The code, currently available as a well-commented Matlab Live Script file, uses a common modeling framework developed for a portfolio of other physiological models that tie to a planned textbook; an interactive web-based version will follow. Results. Default population results are provided for the USA as a whole, three states in which this author has lived (Arizona, Wisconsin, Oregon), and several special hypothetical cases of idealized UoDs (e.g., nursing home; healthy lower-risk mostly on I1→R1 path to evaluate reinfection possibilities). Often known events were included (e.g., pulses for holiday weekends; Trump/governor-inspired summer outbreak in Arizona). Runs were mildly tuned by the author, in two stages: i) mild model-tuning (e.g., for risk demographics such as obesity), then ii) iterative input tuning to obtain similar overall March-thru-November curve shapes and appropriate cumulative numbers (recognizing limitations of data like "cases"). Predictions are consistent deaths, and CDC estimates of actual cases and immunity (e.g., antibodies). Results could be further refined by groups with more resources (human, data access, computational). It is hoped that its structure and causal predictions might prove helpful to policymakers, medical professionals, and "on the ground" managers of science-based interventions. Discussion and Future Directions. These include: i) sensitivity of the model to parameters; ii) possible next steps for this SEI3R2S-Nrec framework such as dynamic sub-models to better address compartment-specific forms of population diversity (e.g., for E [host-parasite biophysics], I's [infection diversity], and/or R's [immune diversity]); iii) model's potential utility as a framework for applying optimal/feedback control engineering to help manage the ongoing pandemic response in the context of competing subcriteria and emerging new tools (e.g., more timely testing, vaccines); and iv) ways in which the Nrec medical submodel could be expanded to provide refined estimates of the types of tissue damage, impairments and dysfunction that are known byproducts of the COVID-19 disease process, including as a function of existing comorbidities.

Experimental Performance Evaluation of a Catalytic Flat Plate Fuel Reformer for Fuel Cell Grade Reformate

2014 ◽  
Author(s):  
Susanta K. Das ◽  
K. Joel Berry
Keyword(s):  
Fuel Cell ◽  
Flat Plate ◽  
Reaction Rates ◽  
Design Parameters ◽  
Channel Height ◽  
Inlet Temperature ◽  
Cfd Model ◽  
Current Voltage ◽  
Study Results ◽  
Current Voltage Characteristics

Compact and efficient fuel reforming system design is a major challenge because of strict requirements of efficient heat distribution on both the reforming and combustion side. As an alternative to traditional packed bed tubular reformers, catalytic flat plate fuel reformer offers better heat integration by combining the combustion reaction on one side and reforming reaction on the other side. In this study, with the help of a two-dimensional computational fluid dynamics (CFD) model, a catalytic flat plate fuel reformer is built and investigated its performance experimentally. The CFD model simulation results help to capture the effect of design parameters such as catalyst layer thickness, reaction rates, inlet temperature and velocity, and channel height. The CFD model study results also help to design and built the actual reformer in such a way that eliminate the limitations or uncertainties of heat and mass transfer coefficients. In our study, we experimentally evaluated the catalytic flat plate fuel reformer performance using natural gas. The effect of reformate gas on the current-voltage characteristics of a 5kW high temperature PEM fuel cell (HTPEMFC) stack is investigated extensively. The results shows that the overall system performance increases in terms of current-voltage characteristics of HTPEMFC while fed with reformate directly from the catalytic flat plate reformer.

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